def load_model(self):
self.model = GPT2SequenceClassifierModel(
hidden_size=self.embed_dim,
num_classes=len(self.classes),
gpt_model_name=self.model_name,
max_seq_length=self.max_seq_length,
finetune_GPT2=self.finetune_GPT2)
self.model.to(device)
self.opt = self.optimizer(self.model.parameters())
if (self.checkpoint_path):
# grab model and optimizer from checkpoint
model_chk, opt_chk, self.current_ephoc, amp_chk = self.load_checkpoint(
)
print("continuing training from checkpoint at ephoc: ",
self.current_ephoc)
self.model.load_state_dict(model_chk)
self.opt.load_state_dict(opt_chk)
if (amp_chk):
amp.load_state_dict(amp_chk)
else:
self.current_ephoc = 0
if self.fp16:
from apex import amp
# inspired by: https://github.com/huggingface/transformers/blob/master/examples/question-answering/run_squad.py
# Before we do anything with models, we want to ensure that we get fp16 execution of torch.einsum if fp16 is set.
# Otherwise it'll default to "promote" mode, and we'll get fp32 operations. Note that running `--fp16_opt_level="O2"` will
# remove the need for this code, but it is still valid.
amp.register_half_function(torch, "einsum")
print("Converting models and optimizer to FP16")
self.model, self.opt = amp.initialize(self.model,
self.opt,
opt_level="O1")
def main():
parser = get_parser()
args = parser.parse_args()
if (os.path.exists(args.output_dir) and os.listdir(args.output_dir)
and not args.overwrite_output_dir):
raise ValueError(
"Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome."
.format(args.output_dir))
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
# Set device
args.device = torch.device(
"cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO)
logging.getLogger("transformers.generation_utils").setLevel(logging.ERROR)
# Load pretrained question generation model and tokenizer
GPT2_tokenizer = GPT2Tokenizer.from_pretrained(
args.question_generation_model, do_lower_case=args.do_lower_case)
GPT2_model = GPT2LMHeadModel.from_pretrained(
args.question_generation_model)
GPT2_model.prepare_inputs_for_generation = prepare_inputs_for_generation
GPT2_model.eval()
GPT2_model.to(args.device)
BERT_tokenizer = BertTokenizer.from_pretrained(
args.answering_model, do_lower_case=args.do_lower_case)
BERT_model = BertForQuestionAnswering.from_pretrained(args.answering_model)
BERT_model.eval()
BERT_model.to(args.device)
logging.info("Parameters %s", args)
# Before we do anything with models, we want to ensure that we get fp16 execution of torch.einsum if args.fp16 is set.
# Otherwise it'll default to "promote" mode, and we'll get fp32 operations. Note that running `--fp16_opt_level="O2"` will
# remove the need for this code, but it is still valid.
if args.fp16:
try:
from apex import amp
amp.register_half_function(torch, "einsum")
GPT2_model = amp.initialize(GPT2_model,
opt_level=args.fp16_opt_level)
BERT_model = amp.initialize(BERT_model,
opt_level=args.fp16_opt_level)
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use fp16 training."
)
generate(args, GPT2_tokenizer, GPT2_model, BERT_tokenizer, BERT_model)
def __init__(self,
args,
device):
print('initializing Reader...', flush=True)
# self.model = IterBertForQuestionAnsweringConfidence.from_pretrained(args.reader_path, num_labels=4, no_masking=True)
# self.model = IterBertForQuestionAnsweringConfidenceV2.from_pretrained(args.reader_path, num_labels=4, no_masking=True)
# self.model = IterBertForQuestionAnsweringConfidenceV3.from_pretrained(args.reader_path, num_labels=4, no_masking=True)
# self.model = IterBertForQuestionAnsweringConfidenceV4.from_pretrained(args.reader_path, num_labels=4, no_masking=True)
# self.model = RobertaForQuestionAnsweringConfidence.from_pretrained(args.reader_path, num_labels=4, no_masking=True)
if args.reader_version == 'bert':
self.model = BertForQuestionAnsweringConfidence.from_pretrained(args.reader_path, num_labels=4, no_masking=True)
elif args.reader_version == 'iter':
self.model = IterBertForQuestionAnsweringConfidence.from_pretrained(args.reader_path, num_labels=4, no_masking=True)
elif args.reader_version == 'iter_v2':
self.model = IterBertForQuestionAnsweringConfidenceV2.from_pretrained(args.reader_path, num_labels=4, no_masking=True)
elif args.reader_version == 'iter_v3':
self.model = IterBertForQuestionAnsweringConfidenceV3.from_pretrained(args.reader_path, num_labels=4, no_masking=True)
elif args.reader_version == 'iter_v4':
self.model = IterBertForQuestionAnsweringConfidenceV4.from_pretrained(args.reader_path, num_labels=4, no_masking=True)
elif args.reader_version == 'roberta':
self.model = RobertaForQuestionAnsweringConfidence.from_pretrained(args.reader_path, num_labels=4, no_masking=True)
elif args.reader_version == 'roberta_iter':
self.model = IterRobertaForQuestionAnsweringConfidence.from_pretrained(args.reader_path, num_labels=4, no_masking=True)
else:
raise RuntimeError()
if args.reader_version == 'bert':
self.tokenizer = BertTokenizer.from_pretrained(args.reader_path, do_lower_case=args.do_lower_case)
else:
self.tokenizer = AutoTokenizer.from_pretrained(args.reader_path)
self.device = device
self.model.to(device)
if args.fp16:
from apex import amp
if args.fp16_opt_level == 'O1':
amp.register_half_function(torch, "einsum")
self.model = amp.initialize(self.model, opt_level=args.fp16_opt_level)
self.model.eval()
print('Done!', flush=True)
def get_amp(fp16):
"""This function ensures that fp16 execution of torch.einsum is enabled
if args.fp16 is set. Otherwise, it'll default to "promote" mode,
where the operations are in fp32.
Note that running `--fp16_opt_level="O2"` will remove the need for this code.
"""
# Before we do anything with models, we want to
if fp16:
try:
from apex import amp
amp.register_half_function(torch, "einsum")
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex")
else:
amp = None
return amp
def __init__(self,
model: GWNet,
scaler,
lrate,
wdecay,
clip=5,
lr_decay_rate=.97,
fp16='',
end_conv_lr=None):
self.model = model
if end_conv_lr:
end_conv2, other_params = model.conv_group
groups = [{'params': end_conv2, 'lr': end_conv_lr}]
if lrate > 0:
groups.append({'params': other_params})
self.model.freeze_group_b()
self.optimizer = optim.Adam(groups, lr=lrate, weight_decay=wdecay)
else:
self.optimizer = optim.Adam(self.model.parameters(),
lr=lrate,
weight_decay=wdecay)
self.scaler = scaler
self.clip = clip
self.fp16 = fp16
l1 = lambda epoch: lr_decay_rate**epoch
self.scheduler = optim.lr_scheduler.LambdaLR(self.optimizer,
lr_lambda=l1)
if self.fp16:
try:
from apex import amp # Apex is only required if we use fp16 training
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use fp16 training."
)
amp.register_half_function(torch, 'einsum')
self.model, self.optimizer = amp.initialize(self.model,
self.optimizer,
opt_level=self.fp16)
def __init__(self,
in_feature=128,
out_feature=10575,
s=32.0,
m=0.50,
easy_margin=False):
super(ArcMarginProduct, self).__init__()
self.in_feature = in_feature
self.out_feature = out_feature
self.s = s
self.m = m
self.weight = Parameter(torch.Tensor(out_feature, in_feature))
nn.init.xavier_uniform_(self.weight)
self.easy_margin = easy_margin
self.cos_m = math.cos(m)
self.sin_m = math.sin(m)
# make the function cos(theta+m) monotonic decreasing while theta in [0°,180°]
self.th = math.cos(math.pi - m)
self.mm = math.sin(math.pi - m) * m
if args.use_amp == True:
amp.register_half_function(torch, 'where')
def main():
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--bert_model",
default='bert-base-uncased',
type=str,
help="Bert pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, "
"bert-base-multilingual-cased, bert-base-chinese.")
parser.add_argument(
"--output_dir",
default=None,
type=str,
required=True,
help=
"The output directory where the model predictions and checkpoints will be written."
)
parser.add_argument(
'--task',
type=str,
default=None,
required=True,
help="Task code in {hotpot_open, hotpot_distractor, squad, nq}")
# Other parameters
parser.add_argument(
"--max_seq_length",
default=378,
type=int,
help=
"The maximum total input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, and sequences shorter \n"
"than this will be padded.")
parser.add_argument(
"--do_lower_case",
action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument("--train_batch_size",
default=1,
type=int,
help="Total batch size for training.")
parser.add_argument("--eval_batch_size",
default=5,
type=int,
help="Total batch size for eval.")
parser.add_argument("--learning_rate",
default=5e-5,
type=float,
help="The initial learning rate for Adam. (def: 5e-5)")
parser.add_argument("--num_train_epochs",
default=5.0,
type=float,
help="Total number of training epochs to perform.")
parser.add_argument(
"--warmup_proportion",
default=0.1,
type=float,
help=
"Proportion of training to perform linear learning rate warmup for. "
"E.g., 0.1 = 10%% of training.")
parser.add_argument("--no_cuda",
action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument('--seed',
type=int,
default=42,
help="random seed for initialization")
parser.add_argument(
'--gradient_accumulation_steps',
type=int,
default=1,
help=
"Number of updates steps to accumulate before performing a backward/update pass."
)
parser.add_argument('--local_rank', default=-1, type=int)
# RNN graph retriever-specific parameters
parser.add_argument("--example_limit", default=None, type=int)
parser.add_argument("--max_para_num", default=10, type=int)
parser.add_argument(
"--neg_chunk",
default=8,
type=int,
help="The chunk size of negative examples during training (to "
"reduce GPU memory consumption with negative sampling)")
parser.add_argument(
"--eval_chunk",
default=100000,
type=int,
help=
"The chunk size of evaluation examples (to reduce RAM consumption during evaluation)"
)
parser.add_argument(
"--split_chunk",
default=300,
type=int,
help=
"The chunk size of BERT encoding during inference (to reduce GPU memory consumption)"
)
parser.add_argument('--train_file_path',
type=str,
default=None,
help="File path to the training data")
parser.add_argument('--dev_file_path',
type=str,
default=None,
help="File path to the eval data")
parser.add_argument('--beam', type=int, default=1, help="Beam size")
parser.add_argument('--min_select_num',
type=int,
default=1,
help="Minimum number of selected paragraphs")
parser.add_argument('--max_select_num',
type=int,
default=3,
help="Maximum number of selected paragraphs")
parser.add_argument(
"--use_redundant",
action='store_true',
help="Whether to use simulated seqs (only for training)")
parser.add_argument(
"--use_multiple_redundant",
action='store_true',
help="Whether to use multiple simulated seqs (only for training)")
parser.add_argument(
'--max_redundant_num',
type=int,
default=100000,
help=
"Whether to limit the number of the initial TF-IDF pool (only for open-domain eval)"
)
parser.add_argument(
"--no_links",
action='store_true',
help=
"Whether to omit any links (or in other words, only use TF-IDF-based paragraphs)"
)
parser.add_argument("--pruning_by_links",
action='store_true',
help="Whether to do pruning by links (and top 1)")
parser.add_argument(
"--expand_links",
action='store_true',
help=
"Whether to expand links with paragraphs in the same article (for NQ)")
parser.add_argument(
'--tfidf_limit',
type=int,
default=None,
help=
"Whether to limit the number of the initial TF-IDF pool (only for open-domain eval)"
)
parser.add_argument("--pred_file",
default=None,
type=str,
help="File name to write paragraph selection results")
parser.add_argument("--tagme",
action='store_true',
help="Whether to use tagme at inference")
parser.add_argument(
'--topk',
type=int,
default=2,
help="Whether to use how many paragraphs from the previous steps")
parser.add_argument(
"--model_suffix",
default=None,
type=str,
help="Suffix to load a model file ('pytorch_model_' + suffix +'.bin')")
parser.add_argument("--db_save_path",
default=None,
type=str,
help="File path to DB")
parser.add_argument("--fp16", default=False, action='store_true')
parser.add_argument("--fp16_opt_level", default="O1", type=str)
parser.add_argument("--do_label",
default=False,
action='store_true',
help="For pre-processing features only.")
parser.add_argument("--oss_cache_dir", default=None, type=str)
parser.add_argument("--cache_dir", default=None, type=str)
parser.add_argument("--dist",
default=False,
action='store_true',
help='use distributed training.')
parser.add_argument("--save_steps", default=5000, type=int)
parser.add_argument("--resume", default=None, type=int)
parser.add_argument("--oss_pretrain", default=None, type=str)
parser.add_argument("--model_version", default='v1', type=str)
parser.add_argument("--disable_rnn_layer_norm",
default=False,
action='store_true')
args = parser.parse_args()
if args.dist:
dist.init_process_group(backend='nccl')
print(f"local rank: {args.local_rank}")
print(f"global rank: {dist.get_rank()}")
print(f"world size: {dist.get_world_size()}")
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available()
and not args.no_cuda else "cpu")
n_gpu = torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
n_gpu = 1
# Initializes the distributed backend which will take care of synchronizing nodes/GPUs
dist.init_process_group(backend='nccl')
if args.dist:
global_rank = dist.get_rank()
world_size = dist.get_world_size()
if world_size > 1:
args.local_rank = global_rank
if args.gradient_accumulation_steps < 1:
raise ValueError(
"Invalid gradient_accumulation_steps parameter: {}, should be >= 1"
.format(args.gradient_accumulation_steps))
args.train_batch_size = int(args.train_batch_size /
args.gradient_accumulation_steps)
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
if args.train_file_path is not None:
do_train = True
if os.path.exists(args.output_dir) and os.listdir(args.output_dir):
raise ValueError(
"Output directory ({}) already exists and is not empty.".
format(args.output_dir))
if args.local_rank in [-1, 0]:
os.makedirs(args.output_dir, exist_ok=True)
elif args.dev_file_path is not None:
do_train = False
else:
raise ValueError(
'One of train_file_path: {} or dev_file_path: {} must be non-None'.
format(args.train_file_path, args.dev_file_path))
processor = DataProcessor()
# Configurations of the graph retriever
graph_retriever_config = GraphRetrieverConfig(
example_limit=args.example_limit,
task=args.task,
max_seq_length=args.max_seq_length,
max_select_num=args.max_select_num,
max_para_num=args.max_para_num,
tfidf_limit=args.tfidf_limit,
train_file_path=args.train_file_path,
use_redundant=args.use_redundant,
use_multiple_redundant=args.use_multiple_redundant,
max_redundant_num=args.max_redundant_num,
dev_file_path=args.dev_file_path,
beam=args.beam,
min_select_num=args.min_select_num,
no_links=args.no_links,
pruning_by_links=args.pruning_by_links,
expand_links=args.expand_links,
eval_chunk=args.eval_chunk,
tagme=args.tagme,
topk=args.topk,
db_save_path=args.db_save_path,
disable_rnn_layer_norm=args.disable_rnn_layer_norm)
logger.info(graph_retriever_config)
logger.info(args)
tokenizer = AutoTokenizer.from_pretrained(args.bert_model)
if args.model_version == 'roberta':
from modeling_graph_retriever_roberta import RobertaForGraphRetriever
elif args.model_version == 'v3':
from modeling_graph_retriever_roberta import RobertaForGraphRetrieverIterV3 as RobertaForGraphRetriever
else:
raise RuntimeError()
##############################
# Training #
##############################
if do_train:
_model_state_dict = None
if args.oss_pretrain is not None:
_model_state_dict = torch.load(load_pretrain_from_oss(
args.oss_pretrain),
map_location='cpu')
logger.info(f"Loaded pretrained model from {args.oss_pretrain}")
if args.resume is not None:
_model_state_dict = torch.load(load_buffer_from_oss(
os.path.join(args.oss_cache_dir,
f"pytorch_model_{args.resume}.bin")),
map_location='cpu')
model = RobertaForGraphRetriever.from_pretrained(
args.bert_model,
graph_retriever_config=graph_retriever_config,
state_dict=_model_state_dict)
model.to(device)
global_step = 0
POSITIVE = 1.0
NEGATIVE = 0.0
_cache_file_name = f"cache_roberta_train_{args.max_seq_length}_{args.max_para_num}"
_examples_cache_file_name = f"examples_{_cache_file_name}"
_features_cache_file_name = f"features_{_cache_file_name}"
# Load training examples
logger.info(f"Loading training examples and features.")
try:
if args.cache_dir is not None and os.path.exists(
os.path.join(args.cache_dir, _features_cache_file_name)):
logger.info(
f"Loading pre-processed features from {os.path.join(args.cache_dir, _features_cache_file_name)}"
)
train_features = torch.load(
os.path.join(args.cache_dir, _features_cache_file_name))
else:
# train_examples = torch.load(load_buffer_from_oss(os.path.join(oss_features_cache_dir,
# _examples_cache_file_name)))
train_features = torch.load(
load_buffer_from_oss(
os.path.join(oss_features_cache_dir,
_features_cache_file_name)))
logger.info(
f"Pre-processed features are loaded from oss: "
f"{os.path.join(oss_features_cache_dir, _features_cache_file_name)}"
)
except:
train_examples = processor.get_train_examples(
graph_retriever_config)
train_features = convert_examples_to_features(
train_examples,
args.max_seq_length,
args.max_para_num,
graph_retriever_config,
tokenizer,
train=True)
logger.info(
f"Saving pre-processed features into oss: {oss_features_cache_dir}"
)
torch_save_to_oss(
train_examples,
os.path.join(oss_features_cache_dir,
_examples_cache_file_name))
torch_save_to_oss(
train_features,
os.path.join(oss_features_cache_dir,
_features_cache_file_name))
if args.do_label:
logger.info("Finished.")
return
# len(train_examples) and len(train_features) can be different, depending on the redundant setting
num_train_steps = int(
len(train_features) / args.train_batch_size /
args.gradient_accumulation_steps * args.num_train_epochs)
# Prepare optimizer
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight', 'layer_norm']
optimizer_grouped_parameters = [{
'params': [
p for n, p in param_optimizer
if not any(nd in n for nd in no_decay)
],
'weight_decay':
0.01
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay':
0.0
}]
t_total = num_train_steps
if args.local_rank != -1:
t_total = t_total // dist.get_world_size()
optimizer = AdamW(optimizer_grouped_parameters,
betas=(0.9, 0.98),
lr=args.learning_rate)
scheduler = get_linear_schedule_with_warmup(
optimizer, int(t_total * args.warmup_proportion), t_total)
logger.info(optimizer)
if args.fp16:
from apex import amp
amp.register_half_function(torch, "einsum")
model, optimizer = amp.initialize(model,
optimizer,
opt_level=args.fp16_opt_level)
if args.local_rank != -1:
if args.fp16_opt_level == 'O2':
try:
import apex
model = apex.parallel.DistributedDataParallel(
model, delay_allreduce=True)
except ImportError:
model = torch.nn.parallel.DistributedDataParallel(
model, find_unused_parameters=True)
else:
model = torch.nn.parallel.DistributedDataParallel(
model, find_unused_parameters=True)
if n_gpu > 1:
model = torch.nn.DataParallel(model)
if args.resume is not None:
_amp_state_dict = os.path.join(args.oss_cache_dir,
f"amp_{args.resume}.bin")
_optimizer_state_dict = os.path.join(
args.oss_cache_dir, f"optimizer_{args.resume}.pt")
_scheduler_state_dict = os.path.join(
args.oss_cache_dir, f"scheduler_{args.resume}.pt")
amp.load_state_dict(
torch.load(load_buffer_from_oss(_amp_state_dict)))
optimizer.load_state_dict(
torch.load(load_buffer_from_oss(_optimizer_state_dict)))
scheduler.load_state_dict(
torch.load(load_buffer_from_oss(_scheduler_state_dict)))
logger.info(f"Loaded resumed state dict of step {args.resume}")
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_features))
logger.info(" Instantaneous batch size per GPU = %d",
args.train_batch_size)
logger.info(
" Total train batch size (w. parallel, distributed & accumulation) = %d",
args.train_batch_size * args.gradient_accumulation_steps *
(dist.get_world_size() if args.local_rank != -1 else 1),
)
logger.info(" Gradient Accumulation steps = %d",
args.gradient_accumulation_steps)
logger.info(" Total optimization steps = %d", t_total)
model.train()
epc = 0
# test
if args.local_rank in [-1, 0]:
if args.fp16:
amp_file = os.path.join(args.oss_cache_dir,
f"amp_{global_step}.bin")
torch_save_to_oss(amp.state_dict(), amp_file)
optimizer_file = os.path.join(args.oss_cache_dir,
f"optimizer_{global_step}.pt")
torch_save_to_oss(optimizer.state_dict(), optimizer_file)
scheduler_file = os.path.join(args.oss_cache_dir,
f"scheduler_{global_step}.pt")
torch_save_to_oss(scheduler.state_dict(), scheduler_file)
tr_loss = 0
for _ in range(int(args.num_train_epochs)):
logger.info('Epoch ' + str(epc + 1))
TOTAL_NUM = len(train_features)
train_start_index = 0
CHUNK_NUM = 8
train_chunk = TOTAL_NUM // CHUNK_NUM
chunk_index = 0
random.shuffle(train_features)
save_retry = False
while train_start_index < TOTAL_NUM:
train_end_index = min(train_start_index + train_chunk - 1,
TOTAL_NUM - 1)
chunk_len = train_end_index - train_start_index + 1
if args.resume is not None and global_step < args.resume:
_chunk_steps = int(
math.ceil(chunk_len * 1.0 / args.train_batch_size /
(1 if args.local_rank == -1 else
dist.get_world_size())))
_chunk_steps = _chunk_steps // args.gradient_accumulation_steps
if global_step + _chunk_steps <= args.resume:
global_step += _chunk_steps
train_start_index = train_end_index + 1
continue
train_features_ = train_features[
train_start_index:train_start_index + chunk_len]
all_input_ids = torch.tensor(
[f.input_ids for f in train_features_], dtype=torch.long)
all_input_masks = torch.tensor(
[f.input_masks for f in train_features_], dtype=torch.long)
all_segment_ids = torch.tensor(
[f.segment_ids for f in train_features_], dtype=torch.long)
all_output_masks = torch.tensor(
[f.output_masks for f in train_features_],
dtype=torch.float)
all_num_paragraphs = torch.tensor(
[f.num_paragraphs for f in train_features_],
dtype=torch.long)
all_num_steps = torch.tensor(
[f.num_steps for f in train_features_], dtype=torch.long)
train_data = TensorDataset(all_input_ids, all_input_masks,
all_segment_ids, all_output_masks,
all_num_paragraphs, all_num_steps)
if args.local_rank != -1:
train_sampler = torch.utils.data.DistributedSampler(
train_data)
else:
train_sampler = RandomSampler(train_data)
train_dataloader = DataLoader(train_data,
sampler=train_sampler,
batch_size=args.train_batch_size,
pin_memory=True,
num_workers=4)
if args.local_rank != -1:
train_dataloader.sampler.set_epoch(epc)
logger.info('Examples from ' + str(train_start_index) +
' to ' + str(train_end_index))
for step, batch in enumerate(
tqdm(train_dataloader,
desc="Iteration",
disable=args.local_rank not in [-1, 0])):
if args.resume is not None and global_step < args.resume:
if (step + 1) % args.gradient_accumulation_steps == 0:
global_step += 1
continue
input_masks = batch[1]
batch_max_len = input_masks.sum(dim=2).max().item()
num_paragraphs = batch[4]
batch_max_para_num = num_paragraphs.max().item()
num_steps = batch[5]
batch_max_steps = num_steps.max().item()
# output_masks_cpu = (batch[3])[:, :batch_max_steps, :batch_max_para_num + 1]
batch = tuple(t.to(device) for t in batch)
input_ids, input_masks, segment_ids, output_masks, _, _ = batch
B = input_ids.size(0)
input_ids = input_ids[:, :batch_max_para_num, :
batch_max_len]
input_masks = input_masks[:, :batch_max_para_num, :
batch_max_len]
segment_ids = segment_ids[:, :batch_max_para_num, :
batch_max_len]
output_masks = output_masks[:, :batch_max_steps, :
batch_max_para_num +
1] # 1 for EOE
target = torch.zeros(output_masks.size()).fill_(
NEGATIVE) # (B, NUM_STEPS, |P|+1) <- 1 for EOE
for i in range(B):
output_masks[i, :num_steps[i], -1] = 1.0 # for EOE
for j in range(num_steps[i].item() - 1):
target[i, j, j].fill_(POSITIVE)
target[i, num_steps[i] - 1, -1].fill_(POSITIVE)
target = target.to(device)
neg_start = batch_max_steps - 1
while neg_start < batch_max_para_num:
neg_end = min(neg_start + args.neg_chunk - 1,
batch_max_para_num - 1)
neg_len = (neg_end - neg_start + 1)
input_ids_ = torch.cat(
(input_ids[:, :batch_max_steps - 1, :],
input_ids[:, neg_start:neg_start + neg_len, :]),
dim=1)
input_masks_ = torch.cat(
(input_masks[:, :batch_max_steps - 1, :],
input_masks[:, neg_start:neg_start + neg_len, :]),
dim=1)
segment_ids_ = torch.cat(
(segment_ids[:, :batch_max_steps - 1, :],
segment_ids[:, neg_start:neg_start + neg_len, :]),
dim=1)
output_masks_ = torch.cat(
(output_masks[:, :, :batch_max_steps - 1],
output_masks[:, :, neg_start:neg_start + neg_len],
output_masks[:, :, batch_max_para_num:
batch_max_para_num + 1]),
dim=2)
target_ = torch.cat(
(target[:, :, :batch_max_steps - 1],
target[:, :, neg_start:neg_start + neg_len],
target[:, :,
batch_max_para_num:batch_max_para_num +
1]),
dim=2)
if neg_start != batch_max_steps - 1:
output_masks_[:, :, :batch_max_steps - 1] = 0.0
output_masks_[:, :, -1] = 0.0
loss = model(input_ids_, segment_ids_, input_masks_,
output_masks_, target_, batch_max_steps)
if n_gpu > 1:
loss = loss.mean(
) # mean() to average on multi-gpu.
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.fp16:
with amp.scale_loss(loss,
optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
tr_loss += loss.item()
neg_start = neg_end + 1
# del input_ids_
# del input_masks_
# del segment_ids_
# del output_masks_
# del target_
if (step + 1) % args.gradient_accumulation_steps == 0:
if args.fp16:
torch.nn.utils.clip_grad_norm_(
amp.master_params(optimizer), 1.0)
else:
torch.nn.utils.clip_grad_norm_(
model.parameters(), 1.0)
optimizer.step()
scheduler.step()
# optimizer.zero_grad()
model.zero_grad()
global_step += 1
if global_step % 50 == 0:
_cur_steps = global_step if args.resume is None else global_step - args.resume
logger.info(
f"Training loss: {tr_loss / _cur_steps}\t"
f"Learning rate: {scheduler.get_lr()[0]}\t"
f"Global step: {global_step}")
if global_step % args.save_steps == 0:
if args.local_rank in [-1, 0]:
model_to_save = model.module if hasattr(
model, 'module') else model
output_model_file = os.path.join(
args.oss_cache_dir,
f"pytorch_model_{global_step}.bin")
torch_save_to_oss(model_to_save.state_dict(),
output_model_file)
_suffix = "" if args.local_rank == -1 else f"_{args.local_rank}"
if args.fp16:
amp_file = os.path.join(
args.oss_cache_dir,
f"amp_{global_step}{_suffix}.bin")
torch_save_to_oss(amp.state_dict(), amp_file)
optimizer_file = os.path.join(
args.oss_cache_dir,
f"optimizer_{global_step}{_suffix}.pt")
torch_save_to_oss(optimizer.state_dict(),
optimizer_file)
scheduler_file = os.path.join(
args.oss_cache_dir,
f"scheduler_{global_step}{_suffix}.pt")
torch_save_to_oss(scheduler.state_dict(),
scheduler_file)
logger.info(
f"checkpoint of step {global_step} is saved to oss."
)
# del input_ids
# del input_masks
# del segment_ids
# del output_masks
# del target
# del batch
chunk_index += 1
train_start_index = train_end_index + 1
# Save the model at the half of the epoch
if (chunk_index == CHUNK_NUM // 2
or save_retry) and args.local_rank in [-1, 0]:
status = save(model, args.output_dir, str(epc + 0.5))
save_retry = (not status)
del train_features_
del all_input_ids
del all_input_masks
del all_segment_ids
del all_output_masks
del all_num_paragraphs
del all_num_steps
del train_data
del train_sampler
del train_dataloader
gc.collect()
# Save the model at the end of the epoch
if args.local_rank in [-1, 0]:
save(model, args.output_dir, str(epc + 1))
# model_to_save = model.module if hasattr(model, 'module') else model # Only save the model it-self
# output_model_file = os.path.join(args.oss_cache_dir, "pytorch_model_" + str(epc + 1) + ".bin")
# torch_save_to_oss(model_to_save.state_dict(), output_model_file)
epc += 1
if do_train:
return
##############################
# Evaluation #
##############################
assert args.model_suffix is not None
if graph_retriever_config.db_save_path is not None:
import sys
sys.path.append('../')
from pipeline.tfidf_retriever import TfidfRetriever
tfidf_retriever = TfidfRetriever(graph_retriever_config.db_save_path,
None)
else:
tfidf_retriever = None
if args.oss_cache_dir is not None:
file_name = 'pytorch_model_' + args.model_suffix + '.bin'
model_state_dict = torch.load(
load_buffer_from_oss(os.path.join(args.oss_cache_dir, file_name)))
else:
model_state_dict = load(args.output_dir, args.model_suffix)
model = RobertaForGraphRetriever.from_pretrained(
args.bert_model,
state_dict=model_state_dict,
graph_retriever_config=graph_retriever_config)
model.to(device)
model.eval()
if args.pred_file is not None:
pred_output = []
eval_examples = processor.get_dev_examples(graph_retriever_config)
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
TOTAL_NUM = len(eval_examples)
eval_start_index = 0
while eval_start_index < TOTAL_NUM:
eval_end_index = min(
eval_start_index + graph_retriever_config.eval_chunk - 1,
TOTAL_NUM - 1)
chunk_len = eval_end_index - eval_start_index + 1
eval_features = convert_examples_to_features(
eval_examples[eval_start_index:eval_start_index + chunk_len],
args.max_seq_length, args.max_para_num, graph_retriever_config,
tokenizer)
all_input_ids = torch.tensor([f.input_ids for f in eval_features],
dtype=torch.long)
all_input_masks = torch.tensor([f.input_masks for f in eval_features],
dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in eval_features],
dtype=torch.long)
all_output_masks = torch.tensor(
[f.output_masks for f in eval_features], dtype=torch.float)
all_num_paragraphs = torch.tensor(
[f.num_paragraphs for f in eval_features], dtype=torch.long)
all_num_steps = torch.tensor([f.num_steps for f in eval_features],
dtype=torch.long)
all_ex_indices = torch.tensor([f.ex_index for f in eval_features],
dtype=torch.long)
eval_data = TensorDataset(all_input_ids, all_input_masks,
all_segment_ids, all_output_masks,
all_num_paragraphs, all_num_steps,
all_ex_indices)
# Run prediction for full data
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data,
sampler=eval_sampler,
batch_size=args.eval_batch_size)
for input_ids, input_masks, segment_ids, output_masks, num_paragraphs, num_steps, ex_indices in tqdm(
eval_dataloader, desc="Evaluating"):
batch_max_len = input_masks.sum(dim=2).max().item()
batch_max_para_num = num_paragraphs.max().item()
batch_max_steps = num_steps.max().item()
input_ids = input_ids[:, :batch_max_para_num, :batch_max_len]
input_masks = input_masks[:, :batch_max_para_num, :batch_max_len]
segment_ids = segment_ids[:, :batch_max_para_num, :batch_max_len]
output_masks = output_masks[:, :batch_max_para_num +
2, :batch_max_para_num + 1]
output_masks[:, 1:, -1] = 1.0 # Ignore EOE in the first step
input_ids = input_ids.to(device)
input_masks = input_masks.to(device)
segment_ids = segment_ids.to(device)
output_masks = output_masks.to(device)
examples = [
eval_examples[eval_start_index + ex_indices[i].item()]
for i in range(input_ids.size(0))
]
with torch.no_grad():
pred, prob, topk_pred, topk_prob = model.beam_search(
input_ids,
segment_ids,
input_masks,
examples=examples,
tokenizer=tokenizer,
retriever=tfidf_retriever,
split_chunk=args.split_chunk)
for i in range(len(pred)):
e = examples[i]
titles = [e.title_order[p] for p in pred[i]]
# Output predictions to a file
if args.pred_file is not None:
pred_output.append({})
pred_output[-1]['q_id'] = e.guid
pred_output[-1]['titles'] = titles
pred_output[-1]['probs'] = []
for prob_ in prob[i]:
entry = {'EOE': prob_[-1]}
for j in range(len(e.title_order)):
entry[e.title_order[j]] = prob_[j]
pred_output[-1]['probs'].append(entry)
topk_titles = [[e.title_order[p] for p in topk_pred[i][j]]
for j in range(len(topk_pred[i]))]
pred_output[-1]['topk_titles'] = topk_titles
topk_probs = []
for k in range(len(topk_prob[i])):
topk_probs.append([])
for prob_ in topk_prob[i][k]:
entry = {'EOE': prob_[-1]}
for j in range(len(e.title_order)):
entry[e.title_order[j]] = prob_[j]
topk_probs[-1].append(entry)
pred_output[-1]['topk_probs'] = topk_probs
# Output the selected paragraphs
context = {}
for ts in topk_titles:
for t in ts:
context[t] = e.all_paras[t]
pred_output[-1]['context'] = context
eval_start_index = eval_end_index + 1
del eval_features
del all_input_ids
del all_input_masks
del all_segment_ids
del all_output_masks
del all_num_paragraphs
del all_num_steps
del all_ex_indices
del eval_data
if args.pred_file is not None:
json.dump(pred_output, open(args.pred_file, 'w'))
def main(args):
set_envs(args)
print("Using: {}".format(args.device))
tokenizer = AutoTokenizer.from_pretrained(args.pretrained_model_path, local_files_only=True)
classifier = AutoQuestionAnswering.from_pretrained(model_path=args.pretrained_model_path,
header_mode=args.header_mode,
cls_index=tokenizer.cls_token_id)
classifier.freeze_to_layer_by_name(args.freeze_layer_name)
classifier.train()
loss_fct = nn.CrossEntropyLoss(ignore_index=-100)
no_decay = ["bias", "LayerNorm.weight"]
optimizer_grouped_parameters = [
{
"params": [p for n, p in classifier.named_parameters() if not any(nd in n for nd in no_decay)],
"weight_decay": args.weight_decay,
},
{"params": [p for n, p in classifier.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0},
]
optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
# optimizer = optim.Adam(filter(lambda p: p.requires_grad, classifier.parameters()),
# lr=args.learning_rate)
# Initialization
opt_level = 'O1'
if args.cuda:
classifier = classifier.to(args.device)
if args.fp16:
classifier, optimizer = amp.initialize(classifier, optimizer, opt_level=opt_level)
amp.register_half_function(torch, "einsum")
# classifier = nn.parallel.DistributedDataParallel(classifier,
# device_ids=args.device_ids,
# output_device=0,
# find_unused_parameters=True)
if args.reload_from_files:
checkpoint = torch.load(args.model_state_file)
classifier.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
amp.load_state_dict(checkpoint['amp'])
train_state = make_train_state(args)
dataset = HotpotQA_QA_Dataset.build_dataset(args.json_train_path)
dataset.set_parameters(tokenizer = tokenizer, topN_sents = args.topN_sents,
max_length=args.max_length, uncased=args.uncased,
permutations=args.permutations, random_seed=args.seed)
print(dataset)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer=optimizer,
mode='min',
factor=0.7,
patience=dataset.get_num_batches(args.batch_size)/50)
# scheduler = get_linear_schedule_with_warmup(
# optimizer,
# num_warmup_steps=args.warmup_steps,
# num_training_steps=dataset.get_num_batches(args.batch_size) * args.num_epochs
# )
try:
writer = SummaryWriter(log_dir=args.log_dir,flush_secs=args.flush_secs)
epoch_bar = tqdm(desc='training routine',
total=args.num_epochs,
position=0)
dataset.set_split('train')
train_bar = tqdm(desc='split=train',
total=dataset.get_num_batches(args.batch_size),
position=1)
dataset.set_split('val')
val_bar = tqdm(desc='split=val',
total=dataset.get_num_batches(args.batch_size),
position=1)
cursor_train = 0
cursor_val = 0
if args.reload_from_files and 'cursor_train' in checkpoint.keys():
cursor_train = checkpoint['cursor_train'] + 1
cursor_val = checkpoint['cursor_val'] + 1
for epoch_index in range(args.num_epochs):
train_bar.n = 0
val_bar.n = 0
train_state['epoch_index'] = epoch_index
dataset.set_split('train')
dataset.random_seed = args.seed + epoch_index
batch_generator = generate_QA_batches(dataset,shuffle=True,
batch_size=args.batch_size,
device=args.device)
running_loss = 0.0
running_ans_span_accuracy = 0.0
running_yes_no_span_accuracy = 0.0
classifier.train()
# dont count running value if denominator == 0.
batch_index_for_yesnospan = 0
batch_index_for_span = 0
for batch_index, batch_dict in enumerate(batch_generator):
optimizer.zero_grad()
yes_no_span = batch_dict.pop('yes_no_span')
res = classifier(**batch_dict)
start_logits, end_logits, cls_logits = res[0], res[1], res[2]
start_loss = loss_fct(start_logits, batch_dict['start_positions'])
end_loss = loss_fct(end_logits, batch_dict['end_positions'])
start_end_loss = (start_loss + end_loss) / 2
if start_end_loss > 1e5:
print(start_logits.gather(-1, batch_dict['start_positions'].view(-1, 1)))
print(batch_dict['special_tokens_mask'].gather(-1, batch_dict['start_positions'].view(-1, 1)))
print(batch_dict['start_positions'])
print('')
print(end_logits.gather(-1, batch_dict['end_positions'].view(-1, 1)))
print(batch_dict['special_tokens_mask'].gather(-1, batch_dict['end_positions'].view(-1, 1)))
print(batch_dict['end_positions'])
exit()
yes_no_span_loss = loss_fct(cls_logits, yes_no_span) / 2
if yes_no_span_loss > 1e5:
print(cls_logits)
print(yes_no_span)
exit()
ans_span_accuracy = compute_span_accuracy(start_logits, batch_dict['start_positions'],
end_logits, batch_dict['end_positions'])
yes_no_span_accuracy = compute_accuracy(cls_logits, yes_no_span)
loss = start_end_loss + yes_no_span_loss
running_loss += (loss.item() - running_loss) / (batch_index + 1)
if ans_span_accuracy:
running_ans_span_accuracy += \
(ans_span_accuracy - running_ans_span_accuracy) / (batch_index_for_span + 1)
batch_index_for_span += 1
if yes_no_span_accuracy:
running_yes_no_span_accuracy += \
(yes_no_span_accuracy - running_yes_no_span_accuracy) / (batch_index_for_yesnospan + 1)
batch_index_for_yesnospan += 1
if args.fp16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
optimizer.step()
scheduler.step(running_loss) # Update learning rate schedule
# update bar
train_bar.set_postfix(running_loss=running_loss,epoch=epoch_index)
train_bar.update()
writer.add_scalar('loss/train', loss.item(), cursor_train)
if ans_span_accuracy:
writer.add_scalar('ans_span_accuracy/train', ans_span_accuracy, cursor_train)
if yes_no_span_accuracy:
writer.add_scalar('yes_no_span_accuracy/train', yes_no_span_accuracy, cursor_train)
writer.add_scalar('running_loss/train', running_loss, cursor_train)
writer.add_scalar('running_ans_span_accuracy/train', running_ans_span_accuracy, cursor_train)
writer.add_scalar('running_yes_no_span_accuracy/train', running_yes_no_span_accuracy, cursor_train)
cursor_train += 1
train_state['train_running_loss'].append(running_loss)
# Iterate over val dataset
# setup: batch generator, set loss and acc to 0; set eval mode on
dataset.set_split('val')
batch_generator = generate_QA_batches(dataset,
batch_size=args.batch_size,
device=args.device)
running_loss = 0.0
running_ans_span_accuracy = 0.0
running_yes_no_span_accuracy = 0.0
classifier.eval()
batch_index_for_yesnospan = 0
batch_index_for_span = 0
for batch_index, batch_dict in enumerate(batch_generator):
with torch.no_grad():
yes_no_span = batch_dict.pop('yes_no_span')
res = classifier(**batch_dict)
start_logits, end_logits, cls_logits = res[0], res[1], res[2]
start_loss = loss_fct(start_logits, batch_dict['start_positions'])
end_loss = loss_fct(end_logits, batch_dict['end_positions'])
start_end_loss = (start_loss + end_loss) / 2
yes_no_span_loss = loss_fct(cls_logits, yes_no_span) / 2
ans_span_accuracy = compute_span_accuracy(start_logits, batch_dict['start_positions'],
end_logits, batch_dict['end_positions'])
yes_no_span_accuracy = compute_accuracy(cls_logits, yes_no_span)
loss = start_end_loss + yes_no_span_loss
running_loss += (loss.item() - running_loss) / (batch_index + 1)
if ans_span_accuracy:
running_ans_span_accuracy += \
(ans_span_accuracy - running_ans_span_accuracy) / (batch_index_for_span + 1)
batch_index_for_span += 1
if yes_no_span_accuracy:
running_yes_no_span_accuracy += \
(yes_no_span_accuracy - running_yes_no_span_accuracy) / (batch_index_for_yesnospan + 1)
batch_index_for_yesnospan += 1
val_bar.set_postfix(running_loss=running_loss,epoch=epoch_index)
val_bar.update()
writer.add_scalar('loss/val', loss.item(), cursor_val)
if ans_span_accuracy:
writer.add_scalar('ans_span_accuracy/val', ans_span_accuracy, cursor_val)
if yes_no_span_accuracy:
writer.add_scalar('yes_no_span_accuracy/val', yes_no_span_accuracy, cursor_val)
writer.add_scalar('running_loss/val', running_loss, cursor_val)
writer.add_scalar('running_ans_span_accuracy/val', running_ans_span_accuracy, cursor_val)
writer.add_scalar('running_yes_no_span_accuracy/val', running_yes_no_span_accuracy, cursor_val)
cursor_val += 1
train_state['val_running_loss'].append(running_loss)
if not args.use_mini:
train_state = update_train_state(args=args,
model=classifier,
optimizer = optimizer,
train_state=train_state)
epoch_bar.update()
if train_state['stop_early']:
print('STOP EARLY!')
break
except KeyboardInterrupt:
print("Exiting loop")
if args.use_mini: rm_rf(args.log_dir)
except :
print_exc()
print(f"err in epoch_index {epoch_index}, batch_index {batch_index}.")
def run():
args = get_args()
fdir = Path(args.dir)
tb = SummaryWriter(args.logdir) # 对啦，tensorboard画图的
device = torch.device(
"cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
output_dir = Path(args.output)
output_dir.mkdir(exist_ok=True, parents=True)
logger.info(args)
logger.info(f"loading vocab...")
tokenizer = Tokenizer.from_pretrained(fdir / 'vocab.pkl')
logger.info(f"loading dataset...")
train_dataset = torch.load(fdir / 'train.pkl')
test_dataset = torch.load(fdir / 'test.pkl')
train_loader = DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=args.batch_size)
logger.info(f"initializing model...")
model = init_model_by_key(args, tokenizer)
model.to(device)
loss_function = nn.CrossEntropyLoss(ignore_index=tokenizer.pad_id)
optimizer = optim.Adam(model.parameters(), lr=args.lr)
if args.fp16:
try:
from apex import amp # 实现不同程度的混合精度加速,提升pytorch的训练速度
amp.register_half_function(
torch, 'einsum'
) # 某些不常用的函数，在使用前需要注册；某些函数（如einsum）暂不支持FP16加速，建议不要用的太heavy
model, optimizer = amp.initialize(model,
optimizer,
opt_level=args.fp16_opt_level)
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use fp16 training."
)
if torch.cuda.device_count() > 1:
model = torch.nn.DataParallel(model)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(
optimizer, mode='min') # 当网络的评价指标不在提升的时候，可以通过降低网络的学习率来提高网络性能。
logger.info(f"num gpu: {torch.cuda.device_count()}")
global_step = 0
for epoch in range(args.epochs):
logger.info(f"***** Epoch {epoch} *****")
model.train()
t1 = time.time()
accu_loss = 0.0
for step, batch in enumerate(train_loader):
optimizer.zero_grad()
batch = tuple(t.to(device) for t in batch)
input_ids, masks, lens, target_ids = batch
logits = model(input_ids, masks)
loss = loss_function(logits.view(-1, tokenizer.vocab_size),
target_ids.view(-1))
if torch.cuda.device_count() > 1:
loss = loss.mean()
accu_loss += loss.item()
if args.fp16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer),
args.max_grad_norm)
else:
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(),
args.max_grad_norm)
optimizer.step()
if step % 100 == 0:
tb.add_scalar('loss', loss.item(),
global_step) # tensorboard画图用的
logger.info(
f"[epoch]: {epoch}, [batch]: {step}, [loss]: {loss.item()}"
)
global_step += 1
scheduler.step(accu_loss)
t2 = time.time()
logger.info(
f"epoch time: {t2-t1:.5}, accumulation loss: {accu_loss:.6}")
if (epoch + 1) % args.test_epoch == 0:
predict_demos(model, tokenizer)
bleu, rl = auto_evaluate(model, test_loader, tokenizer)
logger.info(f"BLEU: {round(bleu, 9)}, Rouge-L: {round(rl, 8)}")
if (epoch + 1) % args.save_epoch == 0:
filename = f"{model.__class__.__name__}_{epoch + 1}.bin"
filename = output_dir / filename
save_model(filename, model, args, tokenizer)
def __init__(
self,
access_mode,
fp16,
fp16_opt_level,
model,
model_name,
device,
myio,
save_dir,
n_best,
max_answer_length,
do_lower_case,
verbose_logging,
version_2_with_negative,
null_score_diff_threshold,
max_steps=1e5,
log_int=1e4,
best_int=500,
verbose_int=1000,
max_grad_norm=1.0,
optimizer=None,
weight_decay=0.0,
lr=5e-3,
eps=1e-8,
warmup_steps=0,
freeze_embeddings=False,
):
"""
Object to store learning. Used for fine-tuning.
Data stored in myio.IO object called myio.
"""
self.debug = False
self.fp16 = fp16
self.fp16_opt_level = fp16_opt_level
self.access_mode = access_mode
self.model = model.to(device)
self.model_name = model_name
self.device = device
self.IO = myio
self.save_dir = save_dir
self.max_steps = max_steps
self.log_int = log_int
self.best_int = best_int
self.verbose_int = verbose_int
self.max_grad_norm = max_grad_norm
self.weight_decay = weight_decay
self.lr = lr
self.eps = eps
self.warmup_steps = warmup_steps
self.freeze = freeze_embeddings
# make directory for recorded weights if doesn't already exist
self.log_dir = os.path.join(self.save_dir, 'logged')
if not os.path.exists(self.log_dir):
os.mkdir(self.log_dir)
# for evaluation
self.n_best = n_best
self.max_answer_length = max_answer_length
self.do_lower_case = do_lower_case
self.verbose_logging = verbose_logging
self.version_2_with_negative = version_2_with_negative
self.null_score_diff_threshold = null_score_diff_threshold
# data
self.train_dataloader = None
self.val_dataloader = None
self.val_examples = None
self.val_features = None
# set optimizer
self.optimizer = optimizer
if optimizer is None:
self.set_optimizer()
# use mixed precision if needed
if self.fp16:
from apex import amp
amp.register_half_function(torch, "einsum")
self.model, self.optimizer = amp.initialize(
self.model, self.optimizer, opt_level=self.fp16_opt_level)
# if multiple GPUs on single device
if torch.cuda.is_available() and torch.cuda.device_count() > 1:
self.model = nn.DataParallel(model)
self.model.to(self.device)
# stop embedding weight grad tracking
if self.freeze:
if isinstance(self.model, nn.DataParallel):
bert = self.model.module.model.bert
else:
bert = self.model.model.bert
for param in bert.parameters():
param.requires_grad = False
log.info("Froze BERT parameters")
if self.debug:
# to check updating
if isinstance(self.model, nn.DataParallel):
qabert = self.model.module.model
else:
qabert = self.model.model
self.fixed_bert = copy.deepcopy(qabert.bert)
self.fixed_qa = copy.deepcopy(qabert.qa_outputs)
def main():
vocab_path = f'{config.data_dir}/vocabs'
args = get_args()
tb = SummaryWriter(args.logdir)
epochs = args.epochs
batch_size = args.batch_size
lr = args.lr
embed_dim = config.embed_dim
hidden_dim = config.hidden_dim
output_dir = config.ouput_dir
print("yes2")
device = torch.device(
"cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
logger.info(f"***** Loading vocab *****")
word_to_ix = load_vocab(vocab_path)
vocab_size = len(word_to_ix)
logger.info(f"***** Initializing dataset *****")
train_dataloader = init_dataset(args.dir, batch_size)
logger.info(f"***** Training *****")
model = TraForEncoder(vocab_size, embed_dim, hidden_dim)
optimizer = optim.Adam(model.parameters(), lr=lr)
model.to(device)
if args.fp16:
try:
from apex import amp
amp.register_half_function(torch, 'einsum')
model, optimizer = amp.initialize(model,
optimizer,
opt_level=args.fp16_opt_level)
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use fp16 training."
)
if torch.cuda.device_count() > 1:
model = torch.nn.DataParallel(model)
model.train()
loss_func = nn.CrossEntropyLoss(ignore_index=word_to_ix['[PAD]'])
logger.info(f"Num GPU {torch.cuda.device_count()}")
global_step = 0
for epoch in range(epochs):
logger.info(f"***** Epoch {epoch} *****")
for step, batch in enumerate(train_dataloader):
optimizer.zero_grad()
batch = tuple(t.to(device) for t in batch)
seq_ids, exted_att_mask, tag_ids = batch
logits = model(seq_ids, exted_att_mask)
loss = loss_func(logits.view(-1, vocab_size), tag_ids.view(-1))
if torch.cuda.device_count() > 1:
loss = loss.mean()
if args.fp16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer),
args.max_grad_norm)
else:
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(),
args.max_grad_norm)
optimizer.step()
if step % 100 == 0:
tb.add_scalar('loss', loss.item(), global_step)
logger.info(
f"[epoch]: {epoch}, [batch]: {step}, [loss]: {loss.item()}"
)
global_step += 1
save_model(model, output_dir, epoch + 1)
def train(args, logger, tb_writer):
logger.info('Args: {}'.format(json.dumps(vars(args), indent=4, sort_keys=True)))
if args.local_rank in [-1, 0]:
with open(os.path.join(args.save_dir, 'args.yaml'), 'w') as file:
yaml.safe_dump(vars(args), file, sort_keys=False)
device_id = args.local_rank if args.local_rank != -1 else 0
device = torch.device('cuda', device_id)
logger.warning(f'Using GPU {args.local_rank}.')
world_size = torch.distributed.get_world_size() if args.local_rank != -1 else 1
logger.info(f'Total number of GPUs used: {world_size}.')
effective_batch_size = args.batch_size * world_size * args.accumulation_steps
logger.info(f'Effective batch size: {effective_batch_size}.')
num_train_samples_per_epoch, num_dev_samples, num_unique_train_epochs = get_data_sizes(data_dir=args.data_dir,
num_epochs=args.num_epochs,
logger=logger)
num_optimization_steps = sum(num_train_samples_per_epoch) // world_size // args.batch_size // \
args.accumulation_steps
if args.max_steps > 0:
num_optimization_steps = min(num_optimization_steps, args.max_steps)
logger.info(f'Total number of optimization steps: {num_optimization_steps}.')
# Set random seed
logger.info(f'Using random seed {args.seed}.')
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
# Get model
if args.local_rank not in [-1, 0]:
torch.distributed.barrier()
logger.info(f'Loading model {args.model} for task {args.task}...')
model = ModelRegistry.get_model(args.task).from_pretrained(args.model)
if args.local_rank in [-1, 0]:
with open(os.path.join(args.save_dir, 'config.json'), 'w') as file:
json.dump(model.config.__dict__, file)
if args.local_rank == 0:
torch.distributed.barrier()
model.to(device)
# Get optimizer
logger.info('Creating optimizer...')
parameter_groups = get_parameter_groups(model)
optimizer = AdamW(parameter_groups, lr=args.learning_rate, weight_decay=args.weight_decay, eps=1e-8)
scheduler = get_lr_scheduler(optimizer, num_steps=num_optimization_steps, warmup_proportion=args.warmup_proportion)
if args.amp:
amp.register_half_function(torch, 'einsum')
model, optimizer = amp.initialize(model, optimizer, opt_level=args.amp_opt_level)
if args.local_rank != -1:
model = DistributedDataParallel(model,
device_ids=[args.local_rank],
output_device=args.local_rank,
find_unused_parameters=True)
# Get dev data loader
dev_data_file = os.path.join(args.data_dir, f'dev.jsonl.gz')
logger.info(f'Creating dev dataset from {dev_data_file}...')
dev_dataset = DatasetRegistry.get_dataset(args.task)(data_file=dev_data_file,
data_size=num_dev_samples,
local_rank=-1)
dev_loader = DataLoader(dev_dataset,
batch_size=2 * args.batch_size,
num_workers=1,
collate_fn=dev_dataset.collate_fn)
# Get evaluator
evaluator = EvaluatorRegistry.get_evaluator(args.task)(data_loader=dev_loader,
logger=logger,
tb_writer=tb_writer,
device=device,
world_size=world_size,
args=args)
# Get saver
saver = CheckpointSaver(save_dir=args.save_dir,
max_checkpoints=args.max_checkpoints,
primary_metric=evaluator.primary_metric,
maximize_metric=evaluator.maximize_metric,
logger=logger)
global_step = 0
samples_processed = 0
# Train
logger.info('Training...')
samples_till_eval = args.eval_every
for epoch in range(1, args.num_epochs + 1):
# Get train data loader for current epoch
train_data_file_num = ((epoch - 1) % num_unique_train_epochs) + 1
train_data_file = os.path.join(args.data_dir, f'epoch_{train_data_file_num}.jsonl.gz')
logger.info(f'Creating training dataset from {train_data_file}...')
train_dataset = DatasetRegistry.get_dataset(args.task)(train_data_file,
data_size=num_train_samples_per_epoch[epoch - 1],
local_rank=args.local_rank,
world_size=world_size)
train_loader = DataLoader(train_dataset,
batch_size=args.batch_size,
num_workers=1,
collate_fn=train_dataset.collate_fn)
logger.info(f'Starting epoch {epoch}...')
model.train()
model.zero_grad()
loss_values = defaultdict(float)
samples_till_end = (num_optimization_steps - global_step) * effective_batch_size
samples_in_cur_epoch = min([len(train_loader.dataset), samples_till_end])
disable_progress_bar = (args.local_rank not in [-1, 0])
with tqdm(total=samples_in_cur_epoch, disable=disable_progress_bar) as progress_bar:
for step, batch in enumerate(train_loader, 1):
batch = {name: tensor.to(device) for name, tensor in batch.items()}
current_batch_size = batch['input_ids'].shape[0]
outputs = model(**batch)
loss, current_loss_values = outputs[:2]
loss = loss / args.accumulation_steps
for name, value in current_loss_values.items():
loss_values[name] += value / args.accumulation_steps
if args.amp:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
samples_processed += current_batch_size * world_size
samples_till_eval -= current_batch_size * world_size
progress_bar.update(current_batch_size * world_size)
if step % args.accumulation_steps == 0:
current_lr = scheduler.get_last_lr()[0]
if args.amp:
torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), 1.0)
else:
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
scheduler.step()
model.zero_grad()
global_step += 1
# Log info
progress_bar.set_postfix(epoch=epoch, step=global_step, lr=current_lr, **loss_values)
if args.local_rank in [-1, 0]:
tb_writer.add_scalar('train/LR', current_lr, global_step)
for name, value in loss_values.items():
tb_writer.add_scalar(f'train/{name}', value, global_step)
loss_values = {name: 0 for name in loss_values}
if global_step == args.max_steps:
logger.info('Reached maximum number of optimization steps.')
break
if samples_till_eval <= 0:
samples_till_eval = args.eval_every
eval_results = evaluator.evaluate(model, global_step)
if args.local_rank in [-1, 0]:
saver.save(model, global_step, eval_results)
if not args.do_not_eval_after_epoch:
eval_results = evaluator.evaluate(model, global_step)
if args.local_rank in [-1, 0]:
saver.save(model, global_step, eval_results)
def train_ts(args):
def build_scheduler(optimizers, args):
optimizer, optimizer_sparse = optimizers
scheduler_sparse = None
if args.scheduler == "cosine":
# here we do not set eta_min to lr_min to be backward compatible
# because in previous versions eta_min is default to 0
# rather than the default value of lr_min 1e-6
scheduler = optim.lr_scheduler.CosineAnnealingLR(
optimizer, args.max_step,
eta_min=args.eta_min) # should use eta_min arg
elif args.scheduler == "inv_sqrt":
# originally used for Transformer (in Attention is all you need)
def lr_lambda(step):
# return a multiplier instead of a learning rate
if step == 0 and args.warmup_step == 0:
return 1.0
else:
return (1.0 /
(step**0.5) if step > args.warmup_step else step /
(args.warmup_step**1.5))
scheduler = optim.lr_scheduler.LambdaLR(optimizer,
lr_lambda=lr_lambda)
elif args.scheduler == "dev_perf":
scheduler = optim.lr_scheduler.ReduceLROnPlateau(
optimizer,
factor=args.decay_rate,
patience=args.patience,
min_lr=args.lr_min,
)
elif args.scheduler == "constant":
pass
else:
raise ValueError(f"scheduler type {args.scheduler} not recognized")
return scheduler, scheduler_sparse
###############################################################################
# Training code
###############################################################################
def evaluate(eval_iter, model):
# Turn on evaluation mode which disables dropout.
model.eval()
# debug
# If the model does not use memory at all, make the ext_len longer.
# Otherwise, make the mem_len longer and keep the ext_len the same.
# if default_args.mem_len == 0:
# model.reset_length(default_args.eval_tgt_len,
# default_args.ext_len + default_args.tgt_len -
# default_args.eval_tgt_len, default_args.mem_len)
# else:
# model.reset_length(default_args.eval_tgt_len,
# default_args.ext_len, default_args.mem_len +
# default_args.tgt_len - default_args.eval_tgt_len)
# Evaluation
total_len, total_loss = 0, 0.0
with torch.no_grad():
mems = tuple()
for i, (data, target, seq_len) in enumerate(eval_iter):
if i >= args.max_eval_steps > 0:
break
ret = model(data, target, *mems)
loss, mems = ret[0], ret[1:]
loss = loss.mean()
total_loss += seq_len * loss.float().item()
total_len += seq_len
# Switch back to the training mode
# model.reset_length(default_args.tgt_len, default_args.ext_len,
# default_args.mem_len)
model.train()
return total_loss / total_len
# reverse distillation util
def get_original_batches(model, tr_iter, integration_length):
model.eval()
if args.batch_chunk > 1:
mems = [None for _ in range(args.batch_chunk)]
first_logits = [[] for _ in range(args.batch_chunk)]
else:
mems = None
first_logits = []
train_iter = tr_iter.get_varlen_iter() if args.varlen else tr_iter
with torch.no_grad():
for batch, (data, target, seq_len) in enumerate(train_iter):
if batch == integration_length:
break
if args.batch_chunk > 1:
data_chunks = torch.chunk(data, args.batch_chunk, 1)
for i in range(args.batch_chunk):
data_i = data_chunks[i].contiguous()
logits, mems[i] = model._forward(data_i, mems=mems[i])
first_logits[i].append(logits.cpu())
else:
logits, mems = model._forward(data, mems=mems)
first_logits.append(logits.cpu())
return first_logits
def build_optimizer(model, args, reload=False):
optimizer_sparse = None
if args.optim.lower() == "sgd":
optimizer = optim.SGD(model.parameters(),
lr=args.lr,
momentum=args.mom)
elif args.optim.lower() == "adam":
optimizer = optim.Adam(model.parameters(), lr=args.lr)
elif args.optim.lower() == "adagrad":
optimizer = optim.Adagrad(model.parameters(), lr=args.lr)
else:
raise ValueError(f"optimizer type {args.optim} not recognized")
if reload:
if args.restart_from is not None:
optim_name = f"optimizer_{args.restart_from}.pt"
else:
optim_name = "optimizer.pt"
optim_file_name = os.path.join(args.restart_dir, optim_name)
logging(f"reloading {optim_file_name}")
if os.path.exists(os.path.join(args.restart_dir, optim_name)):
with open(os.path.join(args.restart_dir, optim_name),
"rb") as optim_file:
opt_state_dict = torch.load(optim_file)
try:
optimizer.load_state_dict(opt_state_dict)
# in case the optimizer param groups aren't the same shape,
# merge them
except:
logging("merging optimizer param groups")
opt_state_dict["param_groups"][0]["params"] = [
param
for param_group in opt_state_dict["param_groups"]
for param in param_group["params"]
]
opt_state_dict["param_groups"] = [
opt_state_dict["param_groups"][0]
]
optimizer.load_state_dict(opt_state_dict)
else:
logging("Optimizer was not saved. Start from scratch.")
return optimizer, optimizer_sparse
def log_val(val_loss, step, compute):
logging("-" * 100)
log_str = ("| Eval {:3d} at step {:>8d} | time: {:5.2f}s "
"| valid loss {:5.2f}".format(
step // args.eval_interval,
step,
(time.time() - eval_start_time),
val_loss,
))
log_str += " | bpc {:9.5f}".format(val_loss / math.log(2))
logging(log_str)
logging("-" * 100)
def epoch_loop(
epoch,
model,
optimizers,
schedulers,
):
nonlocal train_step
# Turn on training mode which enables dropout.
if isinstance(model, nn.DataParallel):
parent_model = model.module
else:
parent_model = model
optimizer, optimizer_sparse = optimizers
scheduler, scheduler_sparse = schedulers
# global train_step, best_val_loss, eval_start_time, log_start_time
train_losses = []
model.train()
if args.batch_chunk > 1:
mems = [tuple() for _ in range(args.batch_chunk)]
else:
mems = tuple()
train_iter = tr_iter.get_varlen_iter() if args.varlen else tr_iter
log_start_time = time.time()
best_val_loss = float("Infinity")
for batch, (data, target, seq_len) in enumerate(train_iter):
model.zero_grad()
if args.batch_chunk > 1:
data_chunks = torch.chunk(data, args.batch_chunk, 1)
target_chunks = torch.chunk(target, args.batch_chunk, 1)
for i in range(args.batch_chunk):
data_i = data_chunks[i].contiguous()
target_i = target_chunks[i].contiguous()
ret = model(data_i, target_i, *mems[i])
loss, mems[i] = ret[0], ret[1:]
loss = loss.float().mean().type_as(loss) / args.batch_chunk
if args.fp16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
train_losses.append(loss.float().item())
else:
ret = model(data, target, *mems)
loss, mems = ret[0], ret[1:]
loss = loss.float().mean().type_as(loss)
if args.fp16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
train_losses.append(loss.float().item())
if args.fp16:
torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer),
args.clip)
else:
torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip)
optimizer.step()
parent_model.compute += openai_compute(
non_emb_param_count(parent_model, nseries), data.numel(), 1)
# step-wise learning rate annealing
train_step += 1
parent_model.training_steps += 1
# check for yet-to-thaw parameters
if getattr(parent_model, "freeze_countdown", 0) > 0:
parent_model.freeze_countdown -= 1
# if this is the last step
if parent_model.freeze_countdown == 0:
for parameter in parent_model.parameters():
parameter.requires_grad = True
logging("thawing all parameters")
if args.scheduler in ["cosine", "constant", "dev_perf"]:
# linear warmup stage
if train_step < args.warmup_step:
curr_lr = args.lr * train_step / args.warmup_step
optimizer.param_groups = curr_lr
else:
if args.scheduler == "cosine":
scheduler.step(train_step)
elif args.scheduler == "inv_sqrt":
scheduler.step(train_step)
if train_step % args.log_interval == 0:
cur_loss = np.mean(train_losses)
elapsed = time.time() - log_start_time
log_str = ("| epoch {:3d} step {:>8d} "
"| {:>6d} batches "
"| lr {:.3g} "
"| ms/batch {:5.2f} "
"| loss {:5.2f}".format(
epoch,
train_step,
batch + 1,
optimizer.param_groups[0]["lr"],
elapsed * 1000 / args.log_interval,
cur_loss,
))
log_str += " | bpc {:9.5f}".format(cur_loss / math.log(2))
logging(log_str)
train_losses = []
log_start_time = time.time()
if train_step % args.eval_interval == 0:
val_loss = evaluate(va_iter, model)
log_val(val_loss,
step=train_step,
compute=parent_model.compute)
# Save the model if the validation loss is the best we've seen so
# far.
if not best_val_loss or val_loss < best_val_loss:
best_val_loss = val_loss
if not args.debug:
if args.fp16:
with open(
os.path.join(args.work_dir,
"amp_checkpoint.pt"),
"wb",
) as f:
checkpoint = {
"model": model.state_dict(),
"optimizer": optimizer.state_dict(),
"amp": amp.state_dict(),
}
torch.save(checkpoint, f)
else:
with open(os.path.join(args.work_dir, "model.pt"),
"wb") as f:
torch.save(parent_model, f)
with open(
os.path.join(args.work_dir,
"optimizer.pt"),
"wb",
) as f:
torch.save(optimizer.state_dict(), f)
# dev-performance based learning rate annealing
if args.scheduler == "dev_perf":
scheduler.step(val_loss)
eval_start_time = time.time()
if train_step == args.max_step:
break
def expand_model(
strategy,
integration,
integration_length,
n_add,
model: MemTransformerLM,
optimizers,
schedulers,
tr_iter,
va_iter,
epoch,
step,
):
optimizer, _ = optimizers
scheduler, _ = schedulers
if integration:
if not integration_length or integration_length <= 0:
warnings.warn(
f"integration {integration} passed but integration_length is {integration_length}"
)
else:
logging(
f"applying integration strategy {integration} with integration length {integration_length}"
)
# pre-expansion validation
logging(f"evaluating before expanding")
val_loss = evaluate(va_iter, model)
log_val(val_loss, step=step, compute=model.compute)
# infer example logits for reverse distillation
if "reverse_distil" in integration:
first_logits = get_original_batches(model, tr_iter,
integration_length)
# expansion
logging(
f"adding {n_add} layers before starting epoch {epoch} with method {strategy}"
)
new_layers = model.expand_layers(n_add,
strategy=strategy,
function=initialization_func)
# optimizer update
optimizer.add_param_group({
"params":
new_layers.parameters(),
"lr":
optimizer.param_groups[0]["lr"],
"initial_lr":
optimizer.param_groups[0]["initial_lr"],
})
scheduler.base_lrs.append(optimizer.param_groups[-1]["initial_lr"])
# training loop for reverse distillation
if "reverse_distil" in integration:
fit_to_previous_model(model, new_layers, tr_iter, first_logits,
integration)
# freezing parameters for frozen restart, we do this afterwards else the
# new layers get copied also without grads
if "freeze" in integration and integration_length > 0:
for param_group in optimizer.param_groups[:-1]:
for parameter in param_group["params"]:
parameter.requires_grad = False
model.freeze_countdown = integration_length
# post-expansion validation
logging(f"reevaluating")
val_loss = evaluate(va_iter, model)
log_val(val_loss, step=step, compute=model.compute)
def expand_state(param, state):
if param.shape != state.shape:
ratios = [
param.shape[i] // state.shape[i]
for i in range(len(param.shape))
]
return state.repeat(*ratios)
else:
return state
def widen_model(
strategy,
ratio,
model: MemTransformerLM,
optimizers,
va_iter,
epoch,
step,
):
optimizer, _ = optimizers
# pre-expansion validation
logging(f"evaluating before widening")
# debug
val_loss = evaluate(va_iter, model)
log_val(val_loss, compute=model.compute, step=step)
# infer example logits for reverse distillation expansion
logging(
f"adding {ratio} layers before starting epoch {epoch} with method {strategy}"
)
model.add_heads(ratio, strategy=strategy, function=initialization_func)
# optimizer update
for param, states in optimizer.state.items():
if isinstance(param, nn.Parameter):
states["exp_avg"] = expand_state(param, states["exp_avg"])
states["exp_avg_sq"] = expand_state(param,
states["exp_avg_sq"])
# training loop for reverse distillation
# post-expansion validation
logging(f"reevaluating")
val_loss = evaluate(va_iter, model)
log_val(val_loss, step=step, compute=model.compute)
# reverse distillation trainer
def fit_to_previous_model(model, new_layers, tr_iter, first_logits,
integration):
mse_loss = torch.nn.MSELoss()
if "partial" in integration:
distil_optimizer, distil_optimizer_sparse = build_optimizer(
new_layers, reload=False)
else:
distil_optimizer, distil_optimizer_sparse = build_optimizer(
model, reload=False)
if args.cuda and args.fp16:
model, distil_optimizer = amp.initialize(model,
distil_optimizer,
opt_level=args.fp16)
model.train()
if args.batch_chunk > 1:
mems = [None for _ in range(args.batch_chunk)]
else:
mems = None
train_iter = tr_iter.get_varlen_iter() if args.varlen else tr_iter
for batch, (data, _, _) in enumerate(train_iter):
if batch == len(first_logits):
break
model.zero_grad()
if args.batch_chunk > 1:
data_chunks = torch.chunk(data, args.batch_chunk, 1)
for i in range(args.batch_chunk):
data_i = data_chunks[i].contiguous()
logits, mems[i] = model._forward(data_i, mems=mems[i])
target_logits = first_logits[i][batch].to(logits.device)
loss = mse_loss(logits, target_logits) / args.batch_chunk
if args.fp16:
with amp.scale_loss(loss,
distil_optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
else:
logits, mems = model._forward(data, mems=mems)
target_logits = first_logits[batch].to(logits.device)
loss = mse_loss(logits, target_logits)
if args.fp16:
with amp.scale_loss(loss, distil_optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
if args.fp16:
torch.nn.utils.clip_grad_norm_(
amp.master_params(distil_optimizer), args.clip)
else:
torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip)
distil_optimizer.step()
###################################################################################
#
# main()
#
args.tied = not args.not_tied
if args.d_embed < 0:
args.d_embed = args.d_model
# Validate `--fp16` option
if args.fp16:
if not args.cuda:
print("WARNING: --fp16 requires --cuda, ignoring --fp16 option")
args.fp16 = False
else:
try:
from apex import amp
if args.fp16 == "O1":
amp.register_half_function(torch, "einsum")
except:
print("WARNING: apex not installed, ignoring --fp16 option")
args.fp16 = False
device = torch.device("cuda" if args.cuda else "cpu")
# Set the random seed manually for reproducibility.
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if torch.cuda.is_available():
if not args.cuda:
print(
"WARNING: You have a CUDA device, so you should probably run "
"with --cuda ")
else:
torch.cuda.manual_seed_all(args.seed)
############################################################################
# Logging
############################################################################
assert args.ext_len >= 0, "extended context length must be non-negative"
assert args.d_batch % args.batch_chunk == 0
args.work_dir = "{}-{}".format(args.work_dir, args.dataset)
args.work_dir = os.path.join(args.work_dir, time.strftime("%Y%m%d-%H%M%S"))
logging = create_exp_dir(
args.work_dir,
scripts_to_save=["train_ts.py", "mem_transformer.py"],
debug=args.debug,
)
############################################################################
# Load data
############################################################################
time_series = get_time_series(args.datadir, args.dataset)
nseries = len(time_series.vocab)
args.n_token = nseries
eval_batch_size = 20
tr_iter = time_series.get_iterator(
"train",
args.d_batch,
args.tgt_len,
device=device,
ext_len=args.ext_len,
)
va_iter = time_series.get_iterator(
"valid",
eval_batch_size,
args.eval_tgt_len,
device=device,
ext_len=args.ext_len,
)
te_iter = time_series.get_iterator(
"test",
eval_batch_size,
args.eval_tgt_len,
device=device,
ext_len=args.ext_len,
)
cutoffs, tie_projs = [], [False]
############################################################################
# Define model
############################################################################
initialization_func = partial(
weights_init,
init=args.init,
init_range=args.init_range,
init_std=args.init_std,
proj_init_std=args.proj_init_std,
)
if args.restart and not args.fp16:
if args.restart_from is not None:
model_name = f"model_{args.restart_from}.pt"
else:
model_name = "model.pt"
model_file_name = os.path.join(args.restart_dir, model_name)
logging(f"reloading {model_file_name}")
with open(model_file_name, "rb") as f:
model = torch.load(f)
# backwards compatibility with older saves
if isinstance(model, nn.DataParallel):
model = model.module
model.backward_compatible(tie_weight=args.tied, tie_projs=tie_projs)
if not args.fp16:
model = model.float()
model.apply(update_dropout)
model.apply(update_dropatt)
else:
model = MemTransformerLM(
nseries,
args.n_layer,
args.n_head,
args.d_model,
args.d_head,
args.d_inner,
args.dropout,
args.dropatt,
tie_weight=args.tied,
d_embed=args.d_embed,
div_val=args.div_val,
tie_projs=tie_projs,
pre_lnorm=args.pre_lnorm,
tgt_len=args.tgt_len,
ext_len=args.ext_len,
mem_len=args.mem_len,
cutoffs=cutoffs,
same_length=args.same_length,
clamp_len=args.clamp_len,
)
model.apply(initialization_func)
# debug
# model.word_emb.apply(initialization_func)
# ensure embedding init is not overridden by out_layer in case of
# weight sharing
args.n_all_param = sum([p.nelement() for p in model.parameters()])
args.n_nonemb_param = non_emb_param_count(model, nseries)
logging("=" * 100)
for k, v in args.__dict__.items():
logging(" - {} : {}".format(k, v))
logging("=" * 100)
logging("#params = {}".format(args.n_all_param))
logging("#non emb params = {}".format(args.n_nonemb_param))
para_model = parallelize_model(model, args)
optimizers = build_optimizer(para_model,
args,
reload=args.restart and not args.fp16)
optimizer, optimizer_sparse = optimizers
schedulers = build_scheduler(optimizers, args)
scheduler, scheduler_sparse = schedulers
if args.cuda and args.fp16:
para_model, optimizer = amp.initialize(para_model,
optimizer,
opt_level=args.fp16)
if args.restart:
if args.restart_from is not None:
checkpoint_name = f"amp_checkpoint_{args.restart_from}.pt"
else:
checkpoint_name = "amp_checkpoint.pt"
with open(os.path.join(args.work_dir, checkpoint_name), "rb") as f:
checkpoint = torch.load(f)
model.load_state_dict(checkpoint["model"])
optimizer.load_state_dict(checkpoint["optimizer"])
amp.load_state_dict(checkpoint["amp"])
############################################################################
# Training loop
############################################################################
# Loop over epochs.
if args.reset_lr:
# then they're different and we use train_step only for the new lr
# scheduling
train_step = 0
optimizer.defaults["lr"] = args.lr
for param_group in optimizer.param_groups:
param_group["lr"] = args.lr
param_group["initial_lr"] = args.lr
scheduler.base_lrs = [args.lr] * len(scheduler.base_lrs)
else:
train_step = model.training_steps
best_val_loss = None
# Reload previous step number in case of default_args.restart
if train_step > 0:
logging(f"restarting from step {train_step}")
log_start_time = time.time()
eval_start_time = time.time()
def run_training():
nonlocal train_step
for epoch in itertools.count(start=first_epoch):
# we check before the training loop; expanding at epoch 0 means
# before training (for debug purposes)
if args.expand and str(epoch - 1) in args.expansion_dict:
n_add = int(args.expansion_dict[str(epoch - 1)])
expand_model(
args.expand,
args.integration,
args.integration_length,
n_add,
model,
optimizers,
schedulers,
tr_iter,
va_iter,
epoch,
train_step,
)
if args.widen and str(epoch - 1) in args.widen_dict:
ratio = int(args.widen_dict[str(epoch - 1)])
widen_model(
args.widen,
ratio,
model,
optimizers,
va_iter,
epoch,
train_step,
)
epoch_loop(epoch, para_model, optimizers, schedulers)
if train_step >= args.max_step:
logging("-" * 100)
logging("End of training")
break
if not args.debug and args.log_first_epochs:
if epoch <= args.log_first_epochs:
logging(f"saving model at the end of epoch {epoch}")
if args.fp16:
with open(
os.path.join(args.work_dir,
f"amp_checkpoint_{epoch}.pt"),
"wb",
) as f:
checkpoint = {
"model": model.state_dict(),
"optimizer": optimizer.state_dict(),
"amp": amp.state_dict(),
}
torch.save(checkpoint, f)
else:
with open(
os.path.join(args.work_dir,
f"model_{epoch}.pt"),
"wb",
) as f:
torch.save(model, f)
with open(
os.path.join(args.work_dir,
f"optimizer_{epoch}.pt"),
"wb",
) as f:
torch.save(optimizer.state_dict(), f)
# At any point you can hit Ctrl + C to break out of training early.
try:
if args.restart_from:
first_epoch = args.restart_from + 1
print(f"restarting from epoch {first_epoch}")
else:
first_epoch = 1
run_training()
except KeyboardInterrupt:
logging("-" * 100)
logging("Exiting from training early")
# Load the best model.
if args.fp16:
with open(os.path.join(args.work_dir, "amp_checkpoint.pt"), "rb") as f:
checkpoint = torch.load(f)
model.load_state_dict(checkpoint["model"])
optimizer.load_state_dict(checkpoint["optimizer"])
amp.load_state_dict(checkpoint["amp"])
else:
with open(os.path.join(args.work_dir, "model.pt"), "rb") as f:
model = torch.load(f)
para_model = model.to(device)
# Run on test data.
test_loss = evaluate(te_iter, para_model)
logging("=" * 100)
logging("| End of training | test loss {:5.2f} | test bpc {:9.5f}".format(
test_loss, test_loss / math.log(2)))
logging("=" * 100)
return None, dw, db, None, None, None, None, None
else:
if (not ctx.needs_input_grad[1] and not ctx.needs_input_grad[0]):
return None, None, None, None, None, None, None
dx, dw = NHWC.cudnn_convolution_transpose_backward_nhwc(x, grad_y, w,
ctx.padding, ctx.output_padding, ctx.stride, ctx.dilation, ctx.groups,
torch.backends.cudnn.benchmark, torch.backends.cudnn.deterministic,
list(ctx.needs_input_grad[0:2]))
if (not ctx.needs_input_grad[1]):
return None, None, None, None, None, None, None, None
elif ctx.needs_input_grad[0]:
return dx, dw, None, None, None, None, None, None
else:
return None, dw, None, None, None, None, None, None
amp.register_half_function(conv2d_NHWC_impl,'apply')
amp.register_half_function(conv2d_transpose_NHWC_impl,'apply')
class Conv2d_NHWC(_ConvNd):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True):
kernel_size = _pair(kernel_size)
stride = _pair(stride)
padding = _pair(padding)
dilation = _pair(dilation)
super(Conv2d_NHWC, self).__init__(
in_channels, out_channels, kernel_size, stride, padding, dilation,
False, _pair(0), groups, bias=bias, padding_mode='zeros')
# permute filters
self.weight = torch.nn.Parameter(self.weight.permute(0, 2, 3, 1).contiguous())
def forward(self, x):
from joeynmt.helpers import log_data_info, load_config, log_cfg, \
store_attention_plots, load_checkpoint, make_model_dir, \
make_logger, set_seed, symlink_update, latest_checkpoint_update, \
ConfigurationError
from joeynmt.model import Model, _DataParallel
from joeynmt.prediction import validate_on_data
from joeynmt.loss import XentLoss
from joeynmt.data import load_data, make_data_iter
from joeynmt.builders import build_optimizer, build_scheduler, \
build_gradient_clipper
from joeynmt.prediction import test
# for fp16 training
try:
from apex import amp
amp.register_half_function(torch, "einsum")
except ImportError as no_apex:
# error handling in TrainManager object construction
pass
logger = logging.getLogger(__name__)
# pylint: disable=too-many-instance-attributes
class TrainManager:
""" Manages training loop, validations, learning rate scheduling
and early stopping."""
def __init__(self,
model: Model,
config: dict,
batch_class: Batch = Batch) -> None:
'same_length': False,
'clamp_len': -1,
'seed': 1111,
'max_step': 100,
'cuda': True,
'multi_gpu': False,
'gpu0_bsz': -1,
'debug': False,
'knockknock': True,
'tied': True
})
device = torch.device('cuda' if default_args.cuda else 'cpu')
if args.fp16 == "O1":
amp.register_half_function(torch, 'einsum')
cutoffs, tie_projs = [], [False]
if default_args.adaptive:
assert default_args.dataset in ['wt103', 'lm1b']
if default_args.dataset == 'wt103':
cutoffs = [20000, 40000, 200000]
tie_projs += [True] * len(cutoffs)
elif default_args.dataset == 'lm1b':
cutoffs = [60000, 100000, 640000]
tie_projs += [False] * len(cutoffs)
for n_layer, d_model, batch_size in product(args.n_layers, args.d_models,
args.batch_sizes):
n_layer, d_model, batch_size = int(n_layer), int(d_model), int(
def main():
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--bert_model",
default=None,
type=str,
required=True,
help="Bert pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, "
"bert-base-multilingual-cased, bert-base-chinese.")
parser.add_argument(
"--output_dir",
default=None,
type=str,
required=True,
help=
"The output directory where the model checkpoints and predictions will be written."
)
# Optimizer parameters
parser.add_argument("--adam_epsilon",
default=1e-6,
type=float,
help="Epsilon for Adam optimizer.")
parser.add_argument("--max_grad_norm",
default=1.0,
type=float,
help="Max gradient norm.")
parser.add_argument("--adam_betas", default="(0.9, 0.999)", type=str)
parser.add_argument("--no_bias_correction",
default=False,
action='store_true')
# Other parameters
parser.add_argument("--train_file",
default=None,
type=str,
help="SQuAD-format json file for training.")
parser.add_argument("--predict_file",
default=None,
type=str,
help="SQuAD-format json file for evaluation.")
parser.add_argument(
"--max_seq_length",
default=384,
type=int,
help=
"The maximum total input sequence length after WordPiece tokenization. Sequences "
"longer than this will be truncated, and sequences shorter than this will be padded."
)
parser.add_argument(
"--doc_stride",
default=128,
type=int,
help=
"When splitting up a long document into chunks, how much stride to take between chunks."
)
parser.add_argument(
"--max_query_length",
default=64,
type=int,
help=
"The maximum number of tokens for the question. Questions longer than this will "
"be truncated to this length.")
parser.add_argument("--do_train",
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_predict",
action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument("--do_label", action='store_true')
parser.add_argument("--train_batch_size",
default=32,
type=int,
help="Total batch size for training.")
parser.add_argument("--predict_batch_size",
default=8,
type=int,
help="Total batch size for predictions.")
parser.add_argument("--learning_rate",
default=5e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--num_train_epochs",
default=3.0,
type=float,
help="Total number of training epochs to perform.")
parser.add_argument(
"--warmup_proportion",
default=0.1,
type=float,
help=
"Proportion of training to perform linear learning rate warmup for. E.g., 0.1 = 10%% "
"of training.")
parser.add_argument(
"--n_best_size",
default=20,
type=int,
help=
"The total number of n-best predictions to generate in the nbest_predictions.json "
"output file.")
parser.add_argument(
"--max_answer_length",
default=30,
type=int,
help=
"The maximum length of an answer that can be generated. This is needed because the start "
"and end predictions are not conditioned on one another.")
parser.add_argument(
"--verbose_logging",
action='store_true',
help=
"If true, all of the warnings related to data processing will be printed. "
"A number of warnings are expected for a normal SQuAD evaluation.")
parser.add_argument("--no_cuda",
action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument('--seed',
type=int,
default=42,
help="random seed for initialization")
parser.add_argument(
'--gradient_accumulation_steps',
type=int,
default=1,
help=
"Number of updates steps to accumulate before performing a backward/update pass."
)
parser.add_argument(
"--do_lower_case",
default=False,
action='store_true',
help=
"Whether to lower case the input text. True for uncased models, False for cased models."
)
parser.add_argument("--local_rank",
type=int,
default=-1,
help="local_rank for distributed training on gpus")
parser.add_argument(
'--fp16',
action='store_true',
help="Whether to use 16-bit float precision instead of 32-bit")
parser.add_argument('--fp16_opt_level', default='O1', type=str)
parser.add_argument(
'--loss_scale',
type=float,
default=0,
help=
"Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
"0 (default value): dynamic loss scaling.\n"
"Positive power of 2: static loss scaling value.\n")
parser.add_argument(
'--version_2_with_negative',
action='store_true',
help=
'If true, the SQuAD examples contain some that do not have an answer.')
parser.add_argument(
'--null_score_diff_threshold',
type=float,
default=0.0,
help=
"If null_score - best_non_null is greater than the threshold predict null."
)
parser.add_argument('--server_ip',
type=str,
default='',
help="Can be used for distant debugging.")
parser.add_argument('--server_port',
type=str,
default='',
help="Can be used for distant debugging.")
parser.add_argument(
'--no_masking',
action='store_true',
help='If true, we do not mask the span loss for no-answer examples.')
parser.add_argument(
'--skip_negatives',
action='store_true',
help=
'If true, we skip negative examples during training; this is mainly for ablation.'
)
# For Natural Questions
parser.add_argument(
'--max_answer_len',
type=int,
default=1000000,
help=
"maximum length of answer tokens (might be set to 5 for Natural Questions!)"
)
# balance the two losses.
parser.add_argument(
'--lambda_scale',
type=float,
default=1.0,
help=
"If you would like to change the two losses, please change the lambda scale."
)
# Save checkpoints more
parser.add_argument(
'--save_gran',
type=str,
default="10,3",
help='"10,5" means saving a checkpoint every 1/10 of the total updates,'
'but start saving from the 5th attempt')
parser.add_argument('--oss_cache_dir', default=None, type=str)
parser.add_argument('--cache_dir', default=None, type=str)
parser.add_argument('--dist', default=False, action='store_true')
args = parser.parse_args()
print(args)
if args.dist:
dist.init_process_group(backend='nccl')
print(f"local rank: {args.local_rank}")
print(f"global rank: {dist.get_rank()}")
print(f"world size: {dist.get_world_size()}")
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available()
and not args.no_cuda else "cpu")
n_gpu = torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
n_gpu = 1
# Initializes the distributed backend which will take care of
# synchronizing nodes/GPUs
dist.init_process_group(backend='nccl')
if args.dist:
global_rank = dist.get_rank()
world_size = dist.get_world_size()
if world_size > 1:
args.local_rank = global_rank
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO)
logger.info(
"device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".
format(device, n_gpu, bool(args.local_rank != -1), args.fp16))
if args.gradient_accumulation_steps < 1:
raise ValueError(
"Invalid gradient_accumulation_steps parameter: {}, should be >= 1"
.format(args.gradient_accumulation_steps))
args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
if not args.do_train and not args.do_predict:
raise ValueError(
"At least one of `do_train` or `do_predict` must be True.")
if args.do_train:
if not args.train_file:
raise ValueError(
"If `do_train` is True, then `train_file` must be specified.")
if args.do_predict:
if not args.predict_file:
raise ValueError(
"If `do_predict` is True, then `predict_file` must be specified."
)
if os.path.exists(args.output_dir) and os.listdir(
args.output_dir) and args.do_train:
raise ValueError(
"Output directory () already exists and is not empty.")
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
# Prepare model and tokenizer
tokenizer = AutoTokenizer.from_pretrained(args.bert_model,
do_lower_case=args.do_lower_case)
model = IterBertForQuestionAnsweringConfidence.from_pretrained(
args.bert_model,
num_labels=4,
no_masking=args.no_masking,
lambda_scale=args.lambda_scale)
model.to(device)
train_examples = None
train_features = None
num_train_optimization_steps = None
if args.do_train:
cached_train_features_file = args.train_file + '_{0}_{1}_{2}_{3}_{4}'.format(
model.base_model_prefix, str(args.max_seq_length),
str(args.doc_stride), str(args.max_query_length),
tokenizer.do_lower_case)
cached_train_features_file_name = cached_train_features_file.split(
'/')[-1]
_oss_feature_save_path = os.path.join(oss_features_cache_dir,
cached_train_features_file_name)
try:
if args.cache_dir is not None and os.path.exists(
os.path.join(args.cache_dir,
cached_train_features_file_name)):
logger.info(
f"Loading pre-processed features from {os.path.join(args.cache_dir, cached_train_features_file_name)}"
)
train_features = torch.load(
os.path.join(args.cache_dir,
cached_train_features_file_name))
else:
logger.info(
f"Loading pre-processed features from oss: {_oss_feature_save_path}"
)
train_features = torch.load(
load_buffer_from_oss(_oss_feature_save_path))
except:
train_examples = read_squad_examples(
input_file=args.train_file,
is_training=True,
version_2_with_negative=args.version_2_with_negative,
max_answer_len=args.max_answer_len,
skip_negatives=args.skip_negatives)
train_features = convert_examples_to_features_yes_no(
examples=train_examples,
tokenizer=tokenizer,
max_seq_length=args.max_seq_length,
doc_stride=args.doc_stride,
max_query_length=args.max_query_length,
is_training=True)
if args.local_rank in [-1, 0]:
torch_save_to_oss(train_features, _oss_feature_save_path)
logger.info(
f"Saving train features into oss: {_oss_feature_save_path}"
)
num_train_optimization_steps = int(
len(train_features) / args.train_batch_size /
args.gradient_accumulation_steps) * args.num_train_epochs
if args.do_label:
logger.info("finished.")
return
if args.do_train:
# Prepare optimizer
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight', 'layer_norm']
optimizer_grouped_parameters = [{
'params': [
p for n, p in param_optimizer
if not any(nd in n for nd in no_decay)
],
'weight_decay':
0.01
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay':
0.0
}]
t_total = num_train_optimization_steps
if args.local_rank != -1:
t_total = t_total // dist.get_world_size()
optimizer = AdamW(optimizer_grouped_parameters,
lr=args.learning_rate,
betas=eval(args.adam_betas),
eps=args.adam_epsilon)
scheduler = get_linear_schedule_with_warmup(
optimizer, int(t_total * args.warmup_proportion),
num_train_optimization_steps)
if args.fp16:
from apex import amp
if args.fp16_opt_level == 'O1':
amp.register_half_function(torch, "einsum")
if args.loss_scale == 0:
model, optimizer = amp.initialize(
model, optimizer, opt_level=args.fp16_opt_level)
else:
model, optimizer = amp.initialize(
model,
optimizer,
opt_level=args.fp16_opt_level,
loss_scale=args.loss_scale)
if args.local_rank != -1:
if args.fp16_opt_level == 'O2':
try:
import apex
model = apex.parallel.DistributedDataParallel(
model, delay_allreduce=True)
except ImportError:
model = torch.nn.parallel.DistributedDataParallel(
model, find_unused_parameters=True)
else:
model = torch.nn.parallel.DistributedDataParallel(
model, find_unused_parameters=True)
if n_gpu > 1:
model = torch.nn.DataParallel(model)
global_step = 0
logger.info("***** Running training *****")
if train_examples:
logger.info(" Num orig examples = %d", len(train_examples))
logger.info(" Num split examples = %d", len(train_features))
logger.info(" Instantaneous batch size per GPU = %d",
args.train_batch_size)
logger.info(
" Total train batch size (w. parallel, distributed & accumulation) = %d",
args.train_batch_size * args.gradient_accumulation_steps *
(dist.get_world_size() if args.local_rank != -1 else 1),
)
logger.info(" Gradient Accumulation steps = %d",
args.gradient_accumulation_steps)
logger.info(" Total optimization steps = %d", t_total)
all_input_ids = torch.tensor([f.input_ids for f in train_features],
dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in train_features],
dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in train_features],
dtype=torch.long)
all_start_positions = torch.tensor(
[f.start_position for f in train_features], dtype=torch.long)
all_end_positions = torch.tensor(
[f.end_position for f in train_features], dtype=torch.long)
all_switches = torch.tensor([f.switch for f in train_features],
dtype=torch.long)
train_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_start_positions,
all_end_positions, all_switches)
if args.local_rank == -1:
train_sampler = RandomSampler(train_data)
else:
train_sampler = DistributedSampler(train_data)
train_dataloader = DataLoader(train_data,
sampler=train_sampler,
batch_size=args.train_batch_size,
pin_memory=True,
num_workers=4)
if args.save_gran is not None:
save_chunk, save_start = args.save_gran.split(',')
save_chunk = t_total // int(save_chunk)
save_start = int(save_start)
model.train()
tr_loss = 0
for _epc in trange(int(args.num_train_epochs), desc="Epoch"):
if args.local_rank != -1:
train_dataloader.sampler.set_epoch(_epc)
for step, batch in enumerate(
tqdm(train_dataloader,
desc="Iteration",
disable=args.local_rank not in [-1, 0])):
if n_gpu == 1:
# multi-gpu does scattering it-self
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, start_positions, end_positions, switches = batch
loss = model(input_ids=input_ids,
token_type_ids=segment_ids,
attention_mask=input_mask,
start_positions=start_positions,
end_positions=end_positions,
switch_list=switches)
if n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.fp16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
tr_loss += loss.item()
if (step + 1) % args.gradient_accumulation_steps == 0:
optimizer.step()
scheduler.step()
optimizer.zero_grad()
global_step += 1
if global_step % 50 == 0:
logger.info(f"Training loss: {tr_loss / global_step}\t"
f"Learning rate: {scheduler.get_lr()[0]}\t"
f"Global step: {global_step}")
if args.save_gran is not None and args.local_rank in [
-1, 0
]:
if (global_step % save_chunk == 0) and (
global_step // save_chunk >= save_start):
logger.info('Saving a checkpoint....')
output_dir_per_epoch = os.path.join(
args.output_dir,
str(global_step) + 'steps')
os.makedirs(output_dir_per_epoch)
# Save a trained model, configuration and tokenizer
model_to_save = model.module if hasattr(
model, 'module'
) else model # Only save the model it-self
if args.oss_cache_dir is not None:
_oss_model_save_path = os.path.join(
args.oss_cache_dir, f"{global_step}steps")
torch_save_to_oss(
model_to_save.state_dict(),
_oss_model_save_path +
"/pytorch_model.bin")
model_to_save.save_pretrained(output_dir_per_epoch)
tokenizer.save_pretrained(output_dir_per_epoch)
logger.info('Done')
if args.do_train and (args.local_rank == -1 or dist.get_rank() == 0):
# Save a trained model, configuration and tokenizer
model_to_save = model.module if hasattr(
model, 'module') else model # Only save the model it-self
model_to_save.save_pretrained(args.output_dir)
tokenizer.save_pretrained(args.output_dir)
torch_save_to_oss(
model_to_save.state_dict(),
os.path.join(args.oss_cache_dir, "pytorch_model.bin"))
# Load a trained model and vocabulary that you have fine-tuned
# model = IterBertForQuestionAnsweringConfidence.from_pretrained(
# args.output_dir, num_labels=4, no_masking=args.no_masking)
tokenizer = AutoTokenizer.from_pretrained(
args.output_dir, do_lower_case=args.do_lower_case)
if args.do_train is False and args.do_predict is True:
model = IterBertForQuestionAnsweringConfidence.from_pretrained(
args.output_dir, num_labels=4, no_masking=args.no_masking)
tokenizer = AutoTokenizer.from_pretrained(
args.output_dir, do_lower_case=args.do_lower_case)
elif args.do_train is True and args.do_predict is True:
model = IterBertForQuestionAnsweringConfidence.from_pretrained(
args.output_dir, num_labels=4, no_masking=args.no_masking)
tokenizer = AutoTokenizer.from_pretrained(
args.output_dir, do_lower_case=args.do_lower_case)
else:
model = IterBertForQuestionAnsweringConfidence.from_pretrained(
args.bert_model,
num_labels=4,
no_masking=args.no_masking,
lambda_scale=args.lambda_scale)
model.to(device)
if args.do_predict and (args.local_rank == -1 or dist.get_rank() == 0):
eval_examples = read_squad_examples(
input_file=args.predict_file,
is_training=False,
version_2_with_negative=args.version_2_with_negative,
max_answer_len=args.max_answer_length,
skip_negatives=args.skip_negatives)
eval_features = convert_examples_to_features_yes_no(
examples=eval_examples,
tokenizer=tokenizer,
max_seq_length=args.max_seq_length,
doc_stride=args.doc_stride,
max_query_length=args.max_query_length,
is_training=False)
logger.info("***** Running predictions *****")
logger.info(" Num orig examples = %d", len(eval_examples))
logger.info(" Num split examples = %d", len(eval_features))
logger.info(" Batch size = %d", args.predict_batch_size)
all_input_ids = torch.tensor([f.input_ids for f in eval_features],
dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in eval_features],
dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in eval_features],
dtype=torch.long)
all_example_index = torch.arange(all_input_ids.size(0),
dtype=torch.long)
eval_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_example_index)
# Run prediction for full data
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data,
sampler=eval_sampler,
batch_size=args.predict_batch_size)
model.eval()
all_results = []
logger.info("Start evaluating")
for input_ids, input_mask, segment_ids, example_indices in tqdm(
eval_dataloader,
desc="Evaluating",
disable=args.local_rank not in [-1, 0]):
if len(all_results) % 1000 == 0:
logger.info("Processing example: %d" % (len(all_results)))
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
with torch.no_grad():
batch_start_logits, batch_end_logits, batch_switch_logits = model(
input_ids, segment_ids, input_mask)
for i, example_index in enumerate(example_indices):
start_logits = batch_start_logits[i].detach().cpu().tolist()
end_logits = batch_end_logits[i].detach().cpu().tolist()
switch_logits = batch_switch_logits[i].detach().cpu().tolist()
eval_feature = eval_features[example_index.item()]
unique_id = int(eval_feature.unique_id)
all_results.append(
RawResult(unique_id=unique_id,
start_logits=start_logits,
end_logits=end_logits,
switch_logits=switch_logits))
output_prediction_file = os.path.join(args.output_dir,
"predictions.json")
output_nbest_file = os.path.join(args.output_dir,
"nbest_predictions.json")
output_null_log_odds_file = os.path.join(args.output_dir,
"null_odds.json")
write_predictions_yes_no_no_empty_answer(
eval_examples, eval_features, all_results, args.n_best_size,
args.max_answer_length, args.do_lower_case, output_prediction_file,
output_nbest_file, output_null_log_odds_file, args.verbose_logging,
args.version_2_with_negative, args.null_score_diff_threshold,
args.no_masking)
