def create_and_check_for_token_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = BertForTokenClassification(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_bert_for_token_classification(
self, config, input_ids, token_type_ids, input_mask,
sequence_labels, token_labels, choice_labels):
config.num_labels = self.num_labels
model = BertForTokenClassification(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
labels=token_labels)
self.parent.assertListEqual(
list(result["logits"].size()),
[self.batch_size, self.seq_length, self.num_labels])
self.check_loss_output(result)
params.seed = args.seed
test_sentences = load_test_sentences(args.bert_model_dir, args.test_file)
# Specify the test set size
params.test_size = len(test_sentences)
params.eval_steps = params.test_size // params.batch_size
# Define the model
config_path = os.path.join(args.bert_model_dir, 'config.json')
config = BertConfig.from_json_file(config_path)
#update config with num_labels
config.update({"num_labels": 2})
model = BertForTokenClassification(config)
#model = BertForTokenClassification(config, num_labels=2)
model.to(params.device)
# Reload weights from the saved file
utils.load_checkpoint(
os.path.join(args.model_dir, args.restore_file + '.pth.tar'), model)
if args.fp16:
model.half()
if params.n_gpu > 1 and args.multi_gpu:
model = torch.nn.DataParallel(model)
predict(model=model,
data_iterator=yield_data_batch(test_sentences, params),
params=params,
sentences_file=args.test_file)
def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument(
"--data_dir",
default=None,
type=str,
required=True,
help=
"The input data dir. Should contain the training files for the CoNLL-2003 NER task."
)
parser.add_argument("--model_type",
default=None,
type=str,
required=True,
help="Model type selected in the list: " +
", ".join(MODEL_CLASSES.keys()))
parser.add_argument(
"--model_name_or_path",
default=None,
type=str,
required=True,
help="Path to pre-trained model or shortcut name selected in the list: "
+ ", ".join(ALL_MODELS))
# parser.add_argument("--output_dir", default=None, type=str, required=True,
# help="The output directory where the model predictions and checkpoints will be written.")
## Other parameters
parser.add_argument(
"--labels",
default="",
type=str,
help=
"Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."
)
parser.add_argument(
"--config_name",
default="",
type=str,
help="Pretrained config name or path if not the same as model_name")
parser.add_argument(
"--tokenizer_name",
default="",
type=str,
help="Pretrained tokenizer name or path if not the same as model_name")
parser.add_argument(
"--cache_dir",
default="",
type=str,
help=
"Where do you want to store the pre-trained models downloaded from s3")
parser.add_argument(
"--max_seq_length",
default=128,
type=int,
help=
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.")
parser.add_argument("--do_train",
action="store_true",
help="Whether to run training.")
parser.add_argument("--do_eval",
action="store_true",
help="Whether to run eval on the dev set.")
parser.add_argument("--do_predict",
action="store_true",
help="Whether to run predictions on the test set.")
parser.add_argument(
"--evaluate_during_training",
action="store_true",
help="Whether to run evaluation during training at each logging step.")
parser.add_argument(
"--test_during_training",
action="store_true",
help="Whether to run test during training at each logging step.")
parser.add_argument(
"--do_lower_case",
action="store_true",
help="Set this flag if you are using an uncased model.")
parser.add_argument("--per_gpu_train_batch_size",
default=8,
type=int,
help="Batch size per GPU/CPU for training.")
parser.add_argument("--per_gpu_eval_batch_size",
default=8,
type=int,
help="Batch size per GPU/CPU for evaluation.")
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("--learning_rate",
default=3e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--weight_decay",
default=0.0,
type=float,
help="Weight decay if we apply some.")
parser.add_argument("--adam_epsilon",
default=1e-8,
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("--num_train_epochs",
default=3.0,
type=float,
help="Total number of training epochs to perform.")
parser.add_argument(
"--max_steps",
default=-1,
type=int,
help=
"If > 0: set total number of training steps to perform. Override num_train_epochs."
)
parser.add_argument("--warmup_steps",
default=0,
type=int,
help="Linear warmup over warmup_steps.")
parser.add_argument(
"--warmup_proportion",
default=0.1,
type=float,
help="Ratio of linear warmup steps over all training steps")
parser.add_argument("--logging_steps",
type=int,
default=50,
help="Log every X updates steps.")
parser.add_argument("--save_steps",
type=int,
default=50,
help="Save checkpoint every X updates steps.")
parser.add_argument(
"--eval_all_checkpoints",
action="store_true",
help=
"Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number"
)
parser.add_argument(
"--test_all_checkpoints",
action="store_true",
help=
"Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number"
)
parser.add_argument("--no_cuda",
action="store_true",
help="Avoid using CUDA when available")
parser.add_argument("--overwrite_output_dir",
action="store_true",
help="Overwrite the content of the output directory")
parser.add_argument(
"--overwrite_cache",
action="store_true",
help="Overwrite the cached training and evaluation sets")
parser.add_argument("--seed",
type=int,
default=42,
help="random seed for initialization")
parser.add_argument(
"--fp16",
action="store_true",
help=
"Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"
)
parser.add_argument(
"--fp16_opt_level",
type=str,
default="O1",
help=
"For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
"See details at https://nvidia.github.io/apex/amp.html")
parser.add_argument("--local_rank",
type=int,
default=-1,
help="For distributed training: local_rank")
parser.add_argument("--server_ip",
type=str,
default="",
help="For distant debugging.")
parser.add_argument("--server_port",
type=str,
default="",
help="For distant debugging.")
parser.add_argument("--random_start", action="store_true")
parser.add_argument("--yago_reference", action="store_true")
parser.add_argument("--max_reference_num",
type=int,
default=10,
help="Number of yago types considered as references")
parser.add_argument("--additional_output_tag",
type=str,
default="",
help="Additional tag to distinguish from other models")
parser.add_argument(
"--do_significant_check",
action="store_true",
help=
"Whether to check if the influence of reference embedding is significant"
)
args = parser.parse_args()
DEFAULT_DATA_REPO = '/work/smt2/qfeng/Project/huggingface/datasets/'
DEFAULT_CACHE_REPO = '/work/smt2/qfeng/Project/huggingface/pretrain/'
DEFAULT_OUTPUT_REPO = '/work/smt2/qfeng/Project/huggingface/models/'
if '/' not in args.data_dir:
args.data_dir = DEFAULT_DATA_REPO + args.data_dir
if '/' not in args.cache_dir:
if args.cache_dir == "":
args.cache_dir = DEFAULT_CACHE_REPO + args.model_name_or_path[
len('bert-'):]
else:
args.cache_dir = DEFAULT_CACHE_REPO + args.cache_dir
if args.labels == "":
if os.path.exists(os.path.join(args.data_dir, 'labels.txt')):
args.labels = os.path.join(args.data_dir, 'labels.txt')
else:
raise ValueError("Invalid or missing labels file!")
if '-uncased' in args.model_name_or_path or '_uncased' in args.model_name_or_path:
args.do_lower_case = True
elif '-cased' in args.model_name_or_path or '_cased' in args.model_name_or_path:
args.do_lower_case = False
# name the output diretory according to the used model, time tag, usage of yago reference
output_dir = args.model_name_or_path.split('/')[-1]
if args.yago_reference:
output_dir += "_yagoref"
if args.additional_output_tag != "":
output_dir += '_' + args.additional_output_tag
else:
now_time = datetime.datetime.now()
output_dir += '_' + '-'.join(
str(i) for i in list(now_time.timetuple()[1:3])) # 'month-date'
args.output_dir = DEFAULT_OUTPUT_REPO + output_dir
logger.info("output model to file {}".format(output_dir))
if args.tokenizer_name == "":
args.tokenizer_name = 'bert-base-uncased' if args.do_lower_case else 'bert-base-cased'
if args.yago_reference:
REFERENCE_SIZE = 959
# if args.yago_reference:
# with open('/work/smt3/wwang/TAC2019/qihui_data/yago/YagoReference.pickle', 'rb') as ref_pickle: #TODO:
# ref_dict = pickle.load(ref_pickle)
if os.path.exists(args.output_dir) and os.listdir(
args.output_dir
) and args.do_train 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))
# Setup distant debugging if needed
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach")
ptvsd.enable_attach(address=(args.server_ip, args.server_port),
redirect_output=True)
ptvsd.wait_for_attach()
# Setup CUDA, GPU & distributed training
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")
args.n_gpu = torch.cuda.device_count()
else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
torch.distributed.init_process_group(backend="nccl")
args.n_gpu = 1
args.device = device
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
args.local_rank, device, args.n_gpu, bool(args.local_rank != -1),
args.fp16)
args.logging_steps = int(args.logging_steps / args.n_gpu)
args.save_steps = int(args.save_steps / args.n_gpu)
# Set seed
set_seed(args)
# Prepare CONLL-2003 task
labels = get_labels(args.labels)
num_labels = len(labels)
# Use cross entropy ignore index as padding label id so that only real label ids contribute to the loss later
pad_token_label_id = CrossEntropyLoss().ignore_index
# Load pretrained model and tokenizer
if args.local_rank not in [-1, 0]:
torch.distributed.barrier(
) # Make sure only the first process in distributed training will download model & vocab
args.model_type = args.model_type.lower()
config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
# bertconfig = config_class.from_pretrained(args.config_name if args.config_name else args.model_name_or_path,
# num_labels=num_labels,
# cache_dir=args.cache_dir if args.cache_dir else None)
tokenizer = tokenizer_class.from_pretrained(
args.tokenizer_name
if args.tokenizer_name else args.model_name_or_path,
do_lower_case=args.do_lower_case,
cache_dir=args.cache_dir if args.cache_dir else None)
if not args.yago_reference:
config = config_class.from_pretrained(
args.config_name if args.config_name else args.model_name_or_path,
num_labels=num_labels,
cache_dir=args.cache_dir if args.cache_dir else None)
"""
Test the ablation of pretrained model
"""
if args.random_start:
model = BertForTokenClassification(config)
else:
model = model_class.from_pretrained(
args.model_name_or_path,
from_tf=bool(".ckpt" in args.model_name_or_path),
config=config,
cache_dir=args.cache_dir if args.cache_dir else None)
else:
# config = YagoRefBertConfig(bertconfig.__dict__, reference_size=REFERENCE_SIZE,
# num_labels=num_labels,
# cache_dir=args.cache_dir if args.cache_dir else None
# )
config = YagoRefBertConfig.from_pretrained(
args.config_name if args.config_name else args.model_name_or_path,
reference_size=REFERENCE_SIZE,
num_labels=num_labels,
cache_dir=args.cache_dir if args.cache_dir else None)
logger.info("number of labels %d", config.num_labels)
logger.info("vocab size: %d", config.vocab_size)
model = YagoRefBertForTokenClassification.from_pretrained(
args.model_name_or_path,
from_tf=bool(".ckpt" in args.model_name_or_path),
config=config,
cache_dir=args.cache_dir if args.cache_dir else None)
# model = model_class.from_pretrained(args.model_name_or_path,
# from_tf=bool(".ckpt" in args.model_name_or_path),
# config=config,
# cache_dir=args.cache_dir if args.cache_dir else None)
if args.local_rank == 0:
torch.distributed.barrier(
) # Make sure only the first process in distributed training will download model & vocab
model.to(args.device)
logger.info("Training/evaluation parameters %s", args)
if args.overwrite_cache:
load_and_cache_examples(args,
tokenizer,
labels,
pad_token_label_id,
mode="train")
load_and_cache_examples(args,
tokenizer,
labels,
pad_token_label_id,
mode="dev")
load_and_cache_examples(args,
tokenizer,
labels,
pad_token_label_id,
mode="test")
args.overwrite_cache = False
# Training
if args.do_train:
train_dataset = load_and_cache_examples(args,
tokenizer,
labels,
pad_token_label_id,
mode="train")
global_step, tr_loss = train(args, train_dataset, model, tokenizer,
labels, pad_token_label_id)
logger.info(" global_step = %s, average loss = %s", global_step,
tr_loss)
# Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
if args.do_train and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
# Create output directory if needed
if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
os.makedirs(args.output_dir)
logger.info("Saving model checkpoint to %s", args.output_dir)
# Save a trained model, configuration and tokenizer using `save_pretrained()`.
# They can then be reloaded using `from_pretrained()`
model_to_save = model.module if hasattr(
model,
"module") else model # Take care of distributed/parallel training
model_to_save.save_pretrained(args.output_dir)
tokenizer.save_pretrained(args.output_dir)
# Good practice: save your training arguments together with the trained model
torch.save(args, os.path.join(args.output_dir, "training_args.bin"))
# Evaluation
results = {}
if args.do_eval and args.local_rank in [-1, 0]:
# tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
checkpoints = [args.output_dir]
if args.eval_all_checkpoints:
checkpoints = list(
os.path.dirname(c) for c in sorted(
glob.glob(args.output_dir + "/**/" + WEIGHTS_NAME,
recursive=True)))
logging.getLogger("pytorch_transformers.modeling_utils").setLevel(
logging.WARN) # Reduce logging
logger.info("Evaluate the following checkpoints: %s", checkpoints)
for checkpoint in checkpoints:
global_step = checkpoint.split("-")[-1] if len(
checkpoint.split("-")[-1]) > 2 else checkpoint
if args.yago_reference:
model = YagoRefBertForTokenClassification.from_pretrained(
checkpoint)
else:
model = model_class.from_pretrained(checkpoint)
model.to(args.device)
result, _ = evaluate(args,
model,
tokenizer,
labels,
pad_token_label_id,
mode="dev",
prefix=global_step)
if global_step:
result = {
"{}_{}".format(global_step, k): v
for k, v in result.items()
}
results.update(result)
output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
with open(output_eval_file, "w") as writer:
for key in sorted(results.keys()):
writer.write("{} = {}\n".format(key, str(results[key])))
if args.do_predict and args.local_rank in [-1, 0]:
# tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
checkpoints = [args.output_dir]
if args.test_all_checkpoints:
checkpoints = list(
os.path.dirname(c) for c in sorted(
glob.glob(args.output_dir + "/**/" + WEIGHTS_NAME,
recursive=True)))
logging.getLogger("pytorch_transformers.modeling_utils").setLevel(
logging.WARN) # Reduce logging
logger.info("Test the following checkpoints: %s", checkpoints)
for checkpoint in checkpoints:
global_step = checkpoint.split("-")[-1] if len(
checkpoint.split("-")[-1]) > 2 else checkpoint
if args.yago_reference:
model = YagoRefBertForTokenClassification.from_pretrained(
checkpoint)
else:
model = model_class.from_pretrained(checkpoint)
model.to(args.device)
result, _ = evaluate(args,
model,
tokenizer,
labels,
pad_token_label_id,
mode="test",
prefix=global_step)
if global_step:
result = {
"{}_{}".format(global_step, k): v
for k, v in result.items()
}
results.update(result)
output_eval_file = os.path.join(args.output_dir, "test_results.txt")
with open(output_eval_file, "w") as writer:
for key in sorted(results.keys()):
writer.write("{} = {}\n".format(key, str(results[key])))
if args.yago_reference:
model = YagoRefBertForTokenClassification.from_pretrained(
args.output_dir, config=config)
else:
model = model_class.from_pretrained(args.output_dir)
model.to(args.device)
result, predictions = evaluate(args,
model,
tokenizer,
labels,
pad_token_label_id,
mode="test")
# Save predictions
output_test_predictions_file = os.path.join(args.output_dir,
"test_predictions.txt")
with open(output_test_predictions_file, "w") as writer:
with open(os.path.join(args.data_dir, "test.txt"), "r") as f:
example_id = 0
for line in f:
if line.startswith(
"-DOCSTART-") or line == "" or line == "\n":
writer.write(line)
if not predictions[example_id]:
example_id += 1
elif predictions[example_id]:
output_line = line.split(
)[0] + " " + predictions[example_id].pop(0) + "\n"
writer.write(output_line)
else:
logger.warning(
"Maximum sequence length exceeded: No prediction for '%s'.",
line.split()[0])
# Temporary code: check whether the values of reference_embedding are significant
if args.do_significant_check and args.yago_reference:
model = YagoRefBertForTokenClassification.from_pretrained(
args.output_dir, config=config)
bertconfig = config_class.from_pretrained(
"/work/smt2/qfeng/Project/huggingface/models/base-cased_1-9/",
num_labels=num_labels,
cache_dir=args.cache_dir if args.cache_dir else None)
model_noyago = BertForTokenClassification.from_pretrained(
"/work/smt2/qfeng/Project/huggingface/models/base-cased_1-9/",
from_tf=bool(".ckpt" in args.model_name_or_path),
config=bertconfig,
cache_dir=args.cache_dir if args.cache_dir else None)
model.to(args.device)
model.eval()
model_noyago.to(args.device)
model_noyago.eval()
# logger.info(model.bert.embeddings.word_embeddings.weight.size())
with open(
'/work/smt3/wwang/TAC2019/qihui_data/yago/YagoReference_prune{}.pickle'
.format("" if args.do_lower_case else "_cased"),
'rb') as ref_pickle: #TODO:
ref_dict = pickle.load(ref_pickle)
cos_sim = []
cos_sim_ww = []
ref_vec_norm = []
cos = torch.nn.CosineSimilarity(dim=0, eps=1e-6)
for id in range(config.vocab_size):
if id in ref_dict:
reference_ids = model.bert.embeddings.ref_ids[id].to(device)
reference_weights = model.bert.embeddings.ref_weights[id].to(
device)
# logger.info(reference_ids.size())
# logger.info(reference_weights.size())
reference_embedding = torch.sum(
model.bert.reference_embeddings(reference_ids) *
torch.unsqueeze(reference_weights, dim=-1),
dim=-2)
word_embedding = model.bert.embeddings.word_embeddings(
torch.tensor(id, dtype=torch.long, device=args.device))
# ref_id_list = torch.tensor(list(ref_dict[id].keys()), dtype=torch.long, device=args.device)
# ref_id_weight = torch.tensor(list(ref_dict[id].values()), dtype=torch.float, device=args.device)
# reference_embeddings = model.bert.embeddings.reference_embeddings(ref_id_list)*torch.unsqueeze(ref_id_weight, dim=-1)
# # logger.info(reference_embeddings.size())
# reference_embedding = torch.sum(reference_embeddings,dim=-2)
vec_norm = torch.norm(reference_embedding)
ref_vec_norm.append(vec_norm)
noyago_word_embedding = model_noyago.bert.embeddings.word_embeddings(
torch.tensor(id, dtype=torch.long, device=args.device))
# logger.info(vec_norm/torch.norm(word_embedding))
cos_sim.append(cos(word_embedding, reference_embedding))
cos_sim_ww.append(cos(word_embedding, noyago_word_embedding))
logger.info(cos(word_embedding, reference_embedding))
assert (word_embedding.size() == reference_embedding.size())
# word_embedding_norm = torch.norm(model.bert.embeddings.word_embeddings.weight, p=2, dim=1)
# reference_embedding_norm = torch.norm(model.bert.embeddings.reference_embeddings.weight, p=2, dim=1)
avg_sim = sum(cos_sim) / len(cos_sim)
avg_sim_ww = sum(cos_sim_ww) / len(cos_sim_ww)
avg_ratio = sum(ref_vec_norm) / len(ref_vec_norm)
logger.info(avg_sim)
logger.info(avg_sim_ww)
logger.info(avg_ratio)
return results
genes, labels = read_non_split_file(
'/home/brian/Downloads/all_samples_6-mer_train.txt')
seq_ids, masks, labels = tokenize_and_pad_samples(genes, labels)
print(seq_ids[0])
print(len(seq_ids))
print("Finished making data")
batch_size = 1
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = BertForTokenClassification(
BertConfig.from_json_file(
'/home/brian/attentive_splice/bert_configuration_all_hex.json'))
model.resize_token_embeddings(4099)
model.to(device)
optimizer = Adam(model.parameters(), lr=1e-3) #lr=3e-5)
class_weights = torch.tensor(np.array([1.0, 165.0])).float().cuda()
loss = CrossEntropyLoss(weight=class_weights)
last_i = 0
def load_model_from_saved():
with open('/home/brian/bert_last_i.txt', 'r') as last_i_file:
i = last_i_file.read()
last_i = int(i)
model.load_state_dict(torch.load("/home/brian/bert_splice_weights.pt"))
def save_weights():
print("Saving weights")
class TorchBertSequenceTagger(TorchModel):
"""BERT-based model on PyTorch for text tagging. It predicts a label for every token (not subtoken) in the text.
You can use it for sequence labeling tasks, such as morphological tagging or named entity recognition.
Args:
n_tags: number of distinct tags
pretrained_bert: pretrained Bert checkpoint path or key title (e.g. "bert-base-uncased")
return_probas: set this to `True` if you need the probabilities instead of raw answers
bert_config_file: path to Bert configuration file, or None, if `pretrained_bert` is a string name
attention_probs_keep_prob: keep_prob for Bert self-attention layers
hidden_keep_prob: keep_prob for Bert hidden layers
optimizer: optimizer name from `torch.optim`
optimizer_parameters: dictionary with optimizer's parameters,
e.g. {'lr': 0.1, 'weight_decay': 0.001, 'momentum': 0.9}
learning_rate_drop_patience: how many validations with no improvements to wait
learning_rate_drop_div: the divider of the learning rate after `learning_rate_drop_patience` unsuccessful
validations
load_before_drop: whether to load best model before dropping learning rate or not
clip_norm: clip gradients by norm
min_learning_rate: min value of learning rate if learning rate decay is used
"""
def __init__(self,
n_tags: int,
pretrained_bert: str,
bert_config_file: Optional[str] = None,
return_probas: bool = False,
attention_probs_keep_prob: Optional[float] = None,
hidden_keep_prob: Optional[float] = None,
optimizer: str = "AdamW",
optimizer_parameters: dict = {"lr": 1e-3, "weight_decay": 1e-6},
learning_rate_drop_patience: int = 20,
learning_rate_drop_div: float = 2.0,
load_before_drop: bool = True,
clip_norm: Optional[float] = None,
min_learning_rate: float = 1e-07,
**kwargs) -> None:
self.n_classes = n_tags
self.return_probas = return_probas
self.attention_probs_keep_prob = attention_probs_keep_prob
self.hidden_keep_prob = hidden_keep_prob
self.clip_norm = clip_norm
self.pretrained_bert = pretrained_bert
self.bert_config_file = bert_config_file
super().__init__(optimizer=optimizer,
optimizer_parameters=optimizer_parameters,
learning_rate_drop_patience=learning_rate_drop_patience,
learning_rate_drop_div=learning_rate_drop_div,
load_before_drop=load_before_drop,
min_learning_rate=min_learning_rate,
**kwargs)
def train_on_batch(self,
input_ids: Union[List[List[int]], np.ndarray],
input_masks: Union[List[List[int]], np.ndarray],
y_masks: Union[List[List[int]], np.ndarray],
y: List[List[int]],
*args, **kwargs) -> Dict[str, float]:
"""
Args:
input_ids: batch of indices of subwords
input_masks: batch of masks which determine what should be attended
args: arguments passed to _build_feed_dict
and corresponding to additional input
and output tensors of the derived class.
kwargs: keyword arguments passed to _build_feed_dict
and corresponding to additional input
and output tensors of the derived class.
Returns:
dict with fields 'loss', 'head_learning_rate', and 'bert_learning_rate'
"""
b_input_ids = torch.from_numpy(input_ids).to(self.device)
b_input_masks = torch.from_numpy(input_masks).to(self.device)
subtoken_labels = [token_labels_to_subtoken_labels(y_el, y_mask, input_mask)
for y_el, y_mask, input_mask in zip(y, y_masks, input_masks)]
b_labels = torch.from_numpy(np.array(subtoken_labels)).to(torch.int64).to(self.device)
self.optimizer.zero_grad()
loss, logits = self.model(input_ids=b_input_ids, token_type_ids=None, attention_mask=b_input_masks,
labels=b_labels)
loss.backward()
# Clip the norm of the gradients to 1.0.
# This is to help prevent the "exploding gradients" problem.
if self.clip_norm:
torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.clip_norm)
self.optimizer.step()
if self.lr_scheduler is not None:
self.lr_scheduler.step()
return {'loss': loss.item()}
def __call__(self,
input_ids: Union[List[List[int]], np.ndarray],
input_masks: Union[List[List[int]], np.ndarray],
y_masks: Union[List[List[int]], np.ndarray]) -> Union[List[List[int]], List[np.ndarray]]:
""" Predicts tag indices for a given subword tokens batch
Args:
input_ids: indices of the subwords
input_masks: mask that determines where to attend and where not to
y_masks: mask which determines the first subword units in the the word
Returns:
Label indices or class probabilities for each token (not subtoken)
"""
b_input_ids = torch.from_numpy(input_ids).to(self.device)
b_input_masks = torch.from_numpy(input_masks).to(self.device)
with torch.no_grad():
# Forward pass, calculate logit predictions
logits = self.model(b_input_ids, token_type_ids=None, attention_mask=b_input_masks)
# Move logits and labels to CPU and to numpy arrays
logits = token_from_subtoken(logits[0].detach().cpu(), torch.from_numpy(y_masks))
if self.return_probas:
pred = torch.nn.functional.softmax(logits, dim=-1)
pred = pred.detach().cpu().numpy()
else:
logits = logits.detach().cpu().numpy()
pred = np.argmax(logits, axis=-1)
seq_lengths = np.sum(y_masks, axis=1)
pred = [p[:l] for l, p in zip(seq_lengths, pred)]
return pred
@overrides
def load(self, fname=None):
if fname is not None:
self.load_path = fname
if self.pretrained_bert and not Path(self.pretrained_bert).is_file():
self.model = BertForTokenClassification.from_pretrained(
self.pretrained_bert, num_labels=self.n_classes,
output_attentions=False, output_hidden_states=False)
elif self.bert_config_file and Path(self.bert_config_file).is_file():
self.bert_config = BertConfig.from_json_file(str(expand_path(self.bert_config_file)))
if self.attention_probs_keep_prob is not None:
self.bert_config.attention_probs_dropout_prob = 1.0 - self.attention_probs_keep_prob
if self.hidden_keep_prob is not None:
self.bert_config.hidden_dropout_prob = 1.0 - self.hidden_keep_prob
self.model = BertForTokenClassification(config=self.bert_config)
else:
raise ConfigError("No pre-trained BERT model is given.")
self.model.to(self.device)
self.optimizer = getattr(torch.optim, self.optimizer_name)(
self.model.parameters(), **self.optimizer_parameters)
if self.lr_scheduler_name is not None:
self.lr_scheduler = getattr(torch.optim.lr_scheduler, self.lr_scheduler_name)(
self.optimizer, **self.lr_scheduler_parameters)
if self.load_path:
log.info(f"Load path {self.load_path} is given.")
if isinstance(self.load_path, Path) and not self.load_path.parent.is_dir():
raise ConfigError("Provided load path is incorrect!")
weights_path = Path(self.load_path.resolve())
weights_path = weights_path.with_suffix(f".pth.tar")
if weights_path.exists():
log.info(f"Load path {weights_path} exists.")
log.info(f"Initializing `{self.__class__.__name__}` from saved.")
# now load the weights, optimizer from saved
log.info(f"Loading weights from {weights_path}.")
checkpoint = torch.load(weights_path, map_location=self.device)
self.model.load_state_dict(checkpoint["model_state_dict"])
self.optimizer.load_state_dict(checkpoint["optimizer_state_dict"])
self.epochs_done = checkpoint.get("epochs_done", 0)
else:
log.info(f"Init from scratch. Load path {weights_path} does not exist.")
