random.seed(args.seed)

np.random.seed(args.seed)

torch.manual_seed(args.seed)

if args.n_gpu > 0:

""" Train the model """

if args.local_rank in [-1, 0]:

tb_writer = SummaryWriter()

args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)

train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset)

train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size)

if args.max_steps > 0:

t_total = args.max_steps

args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) 1

else:

t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs

# Prepare optimizer and schedule (linear warmup and decay)

no_decay = ["bias", "LayerNorm.weight"]

if args.finetune_feature_extractor:

optimizer_grouped_parameters = [

{'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],

'weight_decay': args.weight_decay},

{'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}

]

else:

optimizer_grouped_parameters = [

{'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)

and args.model_type not in n],

'weight_decay': args.weight_decay},

{'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)

and args.model_type not in n],

'weight_decay': 0.0}

]

optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)

scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total)

if args.fp16:

try:

from apex import amp

except ImportError:

raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")

model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)

# multi-gpu training (should be after apex fp16 initialization)

if args.n_gpu > 1:

model = torch.nn.DataParallel(model)

# Distributed training (should be after apex fp16 initialization)

if args.local_rank != -1:

model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank],

output_device=args.local_rank,

find_unused_parameters=True)

# Train!

logger.info("***** Running training *****")

logger.info(" Num examples = %d", len(train_dataset))

logger.info(" Num Epochs = %d", args.num_train_epochs)

logger.info(" Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size)

logger.info(" Total train batch size (w. parallel, distributed & accumulation) = %d",

args.train_batch_size * args.gradient_accumulation_steps * (

torch.distributed.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)

global_step = 0

tr_loss, logging_loss = 0.0, 0.0

model.zero_grad()

train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0])

set_seed(args) # Added here for reproductibility (even between python 2 and 3)

epoch = 0

if args.local_rank in [-1, 0]:

# Save model checkpoint

if args.save == 'all' or args.save == str(epoch):

checkpoint_output_dir = os.path.join(args.output_dir, 'checkpoint-{}'.format(epoch))

if not os.path.exists(checkpoint_output_dir):

os.makedirs(checkpoint_output_dir)

model_to_save = model.module if hasattr(model,

'module') else model # Take care of distributed/parallel training

model_to_save.save_pretrained(checkpoint_output_dir)

torch.save(args, os.path.join(checkpoint_output_dir, 'training_args.bin'))

tokenizer.save_pretrained(checkpoint_output_dir)

logger.info("Saving model checkpoint to %s", checkpoint_output_dir)

for _ in train_iterator:

epoch = 1

epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])

for step, batch in enumerate(epoch_iterator):

model.train()

batch = tuple(t.to(args.device) for t in batch)

inputs = {"input_ids": batch[0],

"attention_mask": batch[1],

"token_type_ids": batch[2],

"labels": batch[3]}

outputs = model(**inputs)

loss = outputs[0] # model outputs are always tuple in pytorch-transformers (see doc)

if args.n_gpu > 1:

loss = loss.mean() # mean() to average on multi-gpu parallel training

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:

if args.fp16:

torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)

else:

torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)

scheduler.step() # Update learning rate schedule

optimizer.step()

model.zero_grad()

global_step = 1

if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:

# Log metrics

if args.local_rank == -1 and args.evaluate_during_training: # Only evaluate when single GPU otherwise metrics may not average well

results, _ = evaluate(args, model, tokenizer, labels, pad_token_label_id, mode="dev")

for key, value in results.items():

tb_writer.add_scalar("eval_{}".format(key), value, global_step)

tb_writer.add_scalar("lr", scheduler.get_lr()[0], global_step)

tb_writer.add_scalar("loss", (tr_loss - logging_loss) / args.logging_steps, global_step)

logging_loss = tr_loss

if args.max_steps > 0 and global_step > args.max_steps:

epoch_iterator.close()

break

if args.local_rank in [-1, 0]:

# Save model checkpoint

if args.save == 'all' or args.save == str(epoch):

checkpoint_output_dir = os.path.join(args.output_dir, 'checkpoint-{}'.format(epoch))

if not os.path.exists(checkpoint_output_dir):

os.makedirs(checkpoint_output_dir)

model_to_save = model.module if hasattr(model,

'module') else model # Take care of distributed/parallel training

model_to_save.save_pretrained(checkpoint_output_dir)

torch.save(args, os.path.join(checkpoint_output_dir, 'training_args.bin'))

tokenizer.save_pretrained(checkpoint_output_dir)

logger.info("Saving model checkpoint to %s", checkpoint_output_dir)

if args.max_steps > 0 and global_step > args.max_steps:

train_iterator.close()

break

if args.local_rank in [-1, 0]:

tb_writer.close()

eval_dataset = load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, mode=mode)

if args.eval_data_subset > 0:

eval_dataset = Subset(eval_dataset, list(range(min(args.eval_data_subset, len(eval_dataset)))))

args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)

# Note that DistributedSampler samples randomly

eval_sampler = SequentialSampler(eval_dataset) if args.local_rank == -1 else DistributedSampler(eval_dataset)

eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)

# Eval!

logger.info("***** Running evaluation %s *****", prefix)

logger.info(" Num examples = %d", len(eval_dataset))

logger.info(" Batch size = %d", args.eval_batch_size)

eval_loss = 0.0

nb_eval_steps = 0

preds = None

out_label_ids = None

model.eval()

for batch in tqdm(eval_dataloader, desc="Evaluating"):

batch = tuple(t.to(args.device) for t in batch)

with torch.no_grad():

inputs = {"input_ids": batch[0],

"attention_mask": batch[1],

"token_type_ids": batch[2],

"labels": batch[3]}

outputs = model(**inputs)

tmp_eval_loss, logits = outputs[:2]

if args.n_gpu > 1:

tmp_eval_loss = tmp_eval_loss.mean() # mean() to average on multi-gpu parallel evaluating

eval_loss = tmp_eval_loss.item()

nb_eval_steps = 1

if preds is None:

preds = logits.detach().cpu().numpy()

out_label_ids = inputs["labels"].detach().cpu().numpy()

else:

preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)

out_label_ids = np.append(out_label_ids, inputs["labels"].detach().cpu().numpy(), axis=0)

eval_loss = eval_loss / nb_eval_steps

preds = np.argmax(preds, axis=2)

label_map = {i: label for i, label in enumerate(labels)}

out_label_list = [[] for _ in range(out_label_ids.shape[0])]

preds_list = [[] for _ in range(out_label_ids.shape[0])]

for i in range(out_label_ids.shape[0]):

for j in range(out_label_ids.shape[1]):

if out_label_ids[i, j] != pad_token_label_id:

out_label_list[i].append(label_map[out_label_ids[i][j]])

preds_list[i].append(label_map[preds[i][j]])

results = {

"loss": eval_loss,

"accuracy": accuracy_score(out_label_list, preds_list),

"precision": precision_score(out_label_list, preds_list),

"recall": recall_score(out_label_list, preds_list),

"f1": f1_score(out_label_list, preds_list)

}

output_eval_file = os.path.join(args.output_dir, "eval_results_{}.txt".format(checkpoint_id) if checkpoint_id is not None

else "eval_results.txt"

)

with open(output_eval_file, "w") as writer:

logger.info("***** Eval results {} *****".format(prefix))

for key in sorted(results.keys()):

logger.info(" %s = %s", key, str(results[key]))

writer.write("%s = %s\n" % (key, str(results[key])))

if args.local_rank not in [-1, 0] and not evaluate:

torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache

# Load data features from cache or dataset file

cached_features_file = os.path.join(args.data_dir, "cached_{}_{}_{}".format(mode,

list(filter(None, args.model_name_or_path.split("/"))).pop(),

str(args.max_seq_length)))

if os.path.exists(cached_features_file) and not args.overwrite_cache:

logger.info("Loading features from cached file %s", cached_features_file)

features = torch.load(cached_features_file)

else:

logger.info("Creating features from dataset file at %s", args.data_dir)

examples = read_examples_from_file(args.data_dir, mode)

features = convert_examples_to_features(examples, labels, args.max_seq_length, tokenizer,

cls_token_at_end=False,

cls_token=tokenizer.cls_token,

cls_token_segment_id=0,

sep_token=tokenizer.sep_token,

sep_token_extra=False,

pad_on_left=False,

pad_token=tokenizer.convert_tokens_to_ids([tokenizer.pad_token])[0],

pad_token_segment_id=0,

pad_token_label_id=pad_token_label_id

)

if args.local_rank in [-1, 0]:

logger.info("Saving features into cached file %s", cached_features_file)

torch.save(features, cached_features_file)

if args.local_rank == 0 and not evaluate:

torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache

# Convert to Tensors and build dataset

all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)

all_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long)

all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long)

all_label_ids = torch.tensor([f.label_ids for f in features], dtype=torch.long)

dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)

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("--train_data_subset", type=int, default=-1,

help="If > 0: limit the training data to a subset of train_data_subset instances.")

parser.add_argument("--eval_data_subset", type=int, default=-1,

help="If > 0: limit the evaluation data to a subset of eval_data_subset instances.")

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("--do_lower_case", action="store_true",

help="Set this flag if you are using an uncased model.")

parser.add_argument("--finetune_feature_extractor", default=True, type=eval,

help="Whether to fine-tune the feature extractor.")

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=5e-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("--logging_steps", type=int, default=50,

help="Log 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("--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('--model_load_mode', type=str, default='all',

help="Select load mode from ['base_model_only', 'all']")

parser.add_argument('--save', type=str, default='all',

help="Select load mode from ['all', '0', '1', '2', '3', ...]")

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.")

args = parser.parse_args()

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))

# Create output directory if needed

if not os.path.exists(args.output_dir):

os.makedirs(args.output_dir)

with open(os.path.join(args.output_dir, 'run_args.txt'), 'w') as f:

f.write(json.dumps(args.__dict__, indent=2))

f.close()

# 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)

# 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

if args.model_load_mode not in ['base_model_only', 'all']:

raise ValueError("Model load mode not found: %s" % (args.model_load_mode))

state_dict_with_prefix = None

if args.model_load_mode == 'base_model_only':

archive_file = os.path.join(args.model_name_or_path, WEIGHTS_NAME)

model_state_dict = torch.load(archive_file)

state_dict_with_prefix = {}

for key, value in model_state_dict.items():

if key.startswith(args.model_type):

state_dict_with_prefix[key] = value

args.model_type = args.model_type.lower()

config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]

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)

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)

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,

state_dict=state_dict_with_prefix)

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)

# Good practice: save your training arguments together with the trained model

torch.save(args, os.path.join(args.output_dir, 'training_args.bin'))

tokenizer.save_pretrained(args.output_dir)

# Training

if args.do_train:

train_dataset = load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, mode="train")

if args.train_data_subset > 0:

train_dataset = Subset(train_dataset, list(range(min(args.train_data_subset, len(train_dataset)))))

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)

# Evaluation

results = {}

if args.do_eval and args.local_rank in [-1, 0]:

eval_list = []

if args.save == 'all':

eval_list = range(int(args.num_train_epochs 1))

elif str(args.save).isdigit():

eval_list = [int(args.save)]

if len(eval_list) > 0:

for epoch in eval_list:

checkpoints = [os.path.join(args.output_dir, 'checkpoint-{}'.format(epoch))]

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(checkpoints) > 1 else ""

prefix = checkpoint.split('/')[-1] if checkpoint.find('checkpoint') != -1 else ""

model = model_class.from_pretrained(checkpoint)

tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)

model.to(args.device)

result, _ = evaluate(args, model, tokenizer, labels, pad_token_label_id, mode="dev", checkpoint_id=epoch, prefix=prefix)

result = dict((k '_{}'.format(global_step), 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])))