"gpt2": (GPT2Config, GPT2LMHeadModel, GPT2Tokenizer),

"openai-gpt": (OpenAIGPTConfig, OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),

"bert": (BertConfig, BertForMaskedLM, BertTokenizer),

"roberta": (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),

"distilbert": (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),

"camembert": (CamembertConfig, CamembertForMaskedLM, CamembertTokenizer),

"xlm":(XLMConfig, XLMWithLMHeadModel, XLMTokenizer)

def __init__(self, tokenizer: PreTrainedTokenizer, args, file_path: str, block_size=512):

print('-------------------------')

print(file_path)

assert os.path.isfile(file_path)

directory, filename = os.path.split(file_path)

cached_features_file = os.path.join(

directory, args.model_type + "_cached_lm_" + str(block_size) + "_" + filename

)

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

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

with open(cached_features_file, "rb") as handle:

self.examples = pickle.load(handle)

else:

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

self.examples = []

with open(file_path, encoding="utf-8") as f:

text = f.read()

tokenized_text = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(text))

for i in range(0, len(tokenized_text) - block_size + 1, block_size): # Truncate in block of block_size

self.examples.append(tokenizer.build_inputs_with_special_tokens(tokenized_text[i : i + block_size]))

# Note that we are loosing the last truncated example here for the sake of simplicity (no padding)

# If your dataset is small, first you should loook for a bigger one :-) and second you

# can change this behavior by adding (model specific) padding.

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

with open(cached_features_file, "wb") as handle:

pickle.dump(self.examples, handle, protocol=pickle.HIGHEST_PROTOCOL)

def __len__(self):

return len(self.examples)

def __getitem__(self, item):

def __init__(self, tokenizer: PreTrainedTokenizer, args, file_path: str, block_size=512):

assert os.path.isfile(file_path)

# Here, we do not cache the features, operating under the assumption

# that we will soon use fast multithreaded tokenizers from the

# `tokenizers` repo everywhere =)

print(file_path)

assert os.path.isfile(file_path)

directory, filename = os.path.split(file_path)

cached_features_file = os.path.join(

directory, args.model_type + "_cached_lm_" + str(block_size) + "_" + filename

)

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

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

with open(cached_features_file, "rb") as handle:

self.examples = pickle.load(handle)

else:

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

with open(file_path, encoding="utf-8") as f:

lines = [line for line in f.read().splitlines() if len(line) > 0]

'''

#I modify the code above to let the model directly read the csv file and produce

#pre-computed embeddings

labels = []

texts = []

dialect_tag = []

with open(file_path, 'r') as csvfile:

reader=csv.reader(csvfile)

next(reader)

for row in reader:

tag = row[-3]

label = int(row[-1].strip())

labels.append(label)

texts.append(row[-2])

dialect_tag.append(row[-3])

lines = texts

'''

self.examples = tokenizer.batch_encode_plus(lines, max_length=block_size)["input_ids"]

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

with open(cached_features_file, "wb") as handle:

pickle.dump(self.examples, handle, protocol=pickle.HIGHEST_PROTOCOL)

def __len__(self):

return len(self.examples)

def __getitem__(self, i):

print('-------------------')

print(args.eval_data_file)

file_path = args.eval_data_file if evaluate else args.train_data_file

if args.line_by_line:

return LineByLineTextDataset(tokenizer, args, file_path=file_path, block_size=args.block_size)

else:

random.seed(args.seed)

np.random.seed(args.seed)

torch.manual_seed(args.seed)

if args.n_gpu > 0:

ordering_and_checkpoint_path = []

glob_checkpoints = glob.glob(os.path.join(args.output_dir, "{}-*".format(checkpoint_prefix)))

for path in glob_checkpoints:

if use_mtime:

ordering_and_checkpoint_path.append((os.path.getmtime(path), path))

else:

regex_match = re.match(".*{}-([0-9]+)".format(checkpoint_prefix), path)

if regex_match and regex_match.groups():

ordering_and_checkpoint_path.append((int(regex_match.groups()[0]), path))

checkpoints_sorted = sorted(ordering_and_checkpoint_path)

checkpoints_sorted = [checkpoint[1] for checkpoint in checkpoints_sorted]

if not args.save_total_limit:

return

if args.save_total_limit <= 0:

return

# Check if we should delete older checkpoint(s)

checkpoints_sorted = _sorted_checkpoints(args, checkpoint_prefix, use_mtime)

if len(checkpoints_sorted) <= args.save_total_limit:

return

number_of_checkpoints_to_delete = max(0, len(checkpoints_sorted) - args.save_total_limit)

checkpoints_to_be_deleted = checkpoints_sorted[:number_of_checkpoints_to_delete]

for checkpoint in checkpoints_to_be_deleted:

logger.info("Deleting older checkpoint [{}] due to args.save_total_limit".format(checkpoint))

""" Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original. """

labels = inputs.clone()

# We sample a few tokens in each sequence for masked-LM training (with probability args.mlm_probability defaults to 0.15 in Bert/RoBERTa)

probability_matrix = torch.full(labels.shape, args.mlm_probability)

special_tokens_mask = [

tokenizer.get_special_tokens_mask(val, already_has_special_tokens=True) for val in labels.tolist()

]

probability_matrix.masked_fill_(torch.tensor(special_tokens_mask, dtype=torch.bool), value=0.0)

if tokenizer._pad_token is not None:

padding_mask = labels.eq(tokenizer.pad_token_id)

probability_matrix.masked_fill_(padding_mask, value=0.0)

masked_indices = torch.bernoulli(probability_matrix).bool()

labels[~masked_indices] = -100 # We only compute loss on masked tokens

# 80% of the time, we replace masked input tokens with tokenizer.mask_token ([MASK])

indices_replaced = torch.bernoulli(torch.full(labels.shape, 0.8)).bool() & masked_indices

inputs[indices_replaced] = tokenizer.convert_tokens_to_ids(tokenizer.mask_token)

# 10% of the time, we replace masked input tokens with random word

indices_random = torch.bernoulli(torch.full(labels.shape, 0.5)).bool() & masked_indices & ~indices_replaced

random_words = torch.randint(len(tokenizer), labels.shape, dtype=torch.long)

inputs[indices_random] = random_words[indices_random]

# The rest of the time (10% of the time) we keep the masked input tokens unchanged

""" 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)

def collate(examples: List[torch.Tensor]):

if tokenizer._pad_token is None:

return pad_sequence(examples, batch_first=True)

return pad_sequence(examples, batch_first=True, padding_value=tokenizer.pad_token_id)

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, collate_fn=collate

)

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"]

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},

]

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

)

# Check if saved optimizer or scheduler states exist

if (

args.model_name_or_path

and os.path.isfile(os.path.join(args.model_name_or_path, "optimizer.pt"))

and os.path.isfile(os.path.join(args.model_name_or_path, "scheduler.pt"))

):

# Load in optimizer and scheduler states

optimizer.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "optimizer.pt")))

scheduler.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "scheduler.pt")))

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

epochs_trained = 0

steps_trained_in_current_epoch = 0

# Check if continuing training from a checkpoint

if args.model_name_or_path and os.path.exists(args.model_name_or_path):

try:

# set global_step to gobal_step of last saved checkpoint from model path

checkpoint_suffix = args.model_name_or_path.split("-")[-1].split("/")[0]

global_step = int(checkpoint_suffix)

epochs_trained = global_step // (len(train_dataloader) // args.gradient_accumulation_steps)

steps_trained_in_current_epoch = global_step % (len(train_dataloader) // args.gradient_accumulation_steps)

logger.info(" Continuing training from checkpoint, will skip to saved global_step")

logger.info(" Continuing training from epoch %d", epochs_trained)

logger.info(" Continuing training from global step %d", global_step)

logger.info(" Will skip the first %d steps in the first epoch", steps_trained_in_current_epoch)

except ValueError:

logger.info(" Starting fine-tuning.")

tr_loss, logging_loss = 0.0, 0.0

model_to_resize = model.module if hasattr(model, "module") else model # Take care of distributed/parallel training

model_to_resize.resize_token_embeddings(len(tokenizer))

model.zero_grad()

train_iterator = trange(

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

)

set_seed(args) # Added here for reproducibility

for _ in train_iterator:

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

for step, batch in enumerate(epoch_iterator):

# Skip past any already trained steps if resuming training

if steps_trained_in_current_epoch > 0:

steps_trained_in_current_epoch -= 1

continue

inputs, labels = mask_tokens(batch, tokenizer, args) if args.mlm else (batch, batch)

inputs = inputs.to(args.device)

labels = labels.to(args.device)

model.train()

outputs = model(inputs, masked_lm_labels=labels) if args.mlm else model(inputs, labels=labels)

loss = outputs[0] # model outputs are always tuple in 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)

optimizer.step()

scheduler.step() # Update learning rate schedule

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)

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

checkpoint_prefix = "checkpoint"

# Save model checkpoint

output_dir = os.path.join(args.output_dir, "{}-{}".format(checkpoint_prefix, global_step))

os.makedirs(output_dir, exist_ok=True)

model_to_save = (

model.module if hasattr(model, "module") else model

) # Take care of distributed/parallel training

model_to_save.save_pretrained(output_dir)

tokenizer.save_pretrained(output_dir)

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

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

_rotate_checkpoints(args, checkpoint_prefix)

torch.save(optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt"))

torch.save(scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt"))

logger.info("Saving optimizer and scheduler states to %s", output_dir)

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

epoch_iterator.close()

break

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()

# Loop to handle MNLI double evaluation (matched, mis-matched)

eval_output_dir = args.output_dir

eval_dataset = load_and_cache_examples(args, tokenizer, evaluate=True)

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

os.makedirs(eval_output_dir, exist_ok=True)

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

# Note that DistributedSampler samples randomly

def collate(examples: List[torch.Tensor]):

if tokenizer._pad_token is None:

return pad_sequence(examples, batch_first=True)

return pad_sequence(examples, batch_first=True, padding_value=tokenizer.pad_token_id)

eval_sampler = SequentialSampler(eval_dataset)

eval_dataloader = DataLoader(

eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size, collate_fn=collate

)

# multi-gpu evaluate

if args.n_gpu > 1:

model = torch.nn.DataParallel(model)

# Eval!

logger.info("***** Running evaluation {} *****".format(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

model.eval()

rep_list = []

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

#inputs, labels = mask_tokens(batch, tokenizer, args) if args.mlm else (batch, batch)

inputs, labels = (batch, batch)

inputs = inputs.to(args.device)

labels = labels.to(args.device)

with torch.no_grad():

outputs = model(inputs, masked_lm_labels=labels) if args.mlm else model(inputs, labels=labels)

lm_loss = outputs[0]

hidden = outputs[2]

print(hidden[-1].size())

avg_hidden = hidden[-1][:,1:,:].mean(dim=1)

print(avg_hidden.size())

avg_hidden = avg_hidden.cpu().numpy()

rep_list.append(avg_hidden)

eval_loss += lm_loss.mean().item()

nb_eval_steps += 1

eval_loss = eval_loss / nb_eval_steps

perplexity = torch.exp(torch.tensor(eval_loss))

result = {"perplexity": perplexity}

rep_arrays = np.concatenate(rep_list, axis=0)

print(rep_arrays.shape)

np.save(args.rep_name, rep_arrays)

output_eval_file = os.path.join(eval_output_dir, prefix, "eval_results.txt")

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

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

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

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

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

parser = argparse.ArgumentParser()

# Required parameters

parser.add_argument(

"--train_data_file", default=None, type=str, required=True, help="The input training data file (a text file)."

)

parser.add_argument(

"--output_dir",

type=str,

required=True,

help="The output directory where the model predictions and checkpoints will be written.",

)

parser.add_argument(

"--model_type", type=str, required=True, help="The model architecture to be trained or fine-tuned.",

)

# Other parameters

parser.add_argument(

"--eval_data_file",

default=None,

type=str,

help="An optional input evaluation data file to evaluate the perplexity on (a text file).",

)

parser.add_argument(

"--line_by_line",

action="store_true",

help="Whether distinct lines of text in the dataset are to be handled as distinct sequences.",

)

parser.add_argument(

"--should_continue", action="store_true", help="Whether to continue from latest checkpoint in output_dir"

)

parser.add_argument(

"--model_name_or_path",

default=None,

type=str,

help="The model checkpoint for weights initialization. Leave None if you want to train a model from scratch.",

)

parser.add_argument(

"--mlm", action="store_true", help="Train with masked-language modeling loss instead of language modeling."

)

parser.add_argument(

"--mlm_probability", type=float, default=0.15, help="Ratio of tokens to mask for masked language modeling loss"

)

parser.add_argument(

"--config_name",

default=None,

type=str,

help="Optional pretrained config name or path if not the same as model_name_or_path. If both are None, initialize a new config.",

)

parser.add_argument(

"--tokenizer_name",

default=None,

type=str,

help="Optional pretrained tokenizer name or path if not the same as model_name_or_path. If both are None, initialize a new tokenizer.",

)

parser.add_argument(

"--cache_dir",

default=None,

type=str,

help="Optional directory to store the pre-trained models downloaded from s3 (instead of the default one)",

)

parser.add_argument(

"--block_size",

default=-1,

type=int,

help="Optional input sequence length after tokenization."

"The training dataset will be truncated in block of this size for training."

"Default to the model max input length for single sentence inputs (take into account special tokens).",

)

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("--special_eval", action="store_true", help="Whether to run eval on the dev set for ori models.")

parser.add_argument(

"--evaluate_during_training", action="store_true", help="Run evaluation during training at each logging step."

)

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=1.0, 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=500, help="Log every X updates steps.")

parser.add_argument("--save_steps", type=int, default=500, help="Save checkpoint every X updates steps.")

parser.add_argument(

"--save_total_limit",

type=int,

default=None,

help="Limit the total amount of checkpoints, delete the older checkpoints in the output_dir, does not delete by default",

)

parser.add_argument(

"--eval_all_checkpoints",

action="store_true",

help="Evaluate all checkpoints starting with the same prefix as model_name_or_path 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("--rep_name", type=str, default="rep.npy", help="For producing representation")

args = parser.parse_args()

if args.model_type in ["bert", "roberta", "distilbert", "camembert"] and not args.mlm:

raise ValueError(

"BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the --mlm "

"flag (masked language modeling)."

)

if args.eval_data_file is None and args.do_eval:

raise ValueError(

"Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file "

"or remove the --do_eval argument."

)

if args.should_continue:

sorted_checkpoints = _sorted_checkpoints(args)

if len(sorted_checkpoints) == 0:

raise ValueError("Used --should_continue but no checkpoint was found in --output_dir.")

else:

args.model_name_or_path = sorted_checkpoints[-1]

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:

print(torch.cuda.is_available())

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)

# Load pretrained model and tokenizer

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

torch.distributed.barrier() # Barrier to make sure only the first process in distributed training download model & vocab

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

if args.config_name:

config = config_class.from_pretrained(args.config_name, cache_dir=args.cache_dir)

elif args.model_name_or_path:

config = config_class.from_pretrained(args.model_name_or_path, cache_dir=args.cache_dir)

else:

config = config_class()

#change config to make it produce hiddenstates

config.output_hidden_states=True

if args.tokenizer_name:

tokenizer = tokenizer_class.from_pretrained(args.tokenizer_name, cache_dir=args.cache_dir)

elif args.model_name_or_path:

tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path, cache_dir=args.cache_dir)

else:

raise ValueError(

"You are instantiating a new {} tokenizer. This is not supported, but you can do it from another script, save it,"

"and load it from here, using --tokenizer_name".format(tokenizer_class.__name__)

)

if args.block_size <= 0:

args.block_size = tokenizer.max_len_single_sentence

# Our input block size will be the max possible for the model

else:

args.block_size = min(args.block_size, tokenizer.max_len_single_sentence)

if args.model_name_or_path:

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,

)

else:

logger.info("Training new model from scratch")

model = model_class(config=config)

model.to(args.device)

if args.local_rank == 0:

torch.distributed.barrier() # End of barrier to make sure only the first process in distributed training download model & vocab

logger.info("Training/evaluation parameters %s", args)

# Training

if args.do_train:

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

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

train_dataset = load_and_cache_examples(args, tokenizer, evaluate=False)

if args.local_rank == 0:

torch.distributed.barrier()

global_step, tr_loss = train(args, train_dataset, model, tokenizer)

logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)

# Saving best-practices: if you use save_pretrained for the model and tokenizer, you can reload them using from_pretrained()

if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):

# Create output directory if needed

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

os.makedirs(args.output_dir, exist_ok=True)

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"))

# Load a trained model and vocabulary that you have fine-tuned

model = model_class.from_pretrained(args.output_dir)

tokenizer = tokenizer_class.from_pretrained(args.output_dir)

model.to(args.device)

# Evaluation

results = {}

if args.special_eval:

results = evaluate(args, model, tokenizer)

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

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("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)

model.to(args.device)

result = evaluate(args, model, tokenizer, prefix=prefix)

result = dict((k + "_{}".format(global_step), v) for k, v in result.items())

results.update(result)