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""" |
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Fine-tuning the library models for language modeling on a text file (GPT, GPT-2, BERT, RoBERTa). |
|
GPT and GPT-2 are fine-tuned using a causal language modeling (CLM) loss while BERT and RoBERTa are fine-tuned |
|
using a masked language modeling (MLM) loss. |
|
""" |
|
|
|
|
|
import argparse |
|
import glob |
|
import logging |
|
import os |
|
import pickle |
|
import random |
|
import re |
|
import shutil |
|
from typing import Dict, List, Tuple |
|
|
|
import numpy as np |
|
import torch |
|
from torch.nn.utils.rnn import pad_sequence |
|
from torch.utils.data import DataLoader, Dataset, RandomSampler, SequentialSampler |
|
from torch.utils.data.distributed import DistributedSampler |
|
from tqdm import tqdm, trange |
|
|
|
from transformers import ( |
|
MODEL_WITH_LM_HEAD_MAPPING, |
|
WEIGHTS_NAME, |
|
AdamW, |
|
AutoConfig, |
|
AutoModelWithLMHead, |
|
AutoTokenizer, |
|
PreTrainedModel, |
|
PreTrainedTokenizer, |
|
get_linear_schedule_with_warmup, |
|
) |
|
|
|
|
|
try: |
|
from torch.utils.tensorboard import SummaryWriter |
|
except ImportError: |
|
from tensorboardX import SummaryWriter |
|
|
|
|
|
logger = logging.getLogger(__name__) |
|
|
|
|
|
MODEL_CONFIG_CLASSES = list(MODEL_WITH_LM_HEAD_MAPPING.keys()) |
|
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) |
|
|
|
|
|
class TextDataset(Dataset): |
|
def __init__(self, tokenizer: PreTrainedTokenizer, args, file_path: str, block_size=512): |
|
assert os.path.isfile(file_path) |
|
|
|
block_size = block_size - (tokenizer.max_len - tokenizer.max_len_single_sentence) |
|
|
|
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): |
|
self.examples.append(tokenizer.build_inputs_with_special_tokens(tokenized_text[i : i + block_size])) |
|
|
|
|
|
|
|
|
|
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): |
|
return torch.tensor(self.examples[item], dtype=torch.long) |
|
|
|
|
|
class LineByLineTextDataset(Dataset): |
|
def __init__(self, tokenizer: PreTrainedTokenizer, args, file_path: str, block_size=512): |
|
assert os.path.isfile(file_path) |
|
|
|
|
|
|
|
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 and not line.isspace())] |
|
|
|
self.examples = tokenizer.batch_encode_plus(lines, add_special_tokens=True, max_length=block_size)["input_ids"] |
|
|
|
def __len__(self): |
|
return len(self.examples) |
|
|
|
def __getitem__(self, i): |
|
return torch.tensor(self.examples[i], dtype=torch.long) |
|
|
|
|
|
def load_and_cache_examples(args, tokenizer, evaluate=False): |
|
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: |
|
return TextDataset(tokenizer, args, file_path=file_path, block_size=args.block_size) |
|
|
|
|
|
def set_seed(args): |
|
random.seed(args.seed) |
|
np.random.seed(args.seed) |
|
torch.manual_seed(args.seed) |
|
if args.n_gpu > 0: |
|
torch.cuda.manual_seed_all(args.seed) |
|
|
|
|
|
def _sorted_checkpoints(args, checkpoint_prefix="checkpoint", use_mtime=False) -> List[str]: |
|
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] |
|
return checkpoints_sorted |
|
|
|
|
|
def _rotate_checkpoints(args, checkpoint_prefix="checkpoint", use_mtime=False) -> None: |
|
if not args.save_total_limit: |
|
return |
|
if args.save_total_limit <= 0: |
|
return |
|
|
|
|
|
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)) |
|
shutil.rmtree(checkpoint) |
|
|
|
|
|
def mask_tokens(inputs: torch.Tensor, tokenizer: PreTrainedTokenizer, args) -> Tuple[torch.Tensor, torch.Tensor]: |
|
""" Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original. """ |
|
|
|
if tokenizer.mask_token is None: |
|
raise ValueError( |
|
"This tokenizer does not have a mask token which is necessary for masked language modeling. Remove the --mlm flag if you want to use this tokenizer." |
|
) |
|
|
|
labels = inputs.clone() |
|
|
|
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 |
|
|
|
|
|
indices_replaced = torch.bernoulli(torch.full(labels.shape, 0.8)).bool() & masked_indices |
|
inputs[indices_replaced] = tokenizer.convert_tokens_to_ids(tokenizer.mask_token) |
|
|
|
|
|
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] |
|
|
|
|
|
return inputs, labels |
|
|
|
|
|
def train(args, train_dataset, model: PreTrainedModel, tokenizer: PreTrainedTokenizer) -> Tuple[int, float]: |
|
""" 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 |
|
|
|
model = model.module if hasattr(model, "module") else model |
|
model.resize_token_embeddings(len(tokenizer)) |
|
|
|
|
|
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 |
|
) |
|
|
|
|
|
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")) |
|
): |
|
|
|
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) |
|
|
|
|
|
if args.n_gpu > 1: |
|
model = torch.nn.DataParallel(model) |
|
|
|
|
|
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 |
|
) |
|
|
|
|
|
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 |
|
|
|
if args.model_name_or_path and os.path.exists(args.model_name_or_path): |
|
try: |
|
|
|
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.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) |
|
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): |
|
|
|
|
|
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] |
|
|
|
if args.n_gpu > 1: |
|
loss = loss.mean() |
|
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() |
|
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: |
|
|
|
if ( |
|
args.local_rank == -1 and args.evaluate_during_training |
|
): |
|
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" |
|
|
|
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 |
|
) |
|
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() |
|
|
|
return global_step, tr_loss / global_step |
|
|
|
|
|
def evaluate(args, model: PreTrainedModel, tokenizer: PreTrainedTokenizer, prefix="") -> Dict: |
|
|
|
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) |
|
|
|
|
|
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 |
|
) |
|
|
|
|
|
if args.n_gpu > 1: |
|
model = torch.nn.DataParallel(model) |
|
|
|
|
|
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() |
|
|
|
for batch in tqdm(eval_dataloader, desc="Evaluating"): |
|
inputs, labels = mask_tokens(batch, tokenizer, args) if args.mlm else (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] |
|
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} |
|
|
|
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]))) |
|
|
|
return result |
|
|
|
|
|
def main(): |
|
parser = argparse.ArgumentParser() |
|
|
|
|
|
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.", |
|
) |
|
|
|
|
|
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( |
|
"--evaluate_during_training", action="store_true", help="Run evaluation during training at each logging step." |
|
) |
|
|
|
parser.add_argument("--per_gpu_train_batch_size", default=4, type=int, help="Batch size per GPU/CPU for training.") |
|
parser.add_argument( |
|
"--per_gpu_eval_batch_size", default=4, 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.") |
|
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 |
|
and not args.should_continue |
|
): |
|
raise ValueError( |
|
"Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format( |
|
args.output_dir |
|
) |
|
) |
|
|
|
|
|
if args.server_ip and args.server_port: |
|
|
|
import ptvsd |
|
|
|
print("Waiting for debugger attach") |
|
ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True) |
|
ptvsd.wait_for_attach() |
|
|
|
|
|
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 = 0 if args.no_cuda else torch.cuda.device_count() |
|
else: |
|
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 |
|
|
|
|
|
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(args) |
|
|
|
|
|
if args.local_rank not in [-1, 0]: |
|
torch.distributed.barrier() |
|
|
|
if args.config_name: |
|
config = AutoConfig.from_pretrained(args.config_name, cache_dir=args.cache_dir) |
|
elif args.model_name_or_path: |
|
config = AutoConfig.from_pretrained(args.model_name_or_path, cache_dir=args.cache_dir) |
|
else: |
|
|
|
|
|
raise ValueError( |
|
"You are instantiating a new config instance from scratch. This is not supported, but you can do it from another script, save it," |
|
"and load it from here, using --config_name" |
|
) |
|
|
|
if args.tokenizer_name: |
|
tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, cache_dir=args.cache_dir) |
|
elif args.model_name_or_path: |
|
tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, cache_dir=args.cache_dir) |
|
else: |
|
raise ValueError( |
|
"You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another script, save it," |
|
"and load it from here, using --tokenizer_name" |
|
) |
|
|
|
if args.block_size <= 0: |
|
args.block_size = tokenizer.max_len |
|
|
|
else: |
|
args.block_size = min(args.block_size, tokenizer.max_len) |
|
|
|
if args.model_name_or_path: |
|
model = AutoModelWithLMHead.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 = AutoModelWithLMHead.from_config(config) |
|
|
|
model.to(args.device) |
|
|
|
if args.local_rank == 0: |
|
torch.distributed.barrier() |
|
|
|
logger.info("Training/evaluation parameters %s", args) |
|
|
|
|
|
if args.do_train: |
|
if args.local_rank not in [-1, 0]: |
|
torch.distributed.barrier() |
|
|
|
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) |
|
|
|
|
|
if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0): |
|
|
|
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) |
|
|
|
|
|
model_to_save = ( |
|
model.module if hasattr(model, "module") else model |
|
) |
|
model_to_save.save_pretrained(args.output_dir) |
|
tokenizer.save_pretrained(args.output_dir) |
|
|
|
|
|
torch.save(args, os.path.join(args.output_dir, "training_args.bin")) |
|
|
|
|
|
model = AutoModelWithLMHead.from_pretrained(args.output_dir) |
|
tokenizer = AutoTokenizer.from_pretrained(args.output_dir) |
|
model.to(args.device) |
|
|
|
|
|
results = {} |
|
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) |
|
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 = AutoModelWithLMHead.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) |
|
|
|
return results |
|
|
|
|
|
if __name__ == "__main__": |
|
main() |
|
|