def main():
# directory for training outputs
output_dir = "results/{:%Y%m%d_%H%M%S}/".format(datetime.now())
# required parameters
parser = argparse.ArgumentParser()
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: " +
", ".join(ALL_MODELS))
parser.add_argument("--checkpoint",
default='',
type=str,
required=True,
help="where to load pre-trained model.")
parser.add_argument(
"--max_seq_length",
default=512,
type=int,
required=True,
help=
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.")
parser.add_argument("--use_pretrained",
action='store_true',
default=True,
help="If use pre-trained model weights.")
# other parameters
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(
"--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("--num_epochs",
default=30,
type=int,
help="Total number of training epochs to perform.")
parser.add_argument(
"--task_name",
default='lpc',
type=str,
help="The name of the task to train selected in the list: " +
", ".join(processors.keys()))
parser.add_argument("--weight_decay",
default=0.0,
type=float,
help="Weight deay if we apply some.")
parser.add_argument("--learning_rate",
default=2e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--adam_epsilon",
default=1e-8,
type=float,
help="Epsilon for Adam optimizer.")
parser.add_argument("--warmup_steps",
default=3,
type=int,
help="Linear warmup over warmup_steps.")
parser.add_argument("--per_gpu_eval_batch_size",
default=4,
type=int,
help="Batch size for evaluation.")
parser.add_argument("--no_cuda",
default=False,
type=bool,
help="Do not use cuda.")
parser.add_argument("--do_lower_case",
default=True,
type=bool,
help="Do lower case.")
parser.add_argument("--seed", default=610, type=int, help="Random seed.")
parser.add_argument("--num_labels",
default=3,
type=int,
help="Classification label number.")
parser.add_argument("--scheduler",
default='warmup',
type=str,
help="Which type of scheduler to use.")
parser.add_argument("--local_rank",
type=int,
default=-1,
help="For distributed training: local_rank")
parser.add_argument(
'--overwrite_cache',
action='store_true',
default=False,
help="Overwrite the cached training and evaluation sets")
parser.add_argument('--write_summary',
default=True,
type=bool,
help="If write summary into tensorboard.")
parser.add_argument(
'--fp16',
action='store_true',
default=False,
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")
# data directory
parser.add_argument("--data_dir",
default='../data/TF-IDF',
type=str,
help="data directory where pickle dataset is stored.")
parser.add_argument(
"--output_dir",
default=output_dir,
type=str,
help="output directory for model, log file and summary.")
parser.add_argument("--log_path",
default=join(output_dir, "log.txt"),
type=str,
help="Path to log.txt.")
parser.add_argument("--summary_path",
default=join(output_dir, "summary"),
type=str,
help="Path to summary file.")
args = parser.parse_args()
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
if not os.path.exists(args.data_dir):
os.makedirs(args.data_dir)
args.logger = get_logger(args.log_path)
# Setup CUDA, GPU & distributed training
args.device = torch.device(
"cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
args.n_gpu = torch.cuda.device_count()
args.logger.info("- device: {}, n_gpu: {}".format(args.device, args.n_gpu))
args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
# set seed
set_seed(args.seed)
# build model
args.logger.info("Build model...")
model = Model(args)
# make data
make_data(ARTICLES_FILEPATH,
METADATA_FILEPATH,
args.data_dir,
file_name='dev')
# build dataset
args.logger.info("Loading dataset...")
eval_dataset, guids = load_and_cache_examples(args,
args.task_name,
model.tokenizer,
evaluate=True)
eval_sampler = SequentialSampler(eval_dataset)
eval_dataloader = DataLoader(eval_dataset,
sampler=eval_sampler,
batch_size=args.eval_batch_size)
# training
args.logger.info("Start testing:")
preds = model.test(eval_dataloader)
assert len(preds) == len(
guids), "Prediction list and GUID list length do NOT equal!!!"
# write results
args.logger.info("Write prediction results:")
write_results(OUTPUT_DIR, guids, preds)
args.logger.info("Save results at: {}".format(
os.path.join(OUTPUT_DIR, 'predictions.txt')))
def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument(
"--data_dir",
default="/scratch/yyv959/commonsenseqa/",
type=str,
help=
"The input data dir. Should contain the .tsv files (or other data files) for the task."
)
parser.add_argument(
"--fake_data_dir",
default="/scratch/yyv959/commonsenseqa/",
type=str,
help=
"The input data dir. Should contain the .tsv files (or other data files) for the task."
)
parser.add_argument("--model_type",
default='roberta',
type=str,
help="Model type selected in the list: " +
", ".join(MODEL_CLASSES.keys()))
parser.add_argument(
"--model_name_or_path",
default="roberta-large",
type=str,
help="Path to pre-trained model or shortcut name selected in the list: "
)
parser.add_argument(
"--output_dir",
default="",
type=str,
help="Path to pre-trained model or shortcut name selected in the list: "
)
parser.add_argument(
"--task_name",
default="commonsenseqa",
type=str,
help="The name of the task to train selected in the list: " +
", ".join(processors.keys()))
## Other parameters
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=70,
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_lower_case",
action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument(
"--mask_question",
action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument("--train_batch_size",
default=10,
type=int,
help="Batch size per GPU/CPU for training.")
parser.add_argument("--gradient_accumulation_steps",
default=10,
type=int,
help="Batch size per GPU/CPU for training.")
parser.add_argument("--eval_batch_size",
default=16,
type=int,
help="Batch size per GPU/CPU for evaluation.")
parser.add_argument("--weight_decay",
default=0.01,
type=float,
help="Weight deay if we apply some.")
parser.add_argument("--damping",
default=0.01,
type=float,
help="Weight deay if we apply some.")
parser.add_argument("--c",
default=1e7,
type=float,
help="Weight deay if we apply some.")
parser.add_argument("--r",
default=10,
type=int,
help="Total number of training epochs to perform.")
parser.add_argument("--t",
default=8000,
type=int,
help="Total number of training epochs to perform.")
parser.add_argument('--logging_steps',
type=int,
default=609,
help="Log every X updates steps.")
parser.add_argument('--save_steps',
type=int,
default=100000,
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(
"--no_hessian",
action='store_true',
help=
"Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number"
)
parser.add_argument(
"--load_hvp",
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("--local_rank",
type=int,
default=-1,
help="For distributed training: local_rank")
args = parser.parse_args()
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
args.device = torch.device("cuda")
# Set seed
set_seed(args)
# Prepare GLUE task
args.task_name = args.task_name.lower()
if args.task_name not in processors:
raise ValueError("Task not found: %s" % (args.task_name))
processor = processors[args.task_name]()
label_list = processor.get_labels()
num_labels = len(label_list)
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,
finetuning_task=args.task_name)
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)
model = model_class.from_pretrained(
args.model_name_or_path,
from_tf=bool('.ckpt' in args.model_name_or_path),
config=config)
model.to(args.device)
#if args.fp16:
# model.half()
train_dataset = load_and_cache_examples(args,
args.task_name,
tokenizer,
evaluate=False,
test=False)
eval_dataset = load_and_cache_examples(args,
args.task_name,
tokenizer,
evaluate=True,
test=False)
fake_dataset = load_and_cache_examples(args,
args.task_name,
tokenizer,
evaluate=False,
test=False,
fake=True)
#eval_dataset = eval_dataset[:200,:]
#print(eval_dataset
#)
if not args.load_hvp:
grad = get_validation_grad(args, eval_dataset, model)
if args.no_hessian:
HVP = grad
else:
HVP = get_HVP(args, train_dataset, model, grad, args)
torch.save(
HVP, args.output_dir + "HVP_" + str(args.train_batch_size) + "b_" +
str(args.t) + "t_" + str(args.r) + "r")
else:
HVP = torch.load(args.output_dir + "HVP_" +
str(args.train_batch_size) + "b_" + str(args.t) +
"t_" + str(args.r) + "r")
influences = get_influence(args, fake_dataset, model, HVP, args)
if args.no_hessian:
np.save(
os.path.join(args.output_dir,
"train_data_influences_no_hessian" + ".npy"),
influences)
else:
np.save(
os.path.join(
args.output_dir,
"fake_data_300000_influences" + str(args.train_batch_size) +
"b_" + str(args.t) + "t_" + str(args.r) + "r" + ".npy"),
influences)
def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument(
"--data_dir",
default="/scratch/yyv959/commonsenseqa/",
type=str,
help=
"The input data dir. Should contain the .tsv files (or other data files) for the task."
)
parser.add_argument("--model_type",
default='gpt2',
type=str,
help="Model type selected in the list: " +
", ".join(MODEL_CLASSES.keys()))
parser.add_argument(
"--model_name_or_path",
default="gpt2",
type=str,
help="Path to pre-trained model or shortcut name selected in the list: "
+ ", ".join(ALL_MODELS))
parser.add_argument(
"--task_name",
default="commonsenseqa",
type=str,
help="The name of the task to train selected in the list: " +
", ".join(processors.keys()))
parser.add_argument(
"--output_dir",
default="/scratch/yyv959/commonsenseqa/outputs/gpt2/qg/",
type=str,
help=
"The output directory where the model predictions and checkpoints will be written."
)
## Other parameters
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(
"--input_max_seq_length",
default=62,
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(
"--output_max_seq_length",
default=10,
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_test",
action='store_true',
help='Whether to run test on the test set')
parser.add_argument(
"--evaluate_during_training",
action='store_true',
help="Rul 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("--per_gpu_train_batch_size",
default=2,
type=int,
help="Batch size per GPU/CPU for training.")
parser.add_argument("--per_gpu_eval_batch_size",
default=32,
type=int,
help="Batch size per GPU/CPU for evaluation.")
parser.add_argument(
'--gradient_accumulation_steps',
type=int,
default=8,
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=0.01,
type=float,
help="Weight deay if we apply some.")
parser.add_argument("--adam_epsilon",
default=1e-6,
type=float,
help="Epsilon for Adam optimizer.")
parser.add_argument("--max_grad_norm",
default=1.0,
type=float,
help="Max gradient norm.")
parser.add_argument("--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_ratio",
default=0.0,
type=float,
help="Linear warmup over warmup_steps.")
parser.add_argument("--linear_decay",
action='store_true',
help="Avoid using CUDA when available")
parser.add_argument('--logging_steps',
type=int,
default=609,
help="Log every X updates steps.")
parser.add_argument('--save_steps',
type=int,
default=5000,
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("--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(
'--multi_task',
action='store_true',
help="Overwrite the cached training and evaluation sets")
parser.add_argument(
'--mc',
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 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)
# Set seed
set_seed(args)
# Prepare GLUE task
args.task_name = args.task_name.lower()
if args.task_name not in processors:
raise ValueError("Task not found: %s" % (args.task_name))
processor = processors[args.task_name]()
label_list = processor.get_labels()
num_labels = len(label_list)
# 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]
config = config_class.from_pretrained(
args.config_name if args.config_name else args.model_name_or_path,
num_labels=num_labels,
finetuning_task=args.task_name)
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)
model = model_class.from_pretrained(
args.model_name_or_path,
from_tf=bool('.ckpt' in args.model_name_or_path),
config=config)
if args.local_rank == 0:
torch.distributed.barrier(
) # Make sure only the first process in distributed training will download model & vocab
tokenizer.add_special_tokens({'pad_token': "[PAD]"})
#tokenizer.add_special_tokens({'cls_token': "[CLS]"})
#tokenizer.add_special_tokens({'sep_token': "[SEP]"})
#print(model.transformer.generator.weight.size())
#print(model.transformer.wte.weight[0,:10])
model.transformer._resize_token_embeddings(len(tokenizer))
#print(model.transformer.wte.weight[0,:10])
#print(model.transformer.generator.weight.size())
model.transformer.tie_weights()
#print(model.transformer.generator.weight[0,:10])
#print(model.transformer.generator.weight.size())
model.transformer.config.vocab_size += 1
#print(model.transformer.wte.weight.size())
#print(model.generator.weight.size())
#quit()
#model.config.vocab_size += 3
model.add_tokenizer(tokenizer)
model.to(args.device)
logger.info("Training/evaluation parameters %s", args)
best_steps = 0
# Training
if args.do_train:
train_dataset = load_and_cache_examples(args,
args.task_name,
tokenizer,
evaluate=False)
global_step, tr_loss, best_steps = train(args, train_dataset, model,
tokenizer)
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'))
# 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, do_lower_case=args.do_lower_case)
model.add_tokenizer(tokenizer)
model.to(args.device)
# Evaluation
results = {}
if args.do_eval and args.local_rank in [-1, 0]:
#if not args.do_train:
# args.output_dir = args.model_name_or_path
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 ""
model = model_class.from_pretrained(checkpoint)
model.add_tokenizer(tokenizer)
model.to(args.device)
result = evaluate(args,
model,
tokenizer,
prefix=global_step,
generation=True)
result = dict(
(k + '_{}'.format(global_step), v) for k, v in result.items())
results.update(result)
if args.do_test and args.local_rank in [-1, 0]:
if not args.do_train:
args.output_dir = args.model_name_or_path
checkpoints = [args.output_dir]
# if args.eval_all_checkpoints: # can not use this to do test!!
# 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 ""
model = model_class.from_pretrained(checkpoint)
model.to(args.device)
result = evaluate(args,
model,
tokenizer,
prefix=global_step,
test=True)
result = dict(
(k + '_{}'.format(global_step), v) for k, v in result.items())
results.update(result)
if best_steps:
logger.info("best steps of eval acc is the following checkpoints: %s",
best_steps)
return results
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 .tsv files (or other data files) for the 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(
"--task_name",
default=None,
type=str,
required=True,
help="The name of the task to train selected in the list: " +
", ".join(processors.keys()),
)
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(
"--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_pred", action="store_true",
help="Whether to do prediction on test set")
parser.add_argument(
"--evaluate_during_training", action="store_true", help="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(
"--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=1e-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_ratio", default=0.1,
type=float, help="Linear warmup ratio.")
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(
"--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("--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
)
)
# 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",
args.local_rank,
device,
args.n_gpu,
bool(args.local_rank != -1),
)
# Set seed
set_seed(args)
# Prepare GLUE task
args.task_name = args.task_name.lower()
if args.task_name not in processors:
raise ValueError("Task not found: %s" % (args.task_name))
processor = processors[args.task_name]()
args.output_mode = output_modes[args.task_name]
label_list = processor.get_labels()
num_labels = len(label_list)
# Load pretrained model and tokenizer
if args.local_rank not in [-1, 0]:
# Make sure only the first process in distributed training will download model & vocab
torch.distributed.barrier()
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,
finetuning_task=args.task_name,
)
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,
)
model = model_class.from_pretrained(
args.model_name_or_path,
from_tf=bool(".ckpt" in args.model_name_or_path),
config=config,
)
if args.local_rank == 0:
# Make sure only the first process in distributed training will download model & vocab
torch.distributed.barrier()
model.to(args.device)
logger.info("Training/evaluation parameters %s", args)
# Training
if args.do_train:
train_dataset = load_and_cache_examples(
args, args.task_name, tokenizer, evaluate=False)
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 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"))
# 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.do_eval and args.local_rank in [-1, 0]:
if args.do_train:
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("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)
else:
tokenizer = tokenizer_class.from_pretrained(
args.model_name_or_path)
model = model_class.from_pretrained(args.model_name_or_path)
model.to(args.device)
prefix = args.data_dir.split("/")[-1]
print(prefix)
result = evaluate(args, model, tokenizer, prefix=prefix)
result = dict((k, v) for k, v in result.items())
results.update(result)
if args.do_pred:
tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path)
model = model_class.from_pretrained(args.model_name_or_path)
model.to(args.device)
prefix = args.data_dir.split("/")[-1]
print(prefix)
result = prediction(args, model, tokenizer, prefix=prefix)
result = dict((k, v) for k, v in result.items())
results.update(result)
return results