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(
"--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=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 deay 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('--save_steps',
type=int,
default=50,
help="Save checkpoint every X updates steps.")
parser.add_argument(
"--eval_all_checkpoints",
action='store_true',
help=
"Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number"
)
parser.add_argument("--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 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:
print(args.task_name)
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]:
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
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, do_lower_case=args.do_lower_case)
model.to(args.device)
# Evaluation
results = {}
preds = []
if args.do_eval and args.local_rank in [-1, 0]:
tokenizer = tokenizer_class.from_pretrained(
args.output_dir, do_lower_case=args.do_lower_case)
checkpoints = [args.output_dir]
if args.eval_all_checkpoints:
checkpoints = list(
os.path.dirname(c) for c in sorted(
glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME,
recursive=True)))
logging.getLogger("pytorch_transformers.modeling_utils").setLevel(
logging.WARN) # Reduce logging
logger.info("Evaluate the following checkpoints: %s", checkpoints)
for checkpoint in checkpoints:
global_step = checkpoint.split(
'-')[-1] if len(checkpoints) > 1 else ""
model = model_class.from_pretrained(checkpoint)
model.to(args.device)
result, preds = evaluate(args,
model,
tokenizer,
prefix=global_step)
result = dict(
(k + '_{}'.format(global_step), v) for k, v in result.items())
results.update(result)
evaluation_type = "valid" #else test
if (evaluation_type == "valid"):
filename = args.data_dir + "/" + args.task_name + "_valid.tsv"
else:
filename = args.data_dir + "/" + args.task_name + "_test.tsv"
t_f = read_data(filename=filename)
map_val = calc_map1(t_f, preds)
mrr_val = calc_mrr1(t_f, preds)
print("MAP: " + str(map_val))
print("MRR: " + str(mrr_val))
return results
def compute_metrics(task_name, preds, labels):
print(len(preds))
print(len(labels))
assert len(preds) == len(labels)
if task_name == "cola":
return {"mcc": matthews_corrcoef(labels, preds)}
elif task_name == "sst-2":
return {"acc": simple_accuracy(preds, labels)}
elif task_name == "mrpc":
return acc_and_f1(preds, labels)
elif task_name == "sts-b":
return pearson_and_spearman(preds, labels)
elif task_name == "qqp":
return acc_and_f1(preds, labels)
elif task_name == "mnli":
return {"acc": simple_accuracy(preds, labels)}
elif task_name == "mnli-mm":
return {"acc": simple_accuracy(preds, labels)}
elif task_name == "qnli":
return {"acc": simple_accuracy(preds, labels)}
elif task_name == "rte":
return {"acc": simple_accuracy(preds, labels)}
elif task_name == "lrec":
return {"acc": simple_accuracy(preds, labels)}
elif task_name == "wnli":
return {"acc": simple_accuracy(preds, labels)}
elif task_name == "trecc":
if (type == "test"):
filename = task_name + "_test.tsv"
else:
filename = task_name + "_test.tsv"
t_f = read_data(filename=filename)
return map_and_mrr(preds, labels, t_f)
elif task_name == "trecr":
if (type == "test"):
filename = task_name + "_test.tsv"
else:
filename = task_name + "_test.tsv"
t_f = read_data(filename=filename)
return map_and_mrr(preds, labels, t_f)
elif task_name == "yahoo":
if (type == "test"):
filename = task_name + "_test.tsv"
else:
filename = task_name + "_test.tsv"
t_f = read_data(filename=filename)
return map_and_mrr(preds, labels, t_f)
elif task_name == "wiki":
if (type == "test"):
filename = task_name + "_test.tsv"
else:
filename = task_name + "_test.tsv"
t_f = read_data(filename=filename)
return map_and_mrr(preds, labels, t_f)
elif task_name == "mydataset":
if (type == "test"):
filename = task_name + "_test.tsv"
else:
filename = task_name + "_test.tsv"
t_f = read_data(filename=filename)
return map_and_mrr(preds, labels, t_f)
elif task_name == "semeval2016":
if (type == "test"):
filename = task_name + "_test.tsv"
else:
filename = task_name + "_test.tsv"
t_f = read_data(filename=filename)
return map_and_mrr(preds, labels, t_f)
elif task_name == "semeval2017":
if (type == "test"):
filename = task_name + "_test.tsv"
else:
filename = task_name + "_test.tsv"
t_f = read_data(filename=filename)
return map_and_mrr(preds, labels, t_f)
elif task_name == "semeval2015":
if (type == "test"):
filename = task_name + "_test.tsv"
else:
filename = task_name + "_test.tsv"
t_f = read_data(filename=filename)
return map_and_mrr(preds, labels, t_f)
elif task_name == "dp":
if (type == "test"):
filename = task_name + "_test.tsv"
else:
filename = task_name + "_test.tsv"
t_f = read_data(filename=filename)
return map_and_mrr(preds, labels, t_f)
else:
raise KeyError(task_name)
def compute_metrics(task_name, preds, labels, type):
assert len(preds) == len(labels)
if task_name == "cola":
return {"mcc": matthews_corrcoef(labels, preds)}
elif task_name == "sst-2":
return {"acc": simple_accuracy(preds, labels)}
elif task_name == "mrpc":
return acc_and_f1(preds, labels)
elif task_name == "sts-b":
return pearson_and_spearman(preds, labels)
elif task_name == "qqp":
return acc_and_f1(preds, labels)
elif task_name == "mnli":
return {"acc": simple_accuracy(preds, labels)}
elif task_name == "mnli-mm":
return {"acc": simple_accuracy(preds, labels)}
elif task_name == "qnli":
return {"acc": simple_accuracy(preds, labels)}
elif task_name == "rte":
return {"acc": simple_accuracy(preds, labels)}
elif task_name == "wnli":
return {"acc": simple_accuracy(preds, labels)}
elif task_name == "trec":
if(type=="test"):
filename="/content/gdrive/My Drive/PyTorchBert/examples/Trec_test_not_aligned.txt"
else:
filename = "/content/gdrive/My Drive/PyTorchBert/examples/Trec_valid_not_aligned.txt"
t_f=read_data(filename=filename)
return map_and_mrr(preds, labels, t_f)
elif task_name == "wiki":
if type=="test":
filename="/content/gdrive/My Drive/PyTorchBert/examples/wikitestnewline.txt"
else:
filename = "/content/gdrive/My Drive/PyTorchBert/examples/wikidevnewline.txt"
t_f=read_data(filename=filename)
return map_and_mrr(preds, labels, t_f)
elif task_name == "yahoo":
if type=="test":
filename="/content/gdrive/My Drive/PyTorchBert/examples/yahoo_test.tsv"
else:
filename = "/content/gdrive/My Drive/PyTorchBert/examples/yahoo_dev.tsv"
t_f=read_data(filename=filename)
return map_and_mrr(preds, labels, t_f)
elif task_name == "semeval2016":
if type=="test":
filename="/content/gdrive/My Drive/PyTorchBert/examples/semeval_test_2016.tsv"
else:
filename = "/content/gdrive/My Drive/PyTorchBert/examples/semeval_dev_2016.tsv"
t_f=read_data(filename=filename)
return map_and_mrr(preds, labels, t_f)
elif task_name == "semeval2017":
if type=="test":
filename="/content/gdrive/My Drive/PyTorchBert/examples/semeval_test_2017.tsv"
else:
filename="/content/gdrive/My Drive/PyTorchBert/examples/semeval_dev_2017.tsv"
t_f=read_data(filename=filename)
return map_and_mrr(preds, labels, t_f)
elif task_name == "lrecmc":
return precision_recall_f1(preds,labels)
elif task_name == "lrecm":
return precision_recall_f1(preds,labels)
elif task_name == "lrec":
return {"acc": simple_accuracy(preds, labels)}
else:
raise KeyError(task_name)