def train():
"""Trains a BERT ethicality classifer."""
args = transformers.TrainingArguments(
"saved_models",
evaluation_strategy="epoch",
learning_rate=config['learning_rate'],
per_device_train_batch_size=config['batch_size'],
per_device_eval_batch_size=config['batch_size'],
num_train_epochs=config['num_epochs'],
weight_decay=config['weight_decay'],
load_best_model_at_end=True,
metric_for_best_model="f1")
train, val, test = get_train_val_test_datasets()
trainer = transformers.Trainer(model=get_model(),
args=args,
train_dataset=train,
eval_dataset=val,
compute_metrics=metrics)
# Train the model.
trainer.train()
# Display model eval statistics.
print(trainer.evaluate())
# Test dataset metrics.
trainer.predict(test).metrics
def __init__(self):
"""Initializes a Inference object."""
# self.model = get_pretrained_model()
self.tokenizer = get_tokenizer()
self.model = transformers.Trainer(model=get_pretrained_model())
self.summarizer = pipeline(
"summarization") # ~1.2 GB download the first time this is run.
def _train_model(self, model, tokenizer, train_dataset, val_dataset, **train_kwargs):
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=True)
train_args = self._get_train_args(**train_kwargs)
trainer = transformers.Trainer(model=model,
args=train_args,
data_collator=data_collator,
train_dataset=train_dataset,
eval_dataset=val_dataset,
)
trainer.train()
def main():
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
parser = transformers.HfArgumentParser(
(ModelArguments, ynt.GenernalDataTrainingArguments,
transformers.TrainingArguments))
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
model_args, data_args, training_args = parser.parse_json_file(
json_file=os.path.abspath(sys.argv[1]))
else:
model_args, data_args, training_args = parser.parse_args_into_dataclasses(
)
if (os.path.exists(training_args.output_dir)
and os.listdir(training_args.output_dir) and training_args.do_train
and not training_args.overwrite_output_dir):
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome."
)
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
training_args.local_rank,
training_args.device,
training_args.n_gpu,
bool(training_args.local_rank != -1),
training_args.fp16,
)
logger.info("Training/evaluation parameters %s", training_args)
logger.info(f"Model arguments: {model_args}")
logger.info(f"Data Training arguments: {data_args}")
# Set seed
transformers.set_seed(training_args.seed)
if data_args.task_name in ynt.genernal_tasks_num_labels:
num_labels = ynt.genernal_tasks_num_labels[data_args.task_name]
output_mode = ynt.genernal_output_modes[data_args.task_name]
elif data_args.task_name in transformers.glue_tasks_num_labels:
num_labels = transformers.glue_tasks_num_labels[data_args.task_name]
output_mode = transformers.glue_output_modes[data_args.task_name]
else:
raise ValueError("Task not found: %s" % (data_args.task_name))
# Load pretrained model and tokenizer
config = transformers.AutoConfig.from_pretrained(
model_args.config_name
if model_args.config_name else model_args.model_name_or_path,
num_labels=num_labels,
finetuning_task=data_args.task_name,
cache_dir=model_args.cache_dir,
)
tokenizer = transformers.AutoTokenizer.from_pretrained(
model_args.tokenizer_name
if model_args.tokenizer_name else model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
)
model = transformers.AutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path,
from_tf=bool(".ckpt" in model_args.model_name_or_path),
config=config,
cache_dir=model_args.cache_dir,
)
if data_args.task_name in ynt.genernal_tasks_num_labels:
train_dataset = (ynt.GenernalDataset(data_args,
tokenizer=tokenizer,
mode='train',
cache_dir=model_args.cache_dir)
if training_args.do_train else None)
eval_dataset = (ynt.GenernalDataset(data_args,
tokenizer=tokenizer,
mode='dev',
cache_dir=model_args.cache_dir)
if training_args.do_eval and not data_args.online else
None)
test_dataset = (ynt.GenernalDataset(data_args,
tokenizer=tokenizer,
mode='test',
cache_dir=model_args.cache_dir)
if training_args.do_predict and not data_args.online
else None)
elif data_args.task_name in transformers.glue_tasks_num_labels:
# Get datasets
train_dataset = (transformers.GlueDataset(
data_args, tokenizer=tokenizer, cache_dir=model_args.cache_dir)
if training_args.do_train else None)
eval_dataset = (transformers.GlueDataset(
data_args,
tokenizer=tokenizer,
mode="dev",
cache_dir=model_args.cache_dir) if training_args.do_eval else None)
test_dataset = (transformers.GlueDataset(
data_args,
tokenizer=tokenizer,
mode="test",
cache_dir=model_args.cache_dir)
if training_args.do_predict else None)
def build_compute_metrics_fn(
task_name: str) -> Callable[[transformers.EvalPrediction], Dict]:
def compute_metrics_fn(p: transformers.EvalPrediction):
if output_mode == "classification":
preds = np.argmax(p.predictions, axis=1)
elif output_mode == "regression":
preds = np.squeeze(p.predictions)
if task_name in ynt.genernal_tasks_num_labels:
return ynt.genernal_compute_metrics(task_name, preds,
p.label_ids)
elif task_name in transformers.glue_tasks_num_labels:
return transformers.glue_compute_metrics(
task_name, preds, p.label_ids)
return compute_metrics_fn
# Initialize our Trainer
trainer = transformers.Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
compute_metrics=build_compute_metrics_fn(data_args.task_name),
)
# Training
if training_args.do_train:
trainer.train(model_path=model_args.model_name_or_path if os.path.
isdir(model_args.model_name_or_path) else None)
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir)
# Evaluation
eval_results = {}
if training_args.do_eval:
logger.info("*** Evaluate ***")
# Loop to handle MNLI double evaluation (matched, mis-matched)
eval_datasets = [eval_dataset]
if data_args.task_name == "mnli":
mnli_mm_data_args = dataclasses.replace(data_args,
task_name="mnli-mm")
eval_datasets.append(
GlueDataset(mnli_mm_data_args,
tokenizer=tokenizer,
mode="dev",
cache_dir=model_args.cache_dir))
for eval_dataset in eval_datasets:
trainer.compute_metrics = build_compute_metrics_fn(
eval_dataset.args.task_name)
eval_result = trainer.evaluate(eval_dataset=eval_dataset)
output_eval_file = os.path.join(
training_args.output_dir,
f"eval_results_{eval_dataset.args.task_name}.txt")
if trainer.is_world_master():
with open(output_eval_file, "w") as writer:
logger.info("***** Eval results {} *****".format(
eval_dataset.args.task_name))
for key, value in eval_result.items():
logger.info(" %s = %s", key, value)
writer.write("%s = %s\n" % (key, value))
eval_results.update(eval_result)
if training_args.do_predict:
logging.info("*** Test ***")
test_datasets = [test_dataset]
if data_args.task_name == "mnli":
mnli_mm_data_args = dataclasses.replace(data_args,
task_name="mnli-mm")
test_datasets.append(
transformers.GlueDataset(mnli_mm_data_args,
tokenizer=tokenizer,
mode="test",
cache_dir=model_args.cache_dir))
for test_dataset in test_datasets:
predictions = trainer.predict(
test_dataset=test_dataset).predictions
if output_mode == "classification":
predictions = np.argmax(predictions, axis=1)
output_test_file = os.path.join(
training_args.output_dir,
f"test_results_{test_dataset.args.task_name}.txt")
if trainer.is_world_master():
with open(output_test_file, "w") as writer:
logger.info("***** Test results {} *****".format(
test_dataset.args.task_name))
writer.write("index\tprediction\n")
for index, item in enumerate(predictions):
if output_mode == "regression":
writer.write("%d\t%3.3f\n" % (index, item))
else:
item = test_dataset.get_labels()[item]
writer.write("%d\t%s\n" % (index, item))
return eval_results
logging_dir=f"{_dir}/logging",
logging_steps=256,
dataloader_num_workers=64,
evaluation_strategy="steps",
eval_steps=256,
save_steps=256,
fp16=True,
fp16_opt_level="O3",
learning_rate=5e-4,
run_name=_dir,
)
model = transformers.AlbertForSequenceClassification.from_pretrained(
"albert-large-v2", num_labels=2)
tokenizer = transformers.AlbertTokenizerFast.from_pretrained("albert-large-v2")
data_collator = transformers.DataCollatorWithPadding(tokenizer=tokenizer,
pad_to_multiple_of=32)
trainer = transformers.Trainer(
args=args,
model=model,
tokenizer=tokenizer,
data_collator=data_collator,
train_dataset=train_dataset,
eval_dataset=test_dataset,
compute_metrics=compute_metrics,
)
# In[ ]:
trainer.train()
import argparse
import transformers
parser = argparse.ArgumentParser()
parser.add_argument('--vocab', type=str)
parser.add_argument('--model', type=str)
parser.add_argument('--data', type=str)
args = parser.parse_args()
tokenizer = transformers.BertTokenizer(vocab_file=args.vocab,
do_lower_case=False,
do_basic_tokenize=True)
model = transformers.BertForMaskedLM.from_pretrained(args.model)
dataset = transformers.LineByLineTextDataset(tokenizer=tokenizer,
file_path=args.data,
block_size=128)
data_collator = transformers.DataCollatorForLanguageModeling(
tokenizer=tokenizer, mlm=True, mlm_probability=0.15)
train_args = transformers.TrainingArguments(
per_device_eval_batch_size=16, output_dir=f"/tmp/echau18/{args.model}")
trainer = transformers.Trainer(model=model,
eval_dataset=dataset,
data_collator=data_collator,
prediction_loss_only=True,
args=train_args)
eval_output = trainer.evaluate()
print(eval_output)
def main(
mode: str,
num_examples_to_test: int = 5,
num_repetitions: int = 4,
) -> List[Dict[str, Any]]:
if mode not in ["only-correct", "only-incorrect"]:
raise ValueError(f"Unrecognized mode {mode}")
task_tokenizer, task_model = misc_utils.create_tokenizer_and_model(
constants.MNLI_MODEL_PATH)
train_dataset, eval_dataset = misc_utils.create_datasets(
task_name="mnli", tokenizer=task_tokenizer)
eval_instance_data_loader = misc_utils.get_dataloader(dataset=eval_dataset,
batch_size=1,
random=False)
output_mode = glue_output_modes["mnli"]
def build_compute_metrics_fn(task_name: str):
def compute_metrics_fn(p):
if output_mode == "classification":
preds = np.argmax(p.predictions, axis=1)
elif output_mode == "regression":
preds = np.squeeze(p.predictions)
return glue_compute_metrics(task_name, preds, p.label_ids)
return compute_metrics_fn
# Most of these arguments are placeholders
# and are not really used at all, so ignore
# the exact values of these.
trainer = transformers.Trainer(
model=task_model,
args=TrainingArguments(output_dir="./tmp-output",
per_device_train_batch_size=128,
per_device_eval_batch_size=128,
learning_rate=5e-5,
logging_steps=100),
data_collator=default_data_collator,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
compute_metrics=build_compute_metrics_fn("mnli"),
)
task_model.cuda()
num_examples_tested = 0
output_collections = []
for test_index, test_inputs in enumerate(eval_instance_data_loader):
if num_examples_tested >= num_examples_to_test:
break
# Skip when we only want cases of correction prediction but the
# prediction is incorrect, or vice versa
prediction_is_correct = misc_utils.is_prediction_correct(
trainer=trainer, model=task_model, inputs=test_inputs)
if mode == "only-correct" and prediction_is_correct is False:
continue
if mode == "only-incorrect" and prediction_is_correct is True:
continue
for k, v in test_inputs.items():
if isinstance(v, torch.Tensor):
test_inputs[k] = v.to(torch.device("cuda"))
# with batch-size 128, 1500 iterations is enough
for num_samples in range(700, 1300 + 1, 100): # 7 choices
for batch_size in [1, 2, 4, 8, 16, 32, 64, 128]: # 8 choices
for repetition in range(num_repetitions):
print(
f"Running #{test_index} "
f"N={num_samples} "
f"B={batch_size} "
f"R={repetition} takes ...",
end=" ")
with Timer() as timer:
s_test = one_experiment(
model=task_model,
train_dataset=train_dataset,
test_inputs=test_inputs,
batch_size=batch_size,
random=True,
n_gpu=1,
device=torch.device("cuda"),
damp=constants.DEFAULT_INFLUENCE_HPARAMS["mnli"]
["mnli"]["damp"],
scale=constants.DEFAULT_INFLUENCE_HPARAMS["mnli"]
["mnli"]["scale"],
num_samples=num_samples)
time_elapsed = timer.elapsed
print(f"{time_elapsed:.2f} seconds")
outputs = {
"test_index": test_index,
"num_samples": num_samples,
"batch_size": batch_size,
"repetition": repetition,
"s_test": s_test,
"time_elapsed": time_elapsed,
"correct": prediction_is_correct,
}
output_collections.append(outputs)
remote_utils.save_and_mirror_scp_to_remote(
object_to_save=outputs,
file_name=f"stest.{mode}.{num_examples_to_test}."
f"{test_index}.{num_samples}."
f"{batch_size}.{repetition}.pth")
num_examples_tested += 1
return output_collections
def main(
train_task_name: str,
train_heuristic: str,
eval_heuristics: Optional[List[str]] = None,
num_replicas: Optional[int] = None,
use_parallel: bool = True,
version: Optional[str] = None,
) -> Dict[str, List[Dict[str, Any]]]:
if train_task_name not in ["mnli-2", "hans"]:
raise ValueError
if eval_heuristics is None:
eval_heuristics = DEFAULT_EVAL_HEURISTICS
if num_replicas is None:
num_replicas = DEFAULT_NUM_REPLICAS
if version not in ["new-only-z", "new-only-ztest", "new-z-and-ztest"]:
raise ValueError
task_tokenizer, task_model = misc_utils.create_tokenizer_and_model(
constants.MNLI2_MODEL_PATH)
(mnli_train_dataset,
mnli_eval_dataset) = misc_utils.create_datasets(task_name="mnli-2",
tokenizer=task_tokenizer)
(hans_train_dataset,
hans_eval_dataset) = misc_utils.create_datasets(task_name="hans",
tokenizer=task_tokenizer)
if train_task_name == "mnli-2":
train_dataset = mnli_train_dataset
if train_task_name == "hans":
train_dataset = hans_train_dataset
(s_test_damp, s_test_scale,
s_test_num_samples) = influence_helpers.select_s_test_config(
trained_on_task_name="mnli-2",
train_task_name=train_task_name,
eval_task_name="hans",
)
hans_helper = HansHelper(hans_train_dataset=hans_train_dataset,
hans_eval_dataset=hans_eval_dataset)
# We will be running model trained on MNLI-2
# but calculate influences on HANS dataset
faiss_index = influence_helpers.load_faiss_index(
trained_on_task_name="mnli-2", train_task_name=train_task_name)
output_mode = glue_output_modes["mnli-2"]
def build_compute_metrics_fn(task_name: str):
def compute_metrics_fn(p):
if output_mode == "classification":
preds = np.argmax(p.predictions, axis=1)
elif output_mode == "regression":
preds = np.squeeze(p.predictions)
return glue_compute_metrics(task_name, preds, p.label_ids)
return compute_metrics_fn
# Most of these arguments are placeholders
# and are not really used at all, so ignore
# the exact values of these.
trainer = transformers.Trainer(
model=task_model,
args=TrainingArguments(output_dir="./tmp-output",
per_device_train_batch_size=128,
per_device_eval_batch_size=128,
learning_rate=5e-5,
logging_steps=100),
)
output_collections: Dict[str, List] = defaultdict(list)
if version == "old":
raise ValueError("Deprecated")
else:
NUM_STEPS = 10
num_total_experiments = (len(EXPERIMENT_TYPES) * num_replicas *
len(VERSION_2_NUM_DATAPOINTS_CHOICES) *
len(VERSION_2_LEARNING_RATE_CHOICES) *
NUM_STEPS)
with tqdm(total=num_total_experiments) as pbar:
for experiment_type in EXPERIMENT_TYPES:
for replica_index in range(num_replicas):
(hans_eval_heuristic_inputs, hans_eval_heuristic_raw_inputs
) = hans_helper.sample_batch_of_heuristic(
mode="eval",
heuristic=train_heuristic,
size=EVAL_HEURISTICS_SAMPLE_BATCH_SIZE,
return_raw_data=True)
misc_utils.move_inputs_to_device(
inputs=hans_eval_heuristic_inputs,
device=task_model.device)
for version_2_num_datapoints in VERSION_2_NUM_DATAPOINTS_CHOICES:
for version_2_learning_rate in VERSION_2_LEARNING_RATE_CHOICES:
# The model will be used for multiple
# steps so `deepcopy` it here.
_model = deepcopy(task_model)
for step in range(NUM_STEPS):
outputs_one_experiment, _model = one_experiment(
use_parallel=use_parallel,
train_heuristic=train_heuristic,
eval_heuristics=eval_heuristics,
experiment_type=experiment_type,
hans_helper=hans_helper,
train_dataset=train_dataset,
task_model=_model,
faiss_index=faiss_index,
s_test_damp=s_test_damp,
s_test_scale=s_test_scale,
s_test_num_samples=s_test_num_samples,
trainer=trainer,
version=version,
version_2_num_datapoints=
version_2_num_datapoints,
version_2_learning_rate=
version_2_learning_rate,
hans_eval_heuristic_inputs=
hans_eval_heuristic_inputs,
hans_eval_heuristic_raw_inputs=
hans_eval_heuristic_raw_inputs,
)
output_collections[
f"{experiment_type}-"
f"{replica_index}-"
f"{version_2_num_datapoints}-"
f"{version_2_learning_rate}-"].append(
outputs_one_experiment)
pbar.update(1)
pbar.set_description(
f"{experiment_type} #{replica_index}")
torch.save(
output_collections, f"hans-augmentation-{version}."
f"{train_task_name}."
f"{train_heuristic}."
f"{num_replicas}."
f"{use_parallel}.pth")
return output_collections
writer = SummaryWriter()
training_args = transformers.TrainingArguments(
output_dir="models/gpt2/",
do_train=True,
do_eval=True,
evaluate_during_training=True,
per_device_train_batch_size=32,
per_device_eval_batch_size=32,
num_train_epochs=1,
logging_first_step=True,
save_steps=2000,
save_total_limit=2,
)
trainer = transformers.Trainer(
model=model,
args=training_args,
data_collator=data_collator,
train_dataset=train_set,
eval_dataset=valid_set,
prediction_loss_only=True,
tb_writer=writer
)
trainer.train()
# Save Model
trainer.save_model("models/gpt2/")
def train(args):
logging.basicConfig(level=logging.INFO)
tokenizer = transformers.AlbertTokenizer.from_pretrained(
'albert-base-v2', cache_dir=cache_dir)
albert_for_math_config = transformers.AlbertConfig(
hidden_size=768,
num_attention_heads=12,
intermediate_size=3072,
)
if args['--load']:
model = transformers.AlbertForMaskedLM.from_pretrained(
args['--load-from'])
training_args = transformers.TrainingArguments(
output_dir=args['--save-to'],
overwrite_output_dir=True,
num_train_epochs=int(args['--max-epoch']),
per_gpu_train_batch_size=int(args['--batch-size']),
per_gpu_eval_batch_size=int(args['--batch-size']),
logging_steps=int(args['--log-every']),
save_steps=int(args['--save-every']),
save_total_limit=10,
learning_rate=float(args['--lr']),
seed=int(args['--seed']),
)
else:
model = transformers.AlbertForMaskedLM(albert_for_math_config)
training_args = transformers.TrainingArguments(
output_dir=args['--save-to'],
num_train_epochs=int(args['--max-epoch']),
per_gpu_train_batch_size=int(args['--batch-size']),
per_gpu_eval_batch_size=int(args['--batch-size']),
logging_steps=int(args['--log-every']),
save_steps=int(args['--save-every']),
save_total_limit=10,
learning_rate=float(args['--lr']),
seed=int(args['--seed']),
)
#load datasets
print('Loading Data...')
train_data = torch.load(
'./data/train_data_train-easy_algebra__linear_1d.pt')
dev_data = torch.load('./data/dev_data_train-easy_algebra__linear_1d.pt')
print('Finished loading data')
device = torch.device("cuda:0" if args['--cuda'] else "cpu")
model.to(device)
trainer = transformers.Trainer(
model=model,
args=training_args,
data_collator=AnswerMaskDataCollator(tokenizer),
train_dataset=train_data,
eval_dataset=dev_data,
prediction_loss_only=True,
)
if args['--load']:
trainer.train(model_path=args['--load-from'])
else:
trainer.train()
from cfg import config from data import get_train_val_test_datasets from models import get_model from utils import metrics args = transformers.TrainingArguments( "saved_models", evaluation_strategy = "epoch", learning_rate=config['learning_rate'], per_device_train_batch_size=config['batch_size'], per_device_eval_batch_size=config['batch_size'], num_train_epochs=config['num_epochs'], weight_decay=config['weight_decay'], load_best_model_at_end=True, metric_for_best_model="f1" ) train, val, test = get_train_val_test_datasets() trainer = transformers.Trainer(model=get_model(), args=args, train_dataset=train, eval_dataset=val, compute_metrics=metrics) # Train the model. trainer.train() # Display model eval statistics. print(trainer.evaluate()) # Test dataset metrics. trainer.predict(test).metrics
def main():
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
parser = transformers.HfArgumentParser(
(ModelArguments, DataTrainingArguments,
transformers.TrainingArguments))
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
if data_args.eval_data_file is None and training_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 (os.path.exists(training_args.output_dir)
and os.listdir(training_args.output_dir) and training_args.do_train
and not training_args.overwrite_output_dir):
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome."
)
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO
if training_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",
training_args.local_rank,
training_args.device,
training_args.n_gpu,
bool(training_args.local_rank != -1),
training_args.fp16,
)
logger.info("Training/evaluation parameters %s", training_args)
# Set seed
transformers.set_seed(training_args.seed)
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
if model_args.config_name:
config = transformers.AutoConfig.from_pretrained(
model_args.config_name, cache_dir=model_args.cache_dir)
elif model_args.model_name_or_path:
config = transformers.AutoConfig.from_pretrained(
model_args.model_name_or_path, cache_dir=model_args.cache_dir)
else:
config = transformers.CONFIG_MAPPING[model_args.model_type]()
logger.warning(
"You are instantiating a new config instance from scratch.")
if model_args.tokenizer_name:
tokenizer = transformers.AutoTokenizer.from_pretrained(
model_args.tokenizer_name, cache_dir=model_args.cache_dir)
elif model_args.model_name_or_path:
tokenizer = transformers.AutoTokenizer.from_pretrained(
model_args.model_name_or_path, cache_dir=model_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 model_args.model_name_or_path:
model = transformers.AutoModelForPreTraining.from_pretrained(
model_args.model_name_or_path,
from_tf=bool(".ckpt" in model_args.model_name_or_path),
config=config,
cache_dir=model_args.cache_dir,
)
else:
logger.info("Training new model from scratch")
model = transformers.AutoModelWithLMHead.from_config(config)
model.resize_token_embeddings(len(tokenizer))
if config.model_type in ["bert", "roberta", "distilbert", "camembert"
] and not data_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 data_args.block_size <= 0:
data_args.block_size = tokenizer.max_len
# Our input block size will be the max possible for the model
else:
data_args.block_size = min(data_args.block_size, tokenizer.max_len)
# Get datasets
train_dataset = get_dataset(
data_args, tokenizer=tokenizer) if training_args.do_train else None
eval_dataset = get_dataset(
data_args, tokenizer=tokenizer,
evaluate=True) if training_args.do_eval else None
data_collator = transformers.DataCollatorForLanguageModeling(
tokenizer=tokenizer,
mlm=data_args.mlm,
mlm_probability=data_args.mlm_probability)
# Initialize our Trainer
trainer = transformers.Trainer(
model=model,
args=training_args,
data_collator=data_collator,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
prediction_loss_only=True,
)
# Training
if training_args.do_train:
model_path = (model_args.model_name_or_path
if model_args.model_name_or_path is not None
and os.path.isdir(model_args.model_name_or_path) else
None)
trainer.train(model_path=model_path)
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir)
# Evaluation
results = {}
if training_args.do_eval:
logger.info("*** Evaluate ***")
eval_output = trainer.evaluate()
perplexity = math.exp(eval_output["eval_loss"])
result = {"perplexity": perplexity}
output_eval_file = os.path.join(training_args.output_dir,
"eval_results_lm.txt")
if trainer.is_world_master():
with open(output_eval_file, "w") as writer:
logger.info("***** Eval results *****")
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
results.update(result)
return results
# Load data
dlnd_train_dset, dlnd_valid_dset, dlnd_test_dset = DlndData(
).return_datasets()
# Load model
model = create_model()
# Training
training_args = transformers.TrainingArguments(
evaluation_strategy='epoch',
load_best_model_at_end=True,
logging_dir='training_logs',
logging_first_step=True,
logging_steps=10,
num_train_epochs=10,
output_dir='training_results',
per_device_eval_batch_size=BATCH_SIZE,
per_device_train_batch_size=BATCH_SIZE,
weight_decay=0.01,
metric_for_best_model='accuracy',
disable_tqdm=True,
)
trainer = transformers.Trainer(
model=model,
args=training_args,
compute_metrics=compute_metrics,
train_dataset=dlnd_train_dset,
eval_dataset=dlnd_valid_dset,
callbacks=[LogCallback],
)
trainer.train()
trainer.evaluate()
def run_training(args, train_data):
## Checkpoint Loading ########################################################
if args.load:
if '2700' in args.load:
model = transformers.GPTNeoForCausalLM.from_pretrained(args.load)
else:
model = transformers.GPT2LMHeadModel.from_pretrained(args.load)
print(f"Loaded model from {args.load}")
else:
if "EleutherAI" in args.arch:
model = transformers.GPTNeoForCausalLM.from_pretrained(args.arch)
else:
model = transformers.GPT2LMHeadModel.from_pretrained(args.arch)
if args.resume:
raise NotImplementedError
model = transformers.GPT2LMHeadModel.from_pretrained(args.resume)
print(f"Loaded model from {args.resume}")
start_epoch = 0
start_iteration = int(args.resume.split("-")[-1])
print("start_iteration = ", start_iteration)
else:
start_iteration = 0
## Dataloading ########################################################
train_data.start_iteration = start_iteration
## Start Loop ########################################################
print(f"Starting main loop")
training_args = transformers.TrainingArguments(
output_dir=args.save_dir,
overwrite_output_dir=False,
do_train=True,
do_eval=False,
do_predict=True,
evaluation_strategy='no',
eval_steps=0,
num_train_epochs=args.epochs,
per_device_train_batch_size=args.batch_size_per_replica,
gradient_accumulation_steps=args.grad_acc_steps,
learning_rate=args.lr,
weight_decay=0.05,
# warmup_steps=args.lr_warmup_steps,
# max_grad_norm=100000.0,
logging_dir=args.save_dir,
logging_first_step=True,
logging_steps=args.log_freq,
save_steps=args.save_freq,
save_total_limit=2,
dataloader_drop_last=True,
dataloader_num_workers=3,
local_rank=args.local_rank,
deepspeed=args.deepspeed,
fp16=args.fp16,
)
trainer = transformers.Trainer(
model=model,
args=training_args,
train_dataset=train_data,
)
trainer.remove_callback(transformers.integrations.TensorBoardCallback)
trainer.add_callback(CustomTensorBoardCallback())
trainer.train()
if args.local_rank == 0:
model.save_pretrained(os.path.join(args.save_dir, "final_checkpoint"))
def run_full_influence_functions(
mode: str,
num_examples_to_test: int,
s_test_num_samples: int = 1000) -> Dict[int, Dict[str, Any]]:
if mode not in ["only-correct", "only-incorrect"]:
raise ValueError(f"Unrecognized mode {mode}")
tokenizer, model = misc_utils.create_tokenizer_and_model(
constants.MNLI_MODEL_PATH)
(mnli_train_dataset,
mnli_eval_dataset) = misc_utils.create_datasets(task_name="mnli",
tokenizer=tokenizer)
batch_train_data_loader = misc_utils.get_dataloader(mnli_train_dataset,
batch_size=128,
random=True)
instance_train_data_loader = misc_utils.get_dataloader(mnli_train_dataset,
batch_size=1,
random=False)
eval_instance_data_loader = misc_utils.get_dataloader(
dataset=mnli_eval_dataset, batch_size=1, random=False)
output_mode = glue_output_modes["mnli"]
def build_compute_metrics_fn(task_name: str):
def compute_metrics_fn(p):
if output_mode == "classification":
preds = np.argmax(p.predictions, axis=1)
elif output_mode == "regression":
preds = np.squeeze(p.predictions)
return glue_compute_metrics(task_name, preds, p.label_ids)
return compute_metrics_fn
# Most of these arguments are placeholders
# and are not really used at all, so ignore
# the exact values of these.
trainer = transformers.Trainer(
model=model,
args=TrainingArguments(output_dir="./tmp-output",
per_device_train_batch_size=128,
per_device_eval_batch_size=128,
learning_rate=5e-5,
logging_steps=100),
data_collator=default_data_collator,
train_dataset=mnli_train_dataset,
eval_dataset=mnli_eval_dataset,
compute_metrics=build_compute_metrics_fn("mnli"),
)
params_filter = [
n for n, p in model.named_parameters() if not p.requires_grad
]
weight_decay_ignores = ["bias", "LayerNorm.weight"] + [
n for n, p in model.named_parameters() if not p.requires_grad
]
model.cuda()
num_examples_tested = 0
outputs_collections = {}
for test_index, test_inputs in enumerate(eval_instance_data_loader):
if num_examples_tested >= num_examples_to_test:
break
# Skip when we only want cases of correction prediction but the
# prediction is incorrect, or vice versa
prediction_is_correct = misc_utils.is_prediction_correct(
trainer=trainer, model=model, inputs=test_inputs)
if mode == "only-correct" and prediction_is_correct is False:
continue
if mode == "only-incorrect" and prediction_is_correct is True:
continue
with Timer() as timer:
influences, _, s_test = nn_influence_utils.compute_influences(
n_gpu=1,
device=torch.device("cuda"),
batch_train_data_loader=batch_train_data_loader,
instance_train_data_loader=instance_train_data_loader,
model=model,
test_inputs=test_inputs,
params_filter=params_filter,
weight_decay=constants.WEIGHT_DECAY,
weight_decay_ignores=weight_decay_ignores,
s_test_damp=5e-3,
s_test_scale=1e4,
s_test_num_samples=s_test_num_samples,
train_indices_to_include=None,
s_test_iterations=1,
precomputed_s_test=None)
outputs = {
"test_index": test_index,
"influences": influences,
"s_test": s_test,
"time": timer.elapsed,
"correct": prediction_is_correct,
}
num_examples_tested += 1
outputs_collections[test_index] = outputs
remote_utils.save_and_mirror_scp_to_remote(
object_to_save=outputs,
file_name=
f"KNN-recall.{mode}.{num_examples_to_test}.{test_index}.pth")
print(
f"Status: #{test_index} | {num_examples_tested} / {num_examples_to_test}"
)
return outputs_collections
training_arg = transformers.TrainingArguments(num_train_epochs=8,
learning_rate=5e-5,
output_dir='scratch/adv312/',
evaluation_strategy="epoch",
per_device_train_batch_size=8)
## TODO: Initialize a transformers.Trainer object and run a Bayesian
## hyperparameter search for at least 5 trials (but not too many) on the
## learning rate. Hint: use the model_init() and
## compute_metrics() methods from finetuning_utils.py as arguments to
## Trainer().
trainer = transformers.Trainer(
model_init=finetuning_utils.model_init,
args=training_arg,
compute_metrics=finetuning_utils.compute_metrics,
train_dataset=train_data)
##Use the hp_space parameter in hyperparameter_search() to specify
## your hyperparameter search space. (Note that this parameter takes a function
## as its value.)
bestrun = trainer.hyperparameter_search(
hp_space=lambda _: {"learning rate": tune.uniform(1e-5, 5e-5)},
n_trials=3,
search_alg=BayesOptSearch(),
metric='eval_loss',
mode='min')
## Also print out the run ID, objective value
def imitator_main(mode: str,
num_examples_to_test: int) -> List[Dict[str, Any]]:
if mode not in ["only-correct", "only-incorrect"]:
raise ValueError(f"Unrecognized mode {mode}")
task_tokenizer, task_model = misc_utils.create_tokenizer_and_model(
constants.MNLI_MODEL_PATH)
imitator_tokenizer, imitator_model = misc_utils.create_tokenizer_and_model(
constants.MNLI_IMITATOR_MODEL_PATH)
(mnli_train_dataset,
mnli_eval_dataset) = misc_utils.create_datasets(task_name="mnli",
tokenizer=task_tokenizer)
task_model.cuda()
imitator_model.cuda()
if task_model.training is True or imitator_model.training is True:
raise ValueError("One of the model is in training mode")
print(task_model.device, imitator_model.device)
# Most of these arguments are placeholders
# and are not really used at all, so ignore
# the exact values of these.
trainer = transformers.Trainer(
model=task_model,
args=TrainingArguments(output_dir="./tmp-output",
per_device_train_batch_size=128,
per_device_eval_batch_size=128,
learning_rate=5e-5,
logging_steps=100),
)
eval_instance_data_loader = misc_utils.get_dataloader(
mnli_eval_dataset, batch_size=1, data_collator=default_data_collator)
train_inputs_collections = torch.load(
constants.MNLI_TRAIN_INPUT_COLLECTIONS_PATH)
inputs_by_label: Dict[str, List[int]] = defaultdict(list)
for i in range(len(train_inputs_collections)):
label = mnli_train_dataset.label_list[train_inputs_collections[i]
["labels"]]
inputs_by_label[label].append(i)
outputs_collections = []
for i, test_inputs in enumerate(eval_instance_data_loader):
if mode == "only-correct" and i not in CORRECT_INDICES[:
num_examples_to_test]:
continue
if mode == "only-incorrect" and i not in INCORRECT_INDICES[:
num_examples_to_test]:
continue
start_time = time.time()
for using_ground_truth in [True, False]:
outputs = run_one_imitator_experiment(
task_model=task_model,
imitator_model=imitator_model,
test_inputs=test_inputs,
trainer=trainer,
train_dataset=mnli_train_dataset,
train_inputs_collections=train_inputs_collections,
inputs_by_label=inputs_by_label,
finetune_using_ground_truth_label=using_ground_truth)
outputs["index"] = i
outputs_collections.append(outputs)
end_time = time.time()
print(f"#{len(outputs_collections)}/{len(outputs_collections)}: "
f"Elapsed {(end_time - start_time) / 60:.2f}")
torch.save(outputs_collections,
f"imiator_experiments.{mode}.{num_examples_to_test}.pt")
return outputs_collections
def train_bert(corpus_path, hebrew_model=False):
"""
Bert model training
:param corpus_path: Corpus to train Bert on
:param hebrew_model: Model in Hebrew or not
:return: The name of the new trained model
"""
language = 'hebrew' if hebrew_model else 'english'
df = pd.read_csv(corpus_path)
corpus_name = get_corpus_name(corpus_path)
print("Preprocess...")
if hebrew_model:
model_name, vocab, raw_text_file = preprocess_hebrew(df, corpus_name)
else:
model_name, vocab, raw_text_file = preprocess_english(df, corpus_name)
pass
print("Cuda availability :", torch.cuda.is_available())
print("Getting tokenizer...")
tokenizer = transformers.AutoTokenizer.from_pretrained(conf.bert_model[language], use_fast=True)
model = transformers.AutoModelForMaskedLM.from_pretrained(conf.bert_model[language]).to('cuda')
tokenizer.add_tokens(vocab)
model.resize_token_embeddings(len(tokenizer))
if os.path.exists(conf.MODELS_PATH + conf.TYPE_MODEL_PATH['bert'] + model_name):
shutil.rmtree(conf.MODELS_PATH + conf.TYPE_MODEL_PATH['bert'] + model_name)
os.mkdir(conf.MODELS_PATH + conf.TYPE_MODEL_PATH['bert'] + model_name)
tokenizer.save_pretrained(conf.MODELS_PATH + conf.TYPE_MODEL_PATH['bert'] + model_name)
print("Tokenizing...")
dataset = transformers.LineByLineTextDataset(
tokenizer=tokenizer,
file_path=raw_text_file,
block_size=128,
)
data_collator = transformers.DataCollatorForLanguageModeling(
tokenizer=tokenizer, mlm=True, mlm_probability=0.15
)
training_args = transformers.TrainingArguments(
output_dir=conf.MODELS_PATH + conf.TYPE_MODEL_PATH['bert'] + model_name,
overwrite_output_dir=True,
num_train_epochs=20,
per_device_train_batch_size=16,
save_steps=300,
logging_steps=100,
save_total_limit=3,
)
trainer = transformers.Trainer(
model=model,
args=training_args,
data_collator=data_collator,
train_dataset=dataset
)
print("Begin training...")
os.environ["TOKENIZERS_PARALLELISM"] = "false"
trainer.train()
trainer.save_model(conf.MODELS_PATH + conf.TYPE_MODEL_PATH['bert'] + model_name)
print('The model has been saved under : ', conf.MODELS_PATH + conf.TYPE_MODEL_PATH['bert'] + model_name)
return conf.MODELS_PATH + conf.TYPE_MODEL_PATH['bert'] + model_name
def testModel(self,
train_val_split_iterator: typing.Iterator = [
sklearn.model_selection.train_test_split
],
**kwargs):
logger.info("Starting testing of RobertaModel")
num_epochs = kwargs['epochs']
batch_size = kwargs['batch_size']
for i, train_test_split in enumerate(train_val_split_iterator):
logger.debug(
f'{i}-th enumeration of train_val split iterator under cross validation'
)
self.model = self.createModel()
# optimizer = tf.keras.optimizers.Adam(learning_rate=3e-5, epsilon=1e-08, clipnorm=1.0)
# loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
if callable(getattr(self.model, 'compile', None)): # if tf model
train_dataset, val_dataset = self.pipeLine.getEncodedDataset(
train_test_split, batch_size=batch_size)
# self.model.compile(optimizer=optimizer, loss=loss, metrics=self._registeredMetrics)
# self.model.fit(train_dataset, epochs=num_epochs)
training_args = transformers.TFTrainingArguments(
output_dir=
f'./results/{self._modelName}', # output directory
num_train_epochs=
num_epochs, # total number of training epochs
per_device_train_batch_size=
batch_size, # batch size per device during training
per_device_eval_batch_size=
batch_size, # batch size for evaluation
warmup_steps=kwargs[
'warmup_steps'], # number of warmup steps for learning rate scheduler
weight_decay=kwargs[
'weight_decay'], # strength of weight decay
logging_dir='./logs', # directory for storing logs
)
trainer = transformers.TFTrainer(
model=self.
model, # the instantiated 🤗 Transformers model to be trained
args=training_args, # training arguments, defined above
train_dataset=
train_dataset, # tensorflow_datasets training dataset
eval_dataset=
val_dataset, # tensorflow_datasets evaluation dataset
compute_metrics=get_compute_metrics(
self._registeredMetrics
) # metrics to compute while training
)
else: # if pytorch model
train_dataset, val_dataset = self.pipeLine.getEncodedDataset(
train_test_split,
batch_size=batch_size,
tfOrPyTorch=torchOrTFEnum.TORCH)
training_args = transformers.TrainingArguments(
output_dir=
f'./results/{self._modelName}', # output directory
num_train_epochs=
num_epochs, # total number of training epochs
per_device_train_batch_size=
batch_size, # batch size per device during training
per_device_eval_batch_size=
batch_size, # batch size for evaluation
warmup_steps=kwargs[
'warmup_steps'], # number of warmup steps for learning rate scheduler
weight_decay=kwargs[
'weight_decay'], # strength of weight decay
logging_dir='./logs', # directory for storing logs
logging_steps=10,
)
trainer = transformers.Trainer(
model=self.
model, # the instantiated 🤗 Transformers model to be trained
args=training_args, # training arguments, defined above
train_dataset=train_dataset, # training dataset
eval_dataset=val_dataset, # evaluation dataset
compute_metrics=get_compute_metrics(
self._registeredMetrics
) # metrics to compute while training
)
trainer.train()
