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run_multiple_choice.py
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# -*- coding: UTF-8 -*-
import torch
import logging
import os
import json
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
Trainer, AdamW,
TrainingArguments,
set_seed,
)
from utils_multiple_choice import processors
from collections import Counter
from Logiformer import Logiformer
from tokenization import arg_tokenizer, our_tokenizer
from utils_multiple_choice import Split, MyMultipleChoiceDataset
from graph_building_blocks.argument_set_punctuation_v4 import punctuations
with open('./graph_building_blocks/explicit_arg_set_v4.json', 'r') as f:
relations = json.load(f) # key: relations, value: ignore
logger = logging.getLogger(__name__)
def simple_accuracy(preds, labels):
return (preds == labels).mean()
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
)
config_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
)
tokenizer_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
)
cache_dir: Optional[str] = field(
default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
)
dropout: float = field(
default=0.1,
metadata={"help": "huggingface RoBERTa config.attention_probs_dropout_prob"}
)
init_weights: bool = field(
default=False,
metadata={"help": "init weights in Argument NumNet."}
)
# training
roberta_lr: float = field(
default=5e-6,
metadata={"help": "learning rate for updating roberta parameters"}
)
gt_lr: float = field(
default=5e-6,
metadata={"help": "learning rate for updating gcn parameters"}
)
proj_lr: float = field(
default=5e-6,
metadata={"help": "learning rate for updating fc parameters"}
)
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
task_name: str = field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys())})
data_dir: str = field(metadata={"help": "Should contain the data files for the task."})
data_type: str = field(
default="argument_numnet",
metadata={
"help": "data types in utils script. roberta_large | argument_numnet "
}
)
max_seq_length: int = field(
default=128,
metadata={
"help": "The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
}
)
max_ngram: int = field(
default=5,
metadata={"help": "max ngram when pre-processing text and find argument/domain words."}
)
overwrite_cache: bool = field(
default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
)
demo_data: bool = field(
default=False,
metadata={"help": "demo data sets with 100 samples."}
)
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 = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
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."
)
# 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
set_seed(training_args.seed)
try:
processor = processors[data_args.task_name]()
label_list = processor.get_labels()
num_labels = len(label_list)
except KeyError:
raise ValueError("Task not found: %s" % (data_args.task_name))
config = 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 = 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,
)
if isinstance(relations, tuple): max_rel_id = int(max(relations[0].values()))
elif isinstance(relations, dict):
if not len(relations) == 0:
max_rel_id = int(max(relations.values()))
else:
max_rel_id = 0
else: raise Exception
model = Logiformer.from_pretrained(
model_args.model_name_or_path,
from_tf=bool(".ckpt" in model_args.model_name_or_path),
config=config,
# token_encoder_type="roberta" if "roberta" in model_args.model_name_or_path else "bert",
token_encoder_type="roberta",
init_weights=model_args.init_weights,
max_rel_id=max_rel_id,
cache_dir=model_args.cache_dir,
hidden_size=config.hidden_size,
dropout_prob=model_args.dropout,
)
train_dataset = (
MyMultipleChoiceDataset(
data_dir=data_args.data_dir,
tokenizer=tokenizer,
arg_tokenizer=arg_tokenizer,
relations=relations,
punctuations=punctuations,
task=data_args.task_name,
max_seq_length=data_args.max_seq_length,
overwrite_cache=data_args.overwrite_cache,
mode=Split.train,
demo=data_args.demo_data
)
if training_args.do_train
else None
)
eval_dataset = (
MyMultipleChoiceDataset(
data_dir=data_args.data_dir,
tokenizer=tokenizer,
arg_tokenizer=arg_tokenizer,
relations=relations,
punctuations=punctuations,
task=data_args.task_name,
max_seq_length=data_args.max_seq_length,
max_ngram=data_args.max_ngram,
overwrite_cache=data_args.overwrite_cache,
mode=Split.dev,
demo=data_args.demo_data
)
if training_args.do_eval
else None
)
test_dataset = (
MyMultipleChoiceDataset(
data_dir=data_args.data_dir,
tokenizer=tokenizer,
arg_tokenizer=arg_tokenizer,
relations=relations,
punctuations=punctuations,
task=data_args.task_name,
max_seq_length=data_args.max_seq_length,
max_ngram=data_args.max_ngram,
overwrite_cache=data_args.overwrite_cache,
mode=Split.test,
demo=data_args.demo_data
)
if training_args.do_predict
else None
)
def compute_metrics(p: EvalPrediction) -> Dict:
preds = np.argmax(p.predictions, axis=1)
return {"acc": simple_accuracy(preds, p.label_ids)}
no_decay = ["bias", "LayerNorm.weight"]
optimizer_grouped_parameters = [
{
"params": [p for n, p in model.named_parameters() if n.startswith("_gt")
and not any(nd in n for nd in no_decay)],
"lr": model_args.gt_lr,
"weight_decay": training_args.weight_decay,
},
{
"params": [p for n, p in model.named_parameters() if n.startswith("_gt")
and any(nd in n for nd in no_decay)],
"lr": model_args.gt_lr,
"weight_decay": 0.0,
},
{
"params": [p for n, p in model.named_parameters() if n.startswith("roberta")
and not any(nd in n for nd in no_decay)],
"lr": model_args.roberta_lr,
"weight_decay": training_args.weight_decay,
},
{
"params": [p for n, p in model.named_parameters() if n.startswith("roberta")
and any(nd in n for nd in no_decay)],
"lr": model_args.roberta_lr,
"weight_decay": 0.0,
},
{
"params": [p for n, p in model.named_parameters() if n.startswith("_proj")
and not any(nd in n for nd in no_decay)],
"lr": model_args.proj_lr,
"weight_decay": training_args.weight_decay,
},
{
"params": [p for n, p in model.named_parameters() if n.startswith("_proj")
and any(nd in n for nd in no_decay)],
"lr": model_args.proj_lr,
"weight_decay": 0.0,
}
]
optimizer = AdamW(
optimizer_grouped_parameters,
)
# Initialize our Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
compute_metrics=compute_metrics,
optimizers=(optimizer, None)
)
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.mode_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 =)
tokenizer.save_pretrained(training_args.output_dir)
# Evaluation
results = {}
if training_args.do_eval:
logger.info("*** Evaluate ***")
result = trainer.evaluate()
output_eval_file = os.path.join(training_args.output_dir, "eval_results.txt")
with open(output_eval_file, "w") as writer:
logger.info("***** Eval results *****")
for key, value in result.items():
logger.info(" %s = %s", key, value)
writer.write("%s = %s\n" % (key, value))
results.update(result)
eval_result = trainer.predict(eval_dataset)
preds = eval_result.predictions # np array. (1000, 4)
pred_ids = np.argmax(preds, axis=1)
output_test_file = os.path.join(training_args.output_dir, "eval_predictions.npy")
np.save(output_test_file, pred_ids)
logger.info("predictions saved to {}".format(output_test_file))
# Test
if training_args.do_predict:
if data_args.task_name == "reclor":
logger.info("*** Test ***")
test_result = trainer.predict(test_dataset)
preds = test_result.predictions # np array. (1000, 4)
pred_ids = np.argmax(preds, axis=1)
output_test_file = os.path.join(training_args.output_dir, "predictions.npy")
np.save(output_test_file, pred_ids)
logger.info("predictions saved to {}".format(output_test_file))
elif data_args.task_name == "logiqa":
logger.info("*** Test ***")
test_result = trainer.predict(test_dataset)
output_test_file = os.path.join(training_args.output_dir, "test_results.txt")
with open(output_test_file, "w") as writer:
logger.info("***** Test results *****")
for key, value in test_result.metrics.items():
logger.info(" %s = %s", key, value)
writer.write("%s = %s\n" % (key, value))
results.update(test_result.metrics)
def _mp_fn(index):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()