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train.py
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import os
import sys
import torch
import logging
import random
import numpy as np
from datasets import load_dataset, load_metric
from transformers.trainer_utils import get_last_checkpoint
from transformers import (
set_seed,
AutoTokenizer,
Trainer,
TrainingArguments,
DataCollatorWithPadding,
EvalPrediction,
default_data_collator,
)
from openprompt.data_utils.utils import InputExample
from openprompt import PromptDataLoader, PromptForClassification
from openprompt.plms import load_plm
from openprompt.prompts import SoftTemplate, ManualVerbalizer
from prompt_hub import task_to_keys, get_model
from prompt_hub.hub import PromptHub
from prompt_hub.training_args import PromptTrainingArguments, RemainArgHfArgumentParser
logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO)
logger = logging.getLogger(__name__)
def main():
# See all possible arguments in src/transformers/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 = RemainArgHfArgumentParser((PromptTrainingArguments))
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.
json_file=os.path.abspath(sys.argv[1])
args, _ = parser.parse_json_file(json_file, return_remaining_args=True) #args = arg_string, return_remaining_strings=True) #parse_json_file(json_file=os.path.abspath(sys.argv[1]))
else:
args = parser.parse_args_into_dataclasses()[0]
set_seed(args.seed)
# Dataset
is_regression = args.dataset in ['stsb']
# raw_dataset = load_dataset("glue", self.args.dataset)
# train_dataset = [InputExample(guid=e['idx'], text_a=e['question'], text_b=e['sentence'], label=e['label']) for e in raw_dataset['train']]#[:100]
# eval_dataset = [InputExample(guid=e['idx'], text_a=e['question'], text_b=e['sentence'], label=e['label']) for e in raw_dataset['validation']]#[:100]
# Model
# plm, tokenizer, model_config, tokenizer_wrapper_class = load_plm('roberta', args.backbone)
# template = '{"soft": None, "duplicate": ' + str(args.prompt_len) + ', "same": True} {"mask"} {"placeholder": "text_a"} {"placeholder": "text_b"}'
# template = SoftTemplate(model=plm, text=template, tokenizer=tokenizer, num_tokens=args.prompt_len) # initialize_from_vocab=args.init_from_vocab
# verbalizer = ManualVerbalizer(tokenizer, classes=raw_dataset['train'].features['label'].names).from_file(f'verbalizer/{args.dataset}.txt', choice=0)
# model = PromptForClassification(plm=plm, template=template, verbalizer=verbalizer, freeze_plm=True)
metric = load_metric("prompt_hub/glue_metrics.py", args.dataset)
def compute_metrics(p: EvalPrediction):
preds = p.predictions[0] if isinstance(p.predictions, tuple) else p.predictions
preds = np.squeeze(preds) if is_regression else np.argmax(preds, axis=1)
result = metric.compute(predictions=preds, references=p.label_ids)
result["combined_score"] = np.mean(list(result.values())).item()
return result
# Train
trainer = PromptHub(
args=args,
compute_metrics=compute_metrics,
)
train_results = trainer.train_prompt(args.backbone, args.dataset)
print(train_results)
eval_results = trainer.eval_prompt(args.backbone, args.dataset)
print(eval_results)
cross_task_results = trainer.cross_task_eval(args.backbone, 'rotten_tomatoes')
print(cross_task_results)
trainer.cross_model_train(args.backbone, 'roberta-large', args.dataset)
trainer.cross_task_eval(args.backbone, 'roberta-large', args.dataset)
# Trainer
# data_collator = DataCollatorWithPadding(tokenizer, max_length=args.max_source_length, pad_to_multiple_of=8)
# model=model,
# template=template,
# verbalizer=verbalizer,
# tokenizer_wrapper_class=tokenizer_wrapper_class,
# train_dataset=train_dataset,
# eval_dataset=eval_dataset,
# tokenizer=tokenizer,
# classes=raw_dataset['train'].features['label'].names
# data_collator=data_collator
# if args.do_train:
# # Detecting last checkpoint.
# last_checkpoint = None
# if os.path.isdir(args.output_dir) and args.do_train and not args.overwrite_output_dir:
# last_checkpoint = get_last_checkpoint(args.output_dir)
# if last_checkpoint is None and len(os.listdir(args.output_dir)) > 0:
# raise ValueError(
# f"Output directory ({args.output_dir}) already exists and is not empty. "
# "Use --overwrite_output_dir to overcome."
# )
# elif last_checkpoint is not None and args.resume_from_checkpoint is None:
# logger.info(
# f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
# "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
# )
# train_result = trainer.train(resume_from_checkpoint=last_checkpoint)
# metrics = train_result.metrics
# # metrics["train_samples"] = min(max_train_samples, len(train_dataset))
# trainer.save_model() # Saves the tokenizer too for easy upload
# trainer.log_metrics("train", metrics)
# trainer.save_metrics("train", metrics)
# trainer.save_state()
# results = {}
# # Evaluation
# if args.do_eval:
# logger.info("*** Evaluate ***")
# # Loop to handle MNLI double evaluation (matched, mis-matched)
# tasks = [data_args.task_name]
# eval_datasets = [eval_dataset]
# if data_args.task_name == "mnli":
# tasks.append("mnli-mm")
# eval_datasets.append(raw_datasets["validation_mismatched"])
# for eval_dataset, task in zip(eval_datasets, tasks):
# metrics = trainer.evaluate(eval_dataset=eval_dataset)
# max_eval_samples = (
# data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)
# )
# metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
# trainer.log_metrics("eval", metrics)
# trainer.save_metrics("eval", metrics)
# results['eval'] = metrics
# if args.do_predict:
# logger.info("*** Predict ***")
# # Loop to handle MNLI double evaluation (matched, mis-matched)
# tasks = [data_args.task_name]
# predict_datasets = [predict_dataset]
# if data_args.task_name == "mnli":
# tasks.append("mnli-mm")
# predict_datasets.append(raw_datasets["test_mismatched"])
# for predict_dataset, task in zip(predict_datasets, tasks):
# # Removing the `label` columns because it contains -1 and Trainer won't like that.
# predict_dataset = predict_dataset.remove_columns("label")
# predictions = trainer.predict(predict_dataset, metric_key_prefix="predict").predictions
# predictions = np.squeeze(predictions) if is_regression else np.argmax(predictions, axis=1)
# output_predict_file = os.path.join(args.output_dir, f"predict_results_{task}.txt")
# if trainer.is_world_process_zero():
# with open(output_predict_file, "w") as writer:
# logger.info(f"***** Predict results {task} *****")
# writer.write("index\tprediction\n")
# for index, item in enumerate(predictions):
# if is_regression:
# writer.write(f"{index}\t{item:3.3f}\n")
# else:
# item = label_list[item]
# writer.write(f"{index}\t{item}\n")
if __name__ == "__main__":
main()