fine-tune_xnli.py

# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Finetuning multi-lingual models on XNLI (e.g. Bert, DistilBERT, XLM).
    Adapted from `examples/text-classification/run_glue.py`"""

import argparse
import glob
import logging
import os
import random

os.environ['CUDA_VISIBLE_DEVICES'] = '0'

import json
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm, trange

import transformers
from transformers import (
    WEIGHTS_NAME,
    AdamW,
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    get_linear_schedule_with_warmup, AutoModelForMaskedLM, BertTokenizerFast,
)
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import xnli_compute_metrics as compute_metrics

from utils.models import ramen, AlignAnchor
from utils.data_processor import processors, output_modes
from transformers.trainer_utils import is_main_process

logger = logging.getLogger(__name__)


def set_seed(args):
    random.seed(args.seed)
    np.random.seed(args.seed)
    torch.manual_seed(args.seed)
    if args.n_gpu > 0:
        torch.cuda.manual_seed_all(args.seed)


def train(args, train_dataset, model, tokenizer):
    """ Train the model """

    args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
    train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset)
    train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size)

    if args.max_steps > 0:
        t_total = args.max_steps
        args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1
    else:
        t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs

    # Changed
    args.logging_steps = len(train_dataloader) // args.num_evals
    args.warmup_steps = args.warmup_proportion * t_total

    # Prepare optimizer and schedule (linear warmup and decay)
    no_decay = ["bias", "LayerNorm.weight"]
    optimizer_grouped_parameters = [
        {
            "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
            "weight_decay": args.weight_decay,
        },
        {"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0},
    ]
    optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
    scheduler = get_linear_schedule_with_warmup(
        optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total
    )

    # Check if saved optimizer or scheduler states exist
    if os.path.isfile(os.path.join(args.model_name_or_path, "optimizer.pt")) and os.path.isfile(
            os.path.join(args.model_name_or_path, "scheduler.pt")
    ):
        # Load in optimizer and scheduler states
        optimizer.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "optimizer.pt")))
        scheduler.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "scheduler.pt")))

    if args.fp16:
        try:
            from apex import amp
        except ImportError:
            raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
        model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)

    # multi-gpu training (should be after apex fp16 initialization)
    if args.n_gpu > 1:
        model = torch.nn.DataParallel(model)

    # Distributed training (should be after apex fp16 initialization)
    if args.local_rank != -1:
        model = torch.nn.parallel.DistributedDataParallel(
            model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True
        )

    # Train!
    logger.info("***** Running training *****")
    logger.info("  Num examples = %d", len(train_dataset))
    logger.info("  Num Epochs = %d", args.num_train_epochs)
    logger.info("  Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size)
    logger.info(
        "  Total train batch size (w. parallel, distributed & accumulation) = %d",
        args.train_batch_size
        * args.gradient_accumulation_steps
        * (torch.distributed.get_world_size() if args.local_rank != -1 else 1),
    )
    logger.info("  Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
    logger.info("  Total optimization steps = %d", t_total)

    global_step = 0
    epochs_trained = 0
    steps_trained_in_current_epoch = 0
    # Check if continuing training from a checkpoint
    if os.path.exists(args.model_name_or_path):
        # set global_step to gobal_step of last saved checkpoint from model path
        global_step = int(args.model_name_or_path.split("-")[-1].split("/")[0])
        epochs_trained = global_step // (len(train_dataloader) // args.gradient_accumulation_steps)
        steps_trained_in_current_epoch = global_step % (len(train_dataloader) // args.gradient_accumulation_steps)

        logger.info("  Continuing training from checkpoint, will skip to saved global_step")
        logger.info("  Continuing training from epoch %d", epochs_trained)
        logger.info("  Continuing training from global step %d", global_step)
        logger.info("  Will skip the first %d steps in the first epoch", steps_trained_in_current_epoch)

    tr_loss, logging_loss = 0.0, 0.0
    # AfterBERT
    best_val_loss = 1e5

    model.zero_grad()
    train_iterator = trange(
        epochs_trained, int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0]
    )
    set_seed(args)  # Added here for reproductibility
    for _ in train_iterator:
        epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])
        for step, batch in enumerate(epoch_iterator):
            # Skip past any already trained steps if resuming training
            if steps_trained_in_current_epoch > 0:
                steps_trained_in_current_epoch -= 1
                continue

            model.train()
            batch = tuple(t.to(args.device) for t in batch)
            inputs = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
            if args.model_type != "distilbert":
                inputs["token_type_ids"] = (
                    batch[2] if args.model_type in ["bert"] else None
                )  # XLM and DistilBERT don't use segment_ids
            outputs = model(**inputs)
            loss = outputs[0]  # model outputs are always tuple in transformers (see doc)

            if args.n_gpu > 1:
                loss = loss.mean()  # mean() to average on multi-gpu parallel training
            if args.gradient_accumulation_steps > 1:
                loss = loss / args.gradient_accumulation_steps

            if args.fp16:
                with amp.scale_loss(loss, optimizer) as scaled_loss:
                    scaled_loss.backward()
            else:
                loss.backward()

            tr_loss += loss.item()
            if (step + 1) % args.gradient_accumulation_steps == 0:
                if args.fp16:
                    torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
                else:
                    torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)

                optimizer.step()

                scheduler.step()  # Update learning rate schedule
                model.zero_grad()
                global_step += 1

                if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
                    # Log metrics
                    logs = {}
                    if (
                            args.local_rank == -1 and args.evaluate_during_training
                    ):  # Only evaluate when single GPU otherwise metrics may not average well
                        results = evaluate(args, model, tokenizer)
                        for key, value in results.items():
                            eval_key = "eval_{}".format(key)
                            logs[eval_key] = value
                    loss_scalar = (tr_loss - logging_loss) / args.logging_steps
                    learning_rate_scalar = scheduler.get_lr()[0]
                    logs["learning_rate"] = learning_rate_scalar
                    logs["loss"] = loss_scalar
                    logging_loss = tr_loss

                    print(json.dumps({**logs, **{"step": global_step}}))

                    # Changed
                    if args.local_rank in [-1, 0] and results["loss"] < best_val_loss:
                        # Changed
                        train_steps = global_step / len(train_dataloader)
                        # Save model checkpoint
                        output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(global_step))
                        if not os.path.exists(output_dir):
                            os.makedirs(output_dir)
                        model_to_save = (
                            model.module if hasattr(model, "module") else model
                        )  # Take care of distributed/parallel training
                        model_to_save.save_pretrained(output_dir)
                        tokenizer.save_pretrained(output_dir)

                        torch.save(args, os.path.join(output_dir, "training_args.bin"))
                        logger.info("Saving model checkpoint to %s", output_dir)

                        torch.save(optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt"))
                        torch.save(scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt"))
                        logger.info("Saving optimizer and scheduler states to %s", output_dir)

                        # Changed
                        best_val_loss = results["loss"]

                        output_ckpt_file = os.path.join(output_dir, "best_loss.txt")
                        with open(output_ckpt_file, "w+") as writer:
                            for key in sorted(results.keys()):
                                writer.write("%s = %s\n" % (key, str(results[key])))
                            writer.write("steps = %s\n" % (str(train_steps)))

            if args.max_steps > 0 and global_step > args.max_steps:
                epoch_iterator.close()
                break
        if args.max_steps > 0 and global_step > args.max_steps:
            train_iterator.close()
            break

    return global_step, tr_loss / global_step


def evaluate(args, model, tokenizer, prefix="", test=False):
    eval_task_names = (args.task_name,)
    eval_outputs_dirs = (args.output_dir,)

    results = {}
    for eval_task, eval_output_dir in zip(eval_task_names, eval_outputs_dirs):
        eval_dataset = load_and_cache_examples(args, eval_task, tokenizer, evaluate=True, test=test)

        if not os.path.exists(eval_output_dir) and args.local_rank in [-1, 0]:
            os.makedirs(eval_output_dir)

        args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
        # Note that DistributedSampler samples randomly
        eval_sampler = SequentialSampler(eval_dataset)
        eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)

        # multi-gpu eval
        if args.n_gpu > 1 and not isinstance(model, torch.nn.DataParallel):
            model = torch.nn.DataParallel(model)

        # Eval!
        logger.info("***** Running evaluation {} *****".format(prefix))
        logger.info("  Num examples = %d", len(eval_dataset))
        logger.info("  Batch size = %d", args.eval_batch_size)
        eval_loss = 0.0
        nb_eval_steps = 0
        preds = None
        out_label_ids = None
        for batch in tqdm(eval_dataloader, desc="Evaluating"):
            model.eval()
            batch = tuple(t.to(args.device) for t in batch)

            with torch.no_grad():
                inputs = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
                if args.model_type != "distilbert":
                    inputs["token_type_ids"] = (
                        batch[2] if args.model_type in ["bert"] else None
                    )  # XLM and DistilBERT don't use segment_ids
                outputs = model(**inputs)
                tmp_eval_loss, logits = outputs[:2]

                eval_loss += tmp_eval_loss.mean().item()
            nb_eval_steps += 1
            if preds is None:
                preds = logits.detach().cpu().numpy()
                out_label_ids = inputs["labels"].detach().cpu().numpy()
            else:
                preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)
                out_label_ids = np.append(out_label_ids, inputs["labels"].detach().cpu().numpy(), axis=0)

        eval_loss = eval_loss / nb_eval_steps
        if args.output_mode == "classification":
            preds = np.argmax(preds, axis=1)
        else:
            raise ValueError("No other `output_mode` for XNLI.")
        with open(os.path.join(eval_output_dir, "predictions.txt"), "w") as writer:
            writer.write("prediction\tlabel\n")
            for prediction, label in zip(preds, out_label_ids):
                writer.write("{}\t{}\n".format(prediction, label))
        result = compute_metrics("xnli", preds, out_label_ids)
        results.update(result)

        # Changed
        result.update({"loss": eval_loss})
        # result.update({"loss": eval_loss})
        results.update(result)

        output_eval_file = os.path.join(eval_output_dir, prefix, "eval_results.txt" if not test else "test_results.txt")
        with open(output_eval_file, "w") as writer:
            logger.info("***** Eval results {} *****".format(prefix))
            for key in sorted(result.keys()):
                logger.info("  %s = %s", key, str(result[key]))
                writer.write("%s = %s\n" % (key, str(result[key])))

    return results


def load_and_cache_examples(args, task, tokenizer, evaluate=False, test=False):
    if args.local_rank not in [-1, 0] and not evaluate:
        torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache

    processor = processors[task](language=args.language, train_language=args.train_language)
    output_mode = output_modes[task]
    # Load data features from cache or dataset file
    specific_language = str(
        args.train_language if (not evaluate and args.train_language is not None) else args.language)
    cached_features_file = os.path.join(
        args.data_dir,
        "cached_{}_{}_{}_{}_{}".format(
            "train" if not evaluate else ("eval" if not test else "test"),
            list(filter(None, args.model_name_or_path.split("/"))).pop(),
            str(args.max_seq_length),
            str(task),
            specific_language if specific_language == 'en' else specific_language + '_{}'.format(args.biLM_model_name),
        ),
    )
    if os.path.exists(cached_features_file) and not args.overwrite_cache:
        logger.info("Loading features from cached file %s", cached_features_file)
        features = torch.load(cached_features_file)
    else:
        logger.info("Creating features from dataset file at %s", args.data_dir)
        label_list = processor.get_labels()
        examples = (
            (processor.get_train_examples(args.data_dir) if not evaluate else (
                processor.get_dev_examples(args.data_dir) if not test else processor.get_test_examples(args.data_dir))))
        features = convert_examples_to_features(
            examples,
            tokenizer,
            max_length=args.max_seq_length,
            label_list=label_list,
            output_mode=output_mode,
        )
        if args.local_rank in [-1, 0]:
            logger.info("Saving features into cached file %s", cached_features_file)
            torch.save(features, cached_features_file)

    if args.local_rank == 0 and not evaluate:
        torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache

    # Convert to Tensors and build dataset
    all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
    all_attention_mask = torch.tensor([f.attention_mask for f in features], dtype=torch.long)
    all_token_type_ids = torch.tensor([f.token_type_ids for f in features], dtype=torch.long)
    if output_mode == "classification":
        all_labels = torch.tensor([f.label for f in features], dtype=torch.long)
    else:
        raise ValueError("No other `output_mode` for XNLI.")

    dataset = TensorDataset(all_input_ids, all_attention_mask, all_token_type_ids, all_labels)
    return dataset


def main(args):
    args.output_dir = os.path.join(args.output_dir, str(args.seed))
    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 = 0 if args.no_cuda else 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 the verbosity to info of the Transformers logger (on main process only):
    if is_main_process(args.local_rank):
        transformers.utils.logging.set_verbosity_info()
        transformers.utils.logging.enable_default_handler()
        transformers.utils.logging.enable_explicit_format()
    # Set seed
    set_seed(args)

    # Prepare XNLI task
    # args.task_name = "xnli"
    if args.task_name not in processors:
        raise ValueError("Task not found: %s" % (args.task_name))
    processor = processors[args.task_name](language=args.language, train_language=args.train_language)
    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

    config = AutoConfig.from_pretrained(
        args.config_name if args.config_name else args.model_name_or_path,
        num_labels=num_labels,
        finetuning_task=args.task_name,
        cache_dir=args.cache_dir,
    )
    args.model_type = config.model_type

    src_tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, cache_dir=args.cache_dir, use_fast=True)

    tgt_tokenizer = BertTokenizerFast(vocab_file=args.tgt_tokenizer_name, do_lower_case=True,
                                      strip_accents=False, handle_chinese_chars=False, )

    src_model = AutoModelForMaskedLM.from_pretrained(
        args.model_name_or_path,
        from_tf=bool(".ckpt" in args.model_name_or_path),
        config=config,
        cache_dir=args.cache_dir,
    )

    tgt_model = AutoModelForMaskedLM.from_pretrained(
        args.foreign_model,
        from_tf=bool(".ckpt" in args.model_name_or_path),
        config=config,
        cache_dir=args.cache_dir,
    )

    if args.biLM_model_name == "ramen":
        biLM_model = ramen(src_model, tgt_model)
    elif args.biLM_model_name in ["ours", "joint"]:
        if not args.alignment_dir:
            raise ValueError("No alignment directory provided")

        with open(args.alignment_dir + "src_shared_idx.json", 'r') as f:
            src_subw_idx = json.load(f)

        logger.info("  Number of shared subwords {}".format(len(src_subw_idx)))

        non_shared_mask = np.ones(src_tokenizer.vocab_size, dtype=bool)
        non_shared_mask[src_subw_idx] = False
        non_shared_src_idx = np.arange(tgt_tokenizer.vocab_size)[non_shared_mask]

        biLM_model = AlignAnchor(src_model, tgt_model, non_shared_src_idx=non_shared_src_idx)
    else:
        raise ValueError("The model name is not correct")

    biLM_model.load_state_dict(torch.load(args.biLM + "/" + WEIGHTS_NAME))
    logger.info("  Loading from checkpoint {}".format(args.biLM))

    # Training
    if args.do_train:
        if args.biLM_model_name in ["ours", "joint"]:
            with torch.no_grad():
                biLM_model.src_model.base_model.embeddings.word_embeddings.weight = biLM_model.src_embs
                biLM_model.src_model.tie_weights()
        model = AutoModelForSequenceClassification.from_pretrained(
            args.model_name_or_path,
            from_tf=bool(".ckpt" in args.model_name_or_path),
            config=config,
            cache_dir=args.cache_dir,
            state_dict=biLM_model.src_model.state_dict(),
        )

        # Freeze all embeddings
        for param in model.base_model.embeddings.parameters():
            param.requires_grad = False

        if args.local_rank == 0:
            torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab

        print('Number of trainable parameters :{}'.format(
            sum(p.numel() for p in model.parameters() if p.requires_grad == True)))

        model.to(args.device)

        logger.info("Training/evaluation parameters %s", args)

        train_dataset = load_and_cache_examples(args, args.task_name, src_tokenizer, evaluate=False)
        global_step, tr_loss = train(args, train_dataset, model, src_tokenizer)
        logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)

    # Evaluation
    if args.do_eval or args.do_test:
        checkpoints = list(
            os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + "/**/" + WEIGHTS_NAME, recursive=True))
        )
        checkpoint = checkpoints[np.argmax(
            [int(ckpt.split("/")[-1].split("-")[-1]) for ckpt in checkpoints])]

        model = AutoModelForSequenceClassification.from_pretrained(
            checkpoint,
            from_tf=bool(".ckpt" in args.model_name_or_path),
            config=config,
            cache_dir=args.cache_dir,
        )

        if args.language != "en":
            tokenizer = tgt_tokenizer

            # Reinit embeddings
            if args.biLM_model_name in ["ours", "joint"]:
                with torch.no_grad():
                    biLM_model.tgt_model.base_model.embeddings.word_embeddings.weight = biLM_model.src_embs.clone()
                    # the non shared embeddings must be positioned to the non-shared source indexes
                    biLM_model.tgt_model.base_model.embeddings.word_embeddings.weight[biLM_model.non_shared_src_idx,
                    :] = biLM_model.non_shared_tgt_subw

            model.base_model.embeddings.word_embeddings.weight.data = biLM_model.tgt_model.base_model.embeddings.word_embeddings.weight
        else:
            tokenizer = src_tokenizer

        model.to(args.device)

        # Evaluation
        logger.info("Evaluate the following checkpoint: %s", checkpoint)
        logs = {}

        prefix = checkpoint.split("/")[-1]
        # if checkpoints[-1].find("checkpoint") != -1 else ""

        results = evaluate(args, model, tokenizer, prefix=prefix, test=args.do_test)

        for key, value in results.items():
            eval_key = "eval_{}".format(key)
            logs[eval_key] = value
            print(json.dumps({**logs}))

        if args.language != "en":
            output_eval_file = os.path.join(args.output_dir,
                                            "{}_{}_results.txt".format("test" if args.do_test else "val",
                                                                       args.language))
            with open(output_eval_file, "w+") as writer:
                for key in results.keys():
                    writer.write("%s = %s\n" % (key, str(results[key])))

    return results


if __name__ == "__main__":
    parser = argparse.ArgumentParser()

    # Required parameters
    parser.add_argument(
        "--data_dir",
        default='data',
        type=str,
        help="The input data dir. Should contain the .tsv files (or other data files) for the task.",
    )
    parser.add_argument(
        "--model_name_or_path",
        default='bert-base-uncased',
        type=str,
        help="Path to pretrained model or model identifier from huggingface.co/models",
    )
    parser.add_argument(
        "--biLM_model_name",
        type=str,
        default="ours",
        help="The name of the biLM model"
    )
    parser.add_argument(
        "--biLM",
        default='/ckpts/greek_ours_align/checkpoint-120000/',
        type=str,
        help="Path to fine-tuned model if it is different than the one trained",
    )
    parser.add_argument(
        "--foreign_model",
        default='emb_layer/el/bert-ours_align_embs',
        type=str,
        help="Path to foreign model if it is different than the one trained",
    )
    parser.add_argument(
        "--task_name",
        default="xnli",
        type=str,
        help="Tha name of the cross-lingual task"
    )
    parser.add_argument(
        "--language",
        default='en',
        type=str,
        help="Evaluation language. Also train language if `train_language` is set to None.",
    )
    parser.add_argument(
        "--train_language", default='en', type=str, help="Train language if is different of the evaluation language."
    )
    parser.add_argument(
        "--output_dir",
        default='ckpts/greek_xnli_ours_align',
        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(
        "--tgt_tokenizer_name",
        default='alignments/en-el/new_tgt_vocab.txt',
        type=str,
        help="Pretrained tokenizer name or path if not the same as model_name",
    )
    parser.add_argument(
        "--alignment_dir",
        default='alignments/en-el/',
        type=str,
        help="The path to the alignment dict",
    )
    parser.add_argument(
        "--cache_dir",
        default='cache/',
        type=str,
        help="Where do you want to store the pre-trained models downloaded from huggingface.co",
    )
    parser.add_argument(
        "--max_seq_length",
        default=256,
        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 evaluation.")
    parser.add_argument("--do_test", action='store_true', help="Whether to run evaluation on the test set.")
    parser.add_argument(
        "--evaluate_during_training", default=True, help="Run evaluation during training at each logging step."
    )
    parser.add_argument(
        "--num_evals", default=5, help="Number of evaluations per epoch."
    )
    parser.add_argument(
        "--do_lower_case", default=True, help="Set this flag if you are using an uncased model."
    )

    parser.add_argument("--per_gpu_train_batch_size", default=32, type=int, help="Batch size per GPU/CPU for training.")
    parser.add_argument(
        "--per_gpu_eval_batch_size", default=64, 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=2e-5, type=float, help="The initial learning rate for Adam.")
    parser.add_argument("--weight_decay", default=0.01, 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=5.0, 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_proportion", default=0.1, type=float,
                        help="Linear warmup over warmup_proportion*total_steps.")

    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", default=False, 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", default=False, help="Overwrite the cached training and evaluation sets"
    )
    parser.add_argument("--seed", type=int, default=12, 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()

    main(args)