idom_token_pretrained.py

#!/usr/bin/env python
# coding=utf-8
# Copyright The HuggingFace Team and The HuggingFace Inc. team. 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.
"""
Fine-tuning the library models for ChID.
"""


import logging
import os
os.environ["WANDB_DISABLED"] = "true"
import sys
import copy
from dataclasses import dataclass, field
from typing import Optional, Union

import datasets
import numpy as np
import torch
from datasets import load_dataset
from contrastive_model import BertForChID
import torch.nn as nn
import transformers
from transformers import (
    AutoConfig,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.models.bert.modeling_bert import BertOnlyMLMHead
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
from transformers.trainer_utils import get_last_checkpoint
from tokenizers import Tokenizer



logger = logging.getLogger(__name__)


@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 huggingface.co"},
    )
    use_fast_tokenizer: bool = field(
        default=True,
        metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
    )
    model_revision: str = field(
        default="main",
        metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
    )
    use_auth_token: bool = field(
        default=False,
        metadata={
            "help": (
                "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
                "with private models)."
            )
        },
    )
    # logging_steps: int = field(
    #     default=10,
    #     metadata={
    #         "help": (
    #             "logging steps."
    #         )
    #     },
    # )

@dataclass
class DataTrainingArguments:
    """
    Arguments pertaining to what data we are going to input our model for training and eval.
    """

    train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
    validation_file: Optional[str] = field(
        default=None,
        metadata={"help": "An optional input evaluation data file (a text file)."},
    )
    test_file: Optional[str] = field(
        default=None,
        metadata={"help": "An optional input test data file (a text file)."},
    )
    overwrite_cache: bool = field(
        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
    )
    preprocessing_num_workers: Optional[int] = field(
        default=None,
        metadata={"help": "The number of processes to use for the preprocessing."},
    )
    max_seq_length: Optional[int] = field(
        default=None,
        metadata={
            "help": (
                "The maximum total input sequence length after tokenization. If passed, sequences longer "
                "than this will be truncated, sequences shorter will be padded."
            )
        },
    )
    pad_to_max_length: bool = field(
        default=False,
        metadata={
            "help": (
                "Whether to pad all samples to the maximum sentence length. "
                "If False, will pad the samples dynamically when batching to the maximum length in the batch. More "
                "efficient on GPU but very bad for TPU."
            )
        },
    )
    max_train_samples: Optional[int] = field(
        default=None,
        metadata={
            "help": (
                "For debugging purposes or quicker training, truncate the number of training examples to this "
                "value if set."
            )
        },
    )
    max_eval_samples: Optional[int] = field(
        default=None,
        metadata={
            "help": (
                "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
                "value if set."
            )
        },
    )


    def __post_init__(self):
        if self.train_file is not None:
            extension = self.train_file.split(".")[-1]
            assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
        if self.validation_file is not None:
            extension = self.validation_file.split(".")[-1]
            assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."


@dataclass
class DataCollatorForChID:
    """
    Data collator that will dynamically pad the inputs.
    Candidate masks will be computed to indicate which tokens are candidates.

    Args:
        tokenizer ([`PreTrainedTokenizer`] or [`PreTrainedTokenizerFast`]):
            The tokenizer used for encoding the data.
        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `True`):
            Select a strategy to pad the returned sequences (according to the model's padding side and padding index)
            among:

            - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
              if provided).
            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided.
            - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
              lengths).
        max_length (`int`, *optional*):
            Maximum length of the returned list and optionally padding length (see above).
        pad_to_multiple_of (`int`, *optional*):
            If set will pad the sequence to a multiple of the provided value.

            This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
            7.5 (Volta).
    """

    tokenizer: PreTrainedTokenizerBase
    padding: Union[bool, str] = True
    max_length: Optional[int] = None
    pad_to_multiple_of: Optional[int] = None

    def __call__(self, features):
        label_name = "label" if "label" in features[0].keys() else "labels"
        labels = [feature.pop(label_name) for feature in features]

        batch = self.tokenizer.pad(
            features,
            padding=self.padding,
            max_length=self.max_length,
            pad_to_multiple_of=self.pad_to_multiple_of,
            return_tensors="pt",
        )


        # Add back labels
        batch["labels"] = torch.tensor(labels, dtype=torch.int64)
        # Compute candidate masks
        batch["candidate_mask"] = batch["input_ids"] == self.tokenizer.mask_token_id
        return batch


def main():
    os.environ["CUDA_LAUNCH_BLOCKING"] = "1"
    # 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))
    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()


    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        handlers=[logging.StreamHandler(sys.stdout)],
    )
    log_level = training_args.get_process_log_level()
    logger.setLevel(log_level)
    datasets.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()

    # Log on each process the small summary:
    logger.warning(
        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
        + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
    )
    logger.info(f"Training/evaluation parameters {training_args}")

    # Detecting last checkpoint.
    last_checkpoint = None
    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
        last_checkpoint = get_last_checkpoint(training_args.output_dir)
        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
            raise ValueError(
                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
                "Use --overwrite_output_dir to overcome."
            )
        elif last_checkpoint is not None and training_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."
            )
   
    # Set seed before initializing model.
    set_seed(training_args.seed)

    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
    # (the dataset will be downloaded automatically from the datasets Hub).

    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
    # 'text' is found. You can easily tweak this behavior (see below).

    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
    # download the dataset.
    if data_args.train_file is not None or data_args.validation_file is not None:
        data_files = {}
        if data_args.train_file is not None:
            data_files["train"] = data_args.train_file
        if data_args.validation_file is not None:
            data_files["validation"] = data_args.validation_file
        if data_args.test_file is not None:
            data_files["test"] = data_args.test_file
        extension = data_args.train_file.split(".")[-1]
        raw_datasets = load_dataset(
            extension,
            data_files=data_files,
            cache_dir=model_args.cache_dir,
            use_auth_token=True if model_args.use_auth_token else None,
        )
    else:
        # Downloading and loading the chid dataset from the hub. This code is not supposed to be executed in.
        raw_datasets = load_dataset(
            "YuAnthony/chid",
            cache_dir=model_args.cache_dir,
            use_auth_token=True if model_args.use_auth_token else None,
        )
    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
    # https://huggingface.co/docs/datasets/loading_datasets.html.

    # Load pretrained model and tokenizer

    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    config = AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
        use_auth_token=True if model_args.use_auth_token else None,
    )

    # https://huggingface.co/docs/transformers/v4.24.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.from_pretrained
    # 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,
    #     use_fast=model_args.use_fast_tokenizer,
    #     revision=model_args.model_revision,
    #     use_auth_token=True if model_args.use_auth_token else None,
    # )
    # import pdb
    # pdb.set_trace()
    # tokenizer.save("./data/my_tokenizer")
    tokenizer = AutoTokenizer.from_pretrained("./data/my_tokenizer")
    print("#############################################")
    logger.warning("the total number of entire vocab is %d"%len(tokenizer.get_vocab()))
    print("#############################################")
    model = BertForChID.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,
        revision=model_args.model_revision,
        use_auth_token=True if model_args.use_auth_token else None,
    )

    # set new word embedding containing idiom embeddings
    embedding_checkpoint = torch.load("./data/idiom_word_embedding.pt", map_location="cpu")
    
    idiom_embeddings = embedding_checkpoint['idiom_embeddings'] 
    
    model.bert.set_input_embeddings(idiom_embeddings)

    # set new predictions.decoder
    # old_prediction_decoder= model.cls.predictions.decoder
    # new_prediction_decoder = nn.Linear(768, 52053, bias=False)
    # new_prediction_decoder.bias = nn.Parameter(torch.zeros(52053))
    # with torch.no_grad():
    #     new_prediction_decoder.weight[:21128] = old_prediction_decoder.weight
    #     new_prediction_decoder.bias[:21128] = old_prediction_decoder.bias
    # model.cls.predictions.decoder = new_prediction_decoder
    
    label_column_name = "labels"
    idiom_tag = '#idiom#'
        
    if data_args.max_seq_length is None:
        max_seq_length = tokenizer.model_max_length
        if max_seq_length > 1024:
            logger.warning(
                f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). "
                "Picking 1024 instead. You can change that default value by passing --max_seq_length xxx."
            )
            max_seq_length = 1024
    else:
        if data_args.max_seq_length > tokenizer.model_max_length:
            logger.warning(
                f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"
                f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
            )
        max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)
    

    # Preprocessing the datasets.

    # We only consider one idiom per instance in the dataset, a sentence containing multiple idioms will be split into multiple instances.
    # The idiom tag of each instance will be replaced with 4 [MASK] tokens.
    def preprocess_function_resize(examples):
        return_dic = {}
        return_dic_keys = ['candidates', 'content', 'labels']
        for k in return_dic_keys:
            return_dic[k] = []

        for i in range(len(examples['content'])):
            idx = -1
            text = examples['content'][i]
            for j in range(examples['realCount'][i]):
                return_dic['candidates'].append(examples['candidates'][i][j]) # multi-choice
                idx = text.find(idiom_tag, idx+1)
                return_dic['content'].append(text[:idx] + tokenizer.mask_token + text[idx+len(idiom_tag):])
                for k, candidate in enumerate(examples['candidates'][i][j]):
                    if candidate == examples['groundTruth'][i][j]:
                        return_dic['labels'].append(k)
                        break
        return return_dic

    # tokenize all instances
    # contribution1: tokenized by meaningful words instead of single word or only replace one idiom masked by masked token. 
    def preprocess_function_tokenize(examples): 
        first_sentences = examples['content']
        labels = examples[label_column_name]
        # truncate the first sentences.
        for i, sentence in enumerate(first_sentences):
            if len(sentence) <= 500:
                continue
            if sentence.find(tokenizer.mask_token) > len(sentence) // 2: # max length of the sentence is 500
                first_sentences[i] = sentence[-500:]
            else:
                first_sentences[i] = sentence[:500]
                
        tokenized_examples = tokenizer(
            first_sentences,
            max_length=max_seq_length,
            padding="max_length" if data_args.pad_to_max_length else False,
            truncation=True,
        )
    
        tokenized_examples["labels"] = labels 
        # contribution2: use idiom vocabs to represent idioms instead of simply word.
        # notice: tokenizer() will split the idiom into single word instead of a entire idiom.
        tokenized_candidates = [[tokenizer.convert_tokens_to_ids(candidate) for candidate in candidates]for candidates in examples['candidates']] # batch_size*choices_num*4
        tokenized_examples["candidates"] = tokenized_candidates
        return tokenized_examples


    if training_args.do_train:
        if "train" not in raw_datasets:
            raise ValueError("--do_train requires a train dataset")
        train_dataset = raw_datasets["train"]
        if data_args.max_train_samples is not None:
            max_train_samples = min(len(train_dataset), data_args.max_train_samples)
            train_dataset = train_dataset.select(range(max_train_samples))

        train_dataset = train_dataset.shuffle(seed=42)
        with training_args.main_process_first(desc="train dataset map pre-processing"):
            train_dataset = train_dataset.map(
                preprocess_function_resize,
                batched=True,
                remove_columns=["groundTruth", "realCount"],
                num_proc=data_args.preprocessing_num_workers,
                load_from_cache_file=not data_args.overwrite_cache,
            )

            train_dataset = train_dataset.map(
                preprocess_function_tokenize,
                batched=True,
                num_proc=data_args.preprocessing_num_workers,
                load_from_cache_file=not data_args.overwrite_cache,
            )
            # for index in range(3):
            #     logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
    if training_args.do_eval:
        if "validation" not in raw_datasets:
            raise ValueError("--do_eval requires a validation dataset")
        eval_dataset = raw_datasets["validation"]
        if data_args.max_eval_samples is not None:
            max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
            eval_dataset = eval_dataset.select(range(max_eval_samples))
        with training_args.main_process_first(desc="validation dataset map pre-processing"):
            eval_dataset = eval_dataset.map(
                preprocess_function_resize,
                batched=True,
                remove_columns=["groundTruth", "realCount"],
                num_proc=data_args.preprocessing_num_workers,
                load_from_cache_file=not data_args.overwrite_cache,
            )
            eval_dataset = eval_dataset.map(
                preprocess_function_tokenize,
                batched=True,
                num_proc=data_args.preprocessing_num_workers,
                load_from_cache_file=not data_args.overwrite_cache,
            )
        test_dataset = raw_datasets["test"]
        with training_args.main_process_first(desc="test dataset map pre-processing"):
            test_dataset = test_dataset.map(
                preprocess_function_resize,
                batched=True,
                remove_columns=["groundTruth", "realCount"],
                num_proc=data_args.preprocessing_num_workers,
                load_from_cache_file=not data_args.overwrite_cache,
            )
            test_dataset = test_dataset.map(
                preprocess_function_tokenize,
                batched=True,
                num_proc=data_args.preprocessing_num_workers,
                load_from_cache_file=not data_args.overwrite_cache,
            )
    # Data collator
    data_collator = (
        default_data_collator
        if data_args.pad_to_max_length
        else DataCollatorForChID(tokenizer=tokenizer, pad_to_multiple_of=8 if training_args.fp16 else None)
    )
    # data_collator = default_data_collator



    # Metric
    def compute_metrics(eval_predictions):
        predictions, label_ids = eval_predictions
        preds = np.argmax(predictions, axis=1)
        return {"accuracy": (preds == label_ids).astype(np.float32).mean().item()}

    # Initialize our Trainer
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset if training_args.do_train else None,
        eval_dataset=eval_dataset if training_args.do_eval else None,
        tokenizer=tokenizer,
        data_collator=data_collator,
        compute_metrics=compute_metrics,
    )


    # Training
    if training_args.do_train:
        checkpoint = None
        if training_args.resume_from_checkpoint is not None:
            checkpoint = training_args.resume_from_checkpoint
        elif last_checkpoint is not None:
            checkpoint = last_checkpoint
        print(training_args.resume_from_checkpoint)
        
        train_result = trainer.train(resume_from_checkpoint=checkpoint)
        trainer.save_model()  # Saves the tokenizer too for easy upload
        metrics = train_result.metrics

        max_train_samples = (
            data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
        )
        metrics["train_samples"] = min(max_train_samples, len(train_dataset))

        trainer.log_metrics("train", metrics)
        trainer.save_metrics("train", metrics)
        trainer.save_state()

    # Evaluation
    if training_args.do_eval:
        logger.info("*** Evaluate ***")

        metrics = trainer.evaluate(eval_dataset=test_dataset)
        metrics["test_samples"] = len(test_dataset)

        trainer.log_metrics("test", metrics)
        trainer.save_metrics("test", metrics)

    # kwargs = dict(
    #     finetuned_from=model_args.model_name_or_path,
    #     tasks="multiple-choice",
    #     dataset_tags="swag",
    #     dataset_args="regular",
    #     dataset="SWAG",
    #     language="en",
    # )

    # if training_args.push_to_hub:
    #     trainer.push_to_hub(**kwargs)
    # else:
    #     trainer.create_model_card(**kwargs)


def _mp_fn(index):
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
    main()