posttrain.py

#!/usr/bin/env python
# coding=utf-8
# Copyright 2021 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 masked language modeling (BERT, ALBERT, RoBERTa...)
on a text file or a dataset without using HuggingFace Trainer.
Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
https://huggingface.co/models?filter=masked-lm
"""

from utils import utils
import logging
import os
import random
import torch
import datasets
import transformers
from transformers import (
    MODEL_MAPPING,
    AdamW,
    AutoTokenizer,
    AutoConfig,
    RobertaTokenizer,
    BertTokenizer,
    DataCollatorForLanguageModeling,
    get_scheduler,
    SchedulerType,
    set_seed,
)
from accelerate import Accelerator, DistributedType, DistributedDataParallelKwargs

from config import parseing_posttrain
from dataloader.data import get_dataset
from torch.utils.data import DataLoader
from datasets import Dataset, DatasetDict, concatenate_datasets


logger = logging.getLogger(__name__)
MODEL_CONFIG_CLASSES = list(MODEL_MAPPING.keys())
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



def main():

    args = parseing_posttrain()

    args.device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
    args = utils.prepare_sequence_posttrain(args)
    if 'proxy' not in args.baseline:
        from approaches.posttrain_baseline import Appr

    else:
        from approaches.posttrain import Appr

    appr = Appr(args)

    # Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
    ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)
    accelerator = Accelerator(fp16=args.fp16, kwargs_handlers=[ddp_kwargs])
    # Make one log on every process with the configuration for debugging.
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO,
    )
    logger.info(accelerator.state)

    # Setup logging, we only want one process per machine to log things on the screen.
    # accelerator.is_local_main_process is only True for one process per machine.
    logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
    if accelerator.is_local_main_process:
        datasets.utils.logging.set_verbosity_warning()
        transformers.utils.logging.set_verbosity_info()
    else:
        datasets.utils.logging.set_verbosity_error()
        transformers.utils.logging.set_verbosity_error()

    # If passed along, set the training seed now.
    if args.seed is not None:
        set_seed(args.seed)

    # Handle the repository creation
    if accelerator.is_main_process:
        if args.push_to_hub:
            pass
        elif args.output_dir is not None:
            os.makedirs(args.output_dir, exist_ok=True)
    accelerator.wait_for_everyone()


    # Load pretrained model and tokenizer
    #
    # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    # tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, use_fast=not args.use_slow_tokenizer)
    tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
    model = utils.lookfor_model_posttrain(args)
    accelerator.wait_for_everyone()



    #TODO: we need to add "comb" as MTL
    # ---------------------
    if 'comb' in args.baseline:
        for t in range(args.task + 1):
            if t == 0:
                raw_datasets = get_dataset(args.data[t], tokenizer=None,args=args)

            else:
                cur_raw_datasets = get_dataset(args.data[t], tokenizer=None,args=args)
                train_dataset = cur_raw_datasets["train"]

                raw_datasets["train"] = concatenate_datasets([raw_datasets["train"], train_dataset])
    else:
        # Get the dataset
        if args.dataset_name is not None:
            # Downloading and loading a dataset from the hub.
            raw_datasets = get_dataset(args.dataset_name, tokenizer=None, args=args)

    # ---------------------




    # 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.


    # Preprocessing the datasets.
    # First we tokenize all the texts.
    column_names = raw_datasets["train"].column_names
    text_column_name = "text" if "text" in column_names else column_names[0]

    if 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 args.max_seq_length > tokenizer.model_max_length:
            logger.warning(
                f"The max_seq_length passed ({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(args.max_seq_length, tokenizer.model_max_length)

    if args.line_by_line:
        # When using line_by_line, we just tokenize each nonempty line.
        padding = "max_length" if args.pad_to_max_length else False


        def tokenize_function(examples):
            # Remove empty lines
            examples[text_column_name] = [
                line for line in examples[text_column_name] if len(line) > 0 and not line.isspace()
            ]
            return tokenizer(
                examples[text_column_name],
                padding=padding,
                truncation=True,
                max_length=max_seq_length,
                # We use this option because DataCollatorForLanguageModeling (see below) is more efficient when it
                # receives the `special_tokens_mask`.
                return_special_tokens_mask=True,
            )


        with accelerator.main_process_first():
            tokenized_datasets = raw_datasets.map(
                tokenize_function,
                batched=True,
                num_proc=args.preprocessing_num_workers,
                remove_columns=[text_column_name],
                load_from_cache_file=not args.overwrite_cache,
                desc="Running tokenizer on dataset line_by_line",
            )
    else:
        # Otherwise, we tokenize every text, then concatenate them together before splitting them in smaller parts.
        # We use `return_special_tokens_mask=True` because DataCollatorForLanguageModeling (see below) is more
        # efficient when it receives the `special_tokens_mask`.
        def tokenize_function(examples):
            return tokenizer(examples[text_column_name], return_special_tokens_mask=True)


        with accelerator.main_process_first():
            tokenized_datasets = raw_datasets.map(
                tokenize_function,
                batched=True,
                num_proc=args.preprocessing_num_workers,
                remove_columns=column_names,
                load_from_cache_file=not args.overwrite_cache,
                desc="Running tokenizer on every text in dataset",
            )


        # Main data processing function that will concatenate all texts from our dataset and generate chunks of
        # max_seq_length.
        def group_texts(examples):
            # Concatenate all texts.
            concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
            total_length = len(concatenated_examples[list(examples.keys())[0]])
            # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
            # customize this part to your needs.
            if total_length >= max_seq_length:
                total_length = (total_length // max_seq_length) * max_seq_length
            # Split by chunks of max_len.
            result = {
                k: [t[i: i + max_seq_length] for i in range(0, total_length, max_seq_length)]
                for k, t in concatenated_examples.items()
            }
            return result


        # Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a
        # remainder for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value
        # might be slower to preprocess.
        #
        # To speed up this part, we use multiprocessing. See the documentation of the map method for more information:
        # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map

        with accelerator.main_process_first():
            tokenized_datasets = tokenized_datasets.map(
                group_texts,
                batched=True,
                num_proc=args.preprocessing_num_workers,
                load_from_cache_file=not args.overwrite_cache,
                desc=f"Grouping texts in chunks of {max_seq_length}",
            )

    train_dataset = tokenized_datasets["train"]
    eval_dataset = tokenized_datasets["validation"]

    # Log a few random samples from the training set:
    for index in random.sample(range(len(train_dataset)), 1):
        logger.info(
            f"Sample {index} of the training set: {train_dataset[index]}. Decode to: {tokenizer.decode(train_dataset[index]['input_ids'])}")

    # Data collator
    # This one will take care of randomly masking the tokens.
    data_collator = utils.PTDataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=args.mlm_probability)

    print('train_dataset: ', len(train_dataset))
    if args.max_train_samples is not None:
        # Number of samples might increase during Feature Creation, We select only specified max samples
        train_dataset = train_dataset.select(range(int(args.max_train_samples)))

    # DataLoaders creation:
    train_dataloader = DataLoader(
        train_dataset, shuffle=True, collate_fn=data_collator, batch_size=args.per_device_train_batch_size,
        num_workers=0
    )
    eval_dataloader = DataLoader(eval_dataset, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size,num_workers=0)

    if '_sup' in args.dataset_name:  # TAPT
        train_dataloader_prune = DataLoader(
            train_dataset, shuffle=True, collate_fn=data_collator, batch_size=100,
            num_workers=0
        )
    elif '_unsup' in args.dataset_name:  # DAPT

        if 'full_prune' in args.baseline and 'wiki' in args.dataset_name:
            print('full')
            train_dataloader_prune_dataset = train_dataset # too large makes NAN
        else:
            train_dataloader_prune_dataset = train_dataset.select(range(int(1e4)))

        if 'derpp' in args.baseline:
            #TODO: we need a smart way for DERPP, instead of buffer
            train_dataloader_prune = DataLoader(
                train_dataloader_prune_dataset, shuffle=True, collate_fn=data_collator, batch_size=50,
                num_workers=0
            )


        else:
            train_dataloader_prune = DataLoader(
                train_dataloader_prune_dataset, shuffle=True, collate_fn=data_collator, batch_size=100,
                num_workers=0
            )


    appr.train(model,accelerator,train_dataset,train_dataloader,tokenizer,train_dataloader_prune,train_dataloader_prune_dataset)

# TODO: may need to change when we wantto combine

if __name__ == "__main__":
    main()