train_unilm.py

#!/usr/bin/env python
# coding=utf-8
# Copyright 2021 The HuggingFace 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 sequence to sequence.
"""
# You can also adapt this script on your own sequence to sequence task. Pointers for this are left as comments.

import logging
import os
import sys

import nltk  # Here to have a nice missing dependency error message early on
import numpy as np
import random
import torch
from datasets import load_metric

import transformers

from accelerate import Accelerator
from filelock import FileLock
from transformers import (
    DataCollatorForTokenClassification, 
    AutoConfig,
    AutoModelForSeq2SeqLM,
    AutoTokenizer,
    HfArgumentParser,
    Seq2SeqTrainingArguments,
    get_scheduler,
    default_data_collator,
)
from transformers.file_utils import is_offline_mode
from transformers.trainer_utils import get_last_checkpoint, is_main_process
from transformers.utils import check_min_version
from model.local_trainer_ls import Seq2SeqTrainer
from model.utils import  ModelArguments,DataTrainingArguments
from model.local_bart_ls import BartForConditionalGeneration
from model.local_data_collator import DataCollatorForSeq2Seq
from datasets import Dataset
import jsonlines
from transformers import BartConfig,PreTrainedTokenizerFast,BartTokenizer

from torch.utils.data.dataloader import DataLoader

import argparse

# parser = argparse.ArgumentParser(description='Process args.')
# parser.add_argument('--outdir', type=str, required=True)

# args = parser.parse_args()
# outdir= args.outdir
# print (outdir)


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.4.0.dev0")

logger = logging.getLogger(__name__)

try:
    nltk.data.find("tokenizers/punkt")
except (LookupError, OSError):
    if is_offline_mode():
        raise LookupError(
            "Offline mode: run this script without TRANSFORMERS_OFFLINE first to download nltk data files"
        )
    with FileLock(".lock") as lock:
        nltk.download("punkt", quiet=True)
summarization_name_mapping = {
    "amazon_reviews_multi": ("review_body", "review_title"),
    "big_patent": ("description", "abstract"),
    "cnn_dailymail": ("article", "highlights"),
    "orange_sum": ("text", "summary"),
    "pn_summary": ("article", "summary"),
    "psc": ("extract_text", "summary_text"),
    "samsum": ("dialogue", "summary"),
    "thaisum": ("body", "summary"),
    "xglue": ("news_body", "news_title"),
    "xsum": ("document", "summary"),
    "wiki_summary": ("article", "highlights"),
}

alpha=0.01
# model_name='linydub/bart-large-samsum'
# model_name = 'jackieliu930/bart-large-cnn-samsum'
# model_name = 'lidiya/bart-large-xsum-samsum'
# model_name = 'philschmid/distilbart-cnn-12-6-samsum'
# model_name = 'Salesforce/bart-large-xsum-samsum'
# outdir='output-' + model_name.split('/')[1] + '-large-epoch-2'
model_name = 'lidiya/bart-base-samsum'

# model_name = 'facebook/bart-large'
outdir = 'output-' + model_name.split('/')[1] + '-baseline'
if not os.path.exists(outdir):
    os.mkdir(outdir)
dirs=os.listdir(outdir)
dir_idx=len(dirs)+1
dir_idx=1
print(dir_idx)
seed=str(dir_idx*1000)

args=[
    # '--model_name_or_path','facebook/bart-large',
    # '--model_name_or_path','lidiya/bart-base-samsum',
    '--model_name_or_path',model_name,
    '--do_predict',
    # '--do_train','--do_eval','--do_predict',
    # '--do_train','--do_predict',
    '--train_file','data/train.json',
    '--validation_file','data/val.json',
    '--test_file','data/test.json',
    '--output_dir',outdir+'/'+str(dir_idx),
    '--per_device_train_batch_size=4',
    '--per_device_eval_batch_size=4',
    '--overwrite_output_dir',
    '--predict_with_generate=1',
    '--seed',seed,
    '--num_train_epochs','3',
    '--save_strategy','epoch',
    '--evaluation_strategy','epoch',
    '--learning_rate','1e-5',
    '--gradient_accumulation_steps=2',
    '--label_smoothing_factor=0.1',
    '--load_best_model_at_end',
    '--metric_for_best_model','eval_rouge1',
]
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments))
model_args, data_args, training_args = parser.parse_args_into_dataclasses(args)


# data_args.max_source_length = 512
# data_args.max_target_length = 90

def set_seed(seed):
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    if torch.cuda.is_available():
        torch.cuda.manual_seed_all(seed)
        
set_seed(training_args.seed)
training_args.remove_unused_columns=False

if 1:

    last_checkpoint=None

    # 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)],
    )
    logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN)

    # 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}, distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}'
    )



    # Set the verbosity to info of the Transformers logger (on main process only):
    if is_main_process(training_args.local_rank):
        transformers.utils.logging.set_verbosity_info()
    logger.info("Training/evaluation parameters %s", training_args)

    # Get the datasets: you can either provide your own CSV/JSON 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 first column for the full texts and the second column for the
    # summaries (unless you specify column names for this with the `text_column` and `summary_column` arguments).
    #
    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
    # download the dataset.
def load_jsonl(file_path):
    import itertools
    import json
    with jsonlines.open(file_path, "r") as rfd:
        result={}
        keys=['dialogue','summary','utterances']
        for key in keys:
            result[key]=[]
        for data in rfd:
            text=data['dialogue']
            text=list(map(lambda x:x.strip().split(' '),text))
            
            utterance_dic={}
            total_words=0 
            
            for utterance in text:
                name=utterance[0]
                addi_len=0
                if ":" not in name:
                    if utterance[1][-1]==':':
                        name=utterance[0]+utterance[1]
                        addi_len=1
                    elif utterance[2][-1]==':':
                        name=utterance[0]+utterance[1]+utterance[2]
                        addi_len=2
                if len(utterance)>addi_len+1:
                    if name not in utterance_dic.keys():
                        utterance_dic[name]=[]
                    utterance_dic[name].append((total_words+1+addi_len,total_words+len(utterance)-1))
                else:
                    print(utterance)
                    pass
                total_words+=len(utterance)
                
            text=list(itertools.chain(*text))
            assert(len(text)==total_words)
            
            summary=data['summary']
            summary=list(map(lambda x:x.split(' '),summary))
            
            summary=list(itertools.chain(*summary))
            result['summary'].append(summary)
            result['dialogue'].append(text)
            result['utterances'].append(json.dumps(utterance_dic))
    return result

datasets={}
data_files = {}
if data_args.train_file is not None:
    data_files["train"] = data_args.train_file+'l'
    extension = data_args.train_file.split(".")[-1]
if data_args.validation_file is not None:
    data_files["validation"] = data_args.validation_file+'l'
    extension = data_args.validation_file.split(".")[-1]
if data_args.test_file is not None:
    data_files["test"] = data_args.test_file+'l'
    extension = data_args.test_file.split(".")[-1]
for key in data_files:
    print(key)
    dic=load_jsonl(data_files[key])
    datasets[key]=Dataset.from_dict(dic)

    # 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=BartConfig.from_pretrained(model_args.model_name_or_path)
model=BartForConditionalGeneration.from_pretrained(model_args.model_name_or_path)
model.alpha=alpha
print(model.alpha)

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,
    #split into word
    add_prefix_space=True,
)
if 1:
    if model.config.decoder_start_token_id is None:
        raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined")

    prefix = data_args.source_prefix if data_args.source_prefix is not None else ""

    # Preprocessing the datasets.
    # We need to tokenize inputs and targets.
    text_column='dialogue'
    summary_column='summary'
    label_column='utterances'
    column_names = datasets["train"].column_names
    # Temporarily set max_target_length for training.
    max_target_length = data_args.max_target_length
    padding = "max_length" if data_args.pad_to_max_length else False

    if training_args.label_smoothing_factor > 0 and not hasattr(model, "prepare_decoder_input_ids_from_labels"):
        logger.warn(
            "label_smoothing is enabled but the `prepare_decoder_input_ids_from_labels` method is not defined for"
            f"`{model.__class__.__name__}`. This will lead to loss being calculated twice and will take up more memory"
        )

    def preprocess_function(examples):
        inputs = examples[text_column]
        targets = examples[summary_column]
        MYlabels = examples[label_column]
        model_inputs = tokenizer(inputs, max_length=data_args.max_source_length, padding=padding, truncation=True,is_split_into_words=True)

        # Setup the tokenizer for targets
        with tokenizer.as_target_tokenizer():
            labels = tokenizer(targets, max_length=max_target_length, padding=padding, truncation=True,is_split_into_words=True)

        # If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore
        # padding in the loss.
        if padding == "max_length" and data_args.ignore_pad_token_for_loss:
            labels["input_ids"] = [
                [(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels["input_ids"]
            ]

        model_inputs["labels"] = labels["input_ids"]
        
        def find_head(l,x):
            return l.index(x)
        def find_tail(l,x):
            return len(l)-l[::-1].index(x)-1
        
        MYlabels_input=[]
        import json
        for i, label in enumerate(MYlabels):
            label=json.loads(label)
            word_ids = model_inputs.word_ids(batch_index=i)
            
            the_last=word_ids[-2]
            utterance={}
            for key in label.keys():
                utterance[key]=[]
                for span in label[key]:
                    if span[1]>the_last:
                        print(len(word_ids))
                        break
                    utterance[key].append([find_head(word_ids,span[0]),find_tail(word_ids,span[1])])
            MYlabels_input.append(json.dumps(utterance))
        model_inputs['speaker_label']=MYlabels_input
        return model_inputs

if 1:
    if training_args.do_train:
        train_dataset = datasets["train"]
        if "train" not in datasets:
            raise ValueError("--do_train requires a train dataset")
        if data_args.max_train_samples is not None:
            train_dataset = train_dataset.select(range(data_args.max_train_samples))
        train_dataset = train_dataset.map(
            preprocess_function,
            batched=True,
            num_proc=data_args.preprocessing_num_workers,
            remove_columns=column_names,
            load_from_cache_file=not data_args.overwrite_cache,
        )

    if training_args.do_eval:
        max_target_length = data_args.val_max_target_length
        if "validation" not in datasets:
            raise ValueError("--do_eval requires a validation dataset")
        eval_dataset = datasets["validation"]
        if data_args.max_val_samples is not None:
            eval_dataset = eval_dataset.select(range(data_args.max_val_samples))
        eval_dataset = eval_dataset.map(
            preprocess_function,
            batched=True,
            num_proc=data_args.preprocessing_num_workers,
            remove_columns=column_names,
            load_from_cache_file=not data_args.overwrite_cache,
        )

    if training_args.do_predict:
        max_target_length = data_args.val_max_target_length
        if "test" not in datasets:
            raise ValueError("--do_predict requires a test dataset")
        test_dataset = datasets["test"]
        if data_args.max_test_samples is not None:
            test_dataset = test_dataset.select(range(data_args.max_test_samples))
        test_dataset = test_dataset.map(
            preprocess_function,
            batched=True,
            num_proc=data_args.preprocessing_num_workers,
            remove_columns=column_names,
            load_from_cache_file=not data_args.overwrite_cache,
        )



# Data collator
label_pad_token_id = -100 if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id
data_collator = DataCollatorForSeq2Seq(
            tokenizer,
            model=model,
            label_pad_token_id=label_pad_token_id,
            pad_to_multiple_of=8 if training_args.fp16 else None,
        )

# Metric
metric = load_metric("rouge", cache_dir="./my_cache")

def postprocess_text(preds, labels):
    preds = [pred.strip() for pred in preds]
    labels = [label.strip() for label in labels]

    # rougeLSum expects newline after each sentence
    preds = ["\n".join(nltk.sent_tokenize(pred)) for pred in preds]
    labels = ["\n".join(nltk.sent_tokenize(label)) for label in labels]

    return preds, labels

def compute_metrics(eval_preds):
    preds, labels = eval_preds
    if isinstance(preds, tuple):
        preds = preds[0]
    decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)
    if data_args.ignore_pad_token_for_loss:
        # Replace -100 in the labels as we can't decode them.
        labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)

    # Some simple post-processing
    decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)

    result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)
    # Extract a few results from ROUGE
    result = {key: value.mid.fmeasure * 100 for key, value in result.items()}

    prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]
    result["gen_len"] = np.mean(prediction_lens)
    result = {k: round(v, 4) for k, v in result.items()}
    return result

# train_dataset.cleanup_cache_files()


trainer = Seq2SeqTrainer(
        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 if training_args.predict_with_generate else None,
    )



# Training
if training_args.do_train:
    train_result = trainer.train(resume_from_checkpoint=None)
    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()

if training_args.do_predict:
    logger.info("*** Test ***")

    test_results = trainer.predict(
        test_dataset,
        metric_key_prefix="test",
        max_length=data_args.val_max_target_length,
        num_beams=data_args.num_beams,
    )
    metrics = test_results.metrics
    max_test_samples = data_args.max_test_samples if data_args.max_test_samples is not None else len(test_dataset)
    metrics["test_samples"] = min(max_test_samples, len(test_dataset))

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

    if trainer.is_world_process_zero():
        if training_args.predict_with_generate:
            test_preds = tokenizer.batch_decode(
                test_results.predictions, skip_special_tokens=True, clean_up_tokenization_spaces=False
            )
            test_preds = [pred.strip() for pred in test_preds]
            output_test_preds_file = os.path.join(training_args.output_dir, "test_generations.txt")
            with open(output_test_preds_file, "w",encoding='UTF-8') as writer:
                writer.write("\n".join(test_preds))

print(model.alpha)
torch.save(model.total_speaker_loss,'total_speaker_loss')
torch.save(model.total_summary_loss,'total_summary_loss')
torch.save(model.total_loss,'total_loss')