train.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 jsonlines
import numpy as np
from datasets import load_dataset, load_metric,Dataset
from model.utils import  ModelArguments,DataTrainingArguments
from model.local_trainer import Seq2SeqTrainer
from model.local_bart import BartForConditionalGeneration
from model.local_data_collator import DataCollatorForSeq2Seq

import transformers
from filelock import FileLock
from transformers import (
    AutoConfig,
    AutoModelForSeq2SeqLM,
    AutoTokenizer,
    HfArgumentParser,
    Seq2SeqTrainingArguments,
    default_data_collator,
    set_seed,
    BartConfig
)
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

# 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"),
}        


# model_name = 'facebook/bart-large'
# model_name = 'lidiya/bart-large-xsum-samsum'
model_name = 'linydub/bart-large-samsum'
# outdir='output-' + model_name.split('/')[1] + '-large-epoch'
output_dir='output-' + model_name.split('/')[1] + '-3'
# output_dir='output-' + model_name.split('/')[1]

# output_dir='pmi-0.001'
if not os.path.exists(output_dir):
    os.mkdir(output_dir)
alpha=0.001

args=[
    '--model_name_or_path',model_name,
    '--do_train',
    '--do_eval',
    '--do_predict',
    '--train_file','data/train.json',
    '--validation_file','data/val.json',
    '--test_file','data/test.json',
    '--output_dir',output_dir,
    '--per_device_train_batch_size=2',
    '--per_device_eval_batch_size=2',
    '--overwrite_output_dir',
    '--predict_with_generate=1',
    '--seed','2021',
    '--max_source_length=1024',
    '--max_target_length=320',
    '--max_steps','20000',
    '--warmup_steps','400',
    '--save_strategy','steps',
    '--evaluation_strategy','steps',
    '--save_steps','1000',
    '--eval_steps','500',
    '--learning_rate','1e-5',
    '--gradient_accumulation_steps=2',
]
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments))
model_args, data_args, training_args = parser.parse_args_into_dataclasses(args)

training_args.remove_unused_columns=False
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:
        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."
        )

# 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}"
    + f"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)

# Set seed before initializing model.
set_seed(training_args.seed)

# 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 json
    import itertools
    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['text']
            text=list(map(lambda x:x.strip().split(' '),text))

            utterance_dic={}
            total_words=0 

            for utterance in text:
                assert(':' in utterance)
                name_len=utterance.index(':')
                name=' '.join(utterance[:name_len])
                if name not in utterance_dic.keys():
                    utterance_dic[name]=[]
                utterance_dic[name].append((total_words+name_len,total_words+len(utterance)-1))
                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


if data_args.dataset_name is not None:
    # Downloading and loading a dataset from the hub.
    datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
else:
    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:
        dic=load_jsonl(data_files[key])
        datasets[key]=Dataset.from_dict(dic)

config=BartConfig.from_pretrained(model_args.model_name_or_path)
config.max_position_embeddings=data_args.max_source_length
model=BartForConditionalGeneration.from_pretrained(model_args.model_name_or_path,max_position_embeddings=data_args.max_source_length)

model.alpha=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,
    add_prefix_space=True
)

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 ""

text_column='dialogue'
summary_column='summary'
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"
    )

label_column='utterances'
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[0]>the_last:
                    break
                if span[1]>the_last:
                    utterance[key].append([find_head(word_ids,span[0]),len(word_ids)-2])
                    break
                utterance[key].append([find_head(word_ids,span[0]),find_tail(word_ids,span[1])])
        to_del=[]
        for key in utterance.keys():
            if utterance[key]==[]:
                to_del.append(key)
        for key in to_del:
            del utterance[key]
        MYlabels_input.append(json.dumps(utterance))
    model_inputs['speaker_label']=MYlabels_input
    return model_inputs

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="./ami_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


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('../ami_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))