finetune_trainer.py

#!/usr/bin/env python

import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeq2SeqLM,
    AutoTokenizer,
    HfArgumentParser,
    Seq2SeqTrainer,
    Seq2SeqTrainingArguments,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    Seq2SeqDataCollator,
    Seq2SeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)
from model import T5


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"},
    )
    load_checkpoint_from: Optional[str] = field(
        default=None,
        metadata={"help": "Path to checkpoint for evaluation"},
    )
    add_tokens: bool = field(
        default=False,
        metadata={
            "help": "Whether to add tokens"
        },
    )
    temp_start: float = field(
        default=1.0,
        metadata={
            "help": "Starting temperature"
        },
    )
    temp_end: float = field(
        default=1.0,
        metadata={
            "help": "Ending temperature"
        },
    )
    scheduler: str = field(
        default="constant",
        metadata={
            "help": "Temperature scheduler"
        },
    )
    freeze_encoder: bool = field(default=False, metadata={"help": "Whether tp freeze the encoder"})
    freeze_embeds: bool = field(default=False, metadata={"help": "Whether to freeze the embeddings"})


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

    data_dir: str = field(
        metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}
    )
    task: Optional[str] = field(
        default="summarization",
        metadata={"help": "Task name, summarization (or summarization_{dataset} for pegasus) or translation"},
    )
    max_source_length: Optional[int] = field(
        default=1024,
        metadata={
            "help": "The maximum total input sequence length after tokenization. Sequences longer "
            "than this will be truncated, sequences shorter will be padded."
        },
    )
    max_target_length: Optional[int] = field(
        default=128,
        metadata={
            "help": "The maximum total sequence length for target text after tokenization. Sequences longer "
            "than this will be truncated, sequences shorter will be padded."
        },
    )
    n_train: Optional[int] = field(default=None, metadata={"help": "# training examples. None means use all."})
    n_val: Optional[int] = field(default=None, metadata={"help": "# validation examples. None means use all."})
    n_test: Optional[int] = field(default=None, metadata={"help": "# test examples. None means use all."})
    eval_beams: Optional[int] = field(default=None, metadata={"help": "# num_beams to use for evaluation."})
    ignore_pad_token_for_loss: bool = field(
        default=True,
        metadata={"help": "If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."},
    )


def handle_metrics(split, metrics, output_dir):
    """
    Log and save metrics

    Args:
    - split: one of train, val, test
    - metrics: metrics dict
    - output_dir: where to save the metrics
    """

    logger.info(f"***** {split} metrics *****")
    for key in sorted(metrics.keys()):
        logger.info(f"  {key} = {metrics[key]}")
    save_json(metrics, os.path.join(output_dir, f"{split}_results.json"))


def main():
    # 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, Seq2SeqTrainingArguments))

    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()

    check_output_dir(training_args)

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO if training_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",
        training_args.local_rank,
        training_args.device,
        training_args.n_gpu,
        bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED),
        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()
        transformers.utils.logging.enable_default_handler()
        transformers.utils.logging.enable_explicit_format()
    logger.info("Training/evaluation parameters %s", training_args)

    # Set seed
    set_seed(training_args.seed)

    dataset_class = Seq2SeqDataset

    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,
    )

    # Get datasets
    train_dataset = (
        dataset_class(
            tokenizer,
            type_path="train",
            data_dir=data_args.data_dir,
            n_obs=data_args.n_train,
            max_target_length=data_args.max_target_length,
            max_source_length=data_args.max_source_length,
        )
    )

    eval_dataset = (
        dataset_class(
            tokenizer,
            type_path="val",
            data_dir=data_args.data_dir,
            n_obs=data_args.n_val,
            max_target_length=data_args.max_target_length,
            max_source_length=data_args.max_source_length,
        )
    )

    test_dataset = (
        dataset_class(
            tokenizer,
            type_path="test",
            data_dir=data_args.data_dir,
            n_obs=data_args.n_test,
            max_target_length=data_args.max_target_length,
            max_source_length=data_args.max_source_length,
        )
        if training_args.do_predict
        else None
    )

    # Load pretrained model
    # 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,
    )

    config.temp_start = model_args.temp_start
    config.temp_end = model_args.temp_end
    config.scheduler = model_args.scheduler
    config.accumulation_steps = training_args.gradient_accumulation_steps
    steps_per_epoch = len(train_dataset) // (training_args.per_device_train_batch_size * training_args.gradient_accumulation_steps)
    config.total_steps = steps_per_epoch * training_args.num_train_epochs

    extra_model_params = ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
    for p in extra_model_params:
        if getattr(model_args, p, None):
            assert hasattr(config, p), f"({config.__class__.__name__}) doesn't have a `{p}` attribute"
            setattr(config, p, getattr(model_args, p))

    model = T5.from_pretrained(
        model_args.model_name_or_path,
        from_tf=".ckpt" in model_args.model_name_or_path,
        config=config,
        cache_dir=model_args.cache_dir,
        output_loading_info=False,
    )

    if model_args.add_tokens:
        special_tokens = ["<last_turn>", "<user>", "<agent>", "<grounding>"]
        tokenizer.add_tokens(special_tokens)
        model.resize_token_embeddings(len(tokenizer))

    # use task specific params
    use_task_specific_params(model, data_args.task)

    # set num_beams for evaluation
    if data_args.eval_beams is None:
        data_args.eval_beams = model.config.num_beams

    if model_args.freeze_embeds:
        freeze_embeds(model)
    if model_args.freeze_encoder:
        freeze_params(model.get_encoder())
        assert_all_frozen(model.get_encoder())

    # Initialize our Trainer
    compute_metrics_fn = (
        build_compute_metrics_fn(data_args.task, tokenizer, training_args.output_dir, data_args.data_dir) if training_args.predict_with_generate else None
    )
    trainer = Seq2SeqTrainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset,
        eval_dataset=eval_dataset,
        data_collator=Seq2SeqDataCollator(tokenizer, data_args, training_args.tpu_num_cores),
        compute_metrics=compute_metrics_fn,
        tokenizer=tokenizer,

    )

    all_metrics = {}
    # Training
    if training_args.do_train:
        logger.info("*** Train ***")

        train_result = trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint)
        metrics = train_result.metrics
        metrics["train_n_objs"] = data_args.n_train

        trainer.save_model()  # this also saves the tokenizer

        if trainer.is_world_process_zero():  # Whether or not this process is the global main process
            handle_metrics("train", metrics, training_args.output_dir)
            all_metrics.update(metrics)

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
            trainer.state.save_to_json(os.path.join(training_args.output_dir, "trainer_state.json"))

            # For convenience, we also re-save the tokenizer to the same directory,
            # so that you can share your model easily on huggingface.co/models =)
            tokenizer.save_pretrained(training_args.output_dir)

    # Evaluation
    if training_args.do_eval:
        if model_args.load_checkpoint_from:
            logger.info(f"Loading model from {model_args.load_checkpoint_from}.")
            device = trainer.model.device
            trainer.model = trainer.model.from_pretrained(model_args.load_checkpoint_from, config=config).to(device)

        logger.info("*** Evaluate ***")

        metrics = trainer.evaluate(
            metric_key_prefix="val", max_length=data_args.max_target_length, num_beams=data_args.eval_beams
        )
        metrics["val_n_objs"] = data_args.n_val
        metrics["val_loss"] = round(metrics["val_loss"], 4)

        if trainer.is_world_process_zero():

            handle_metrics("val", metrics, training_args.output_dir)
            all_metrics.update(metrics)

    if training_args.do_predict:
        if model_args.load_checkpoint_from:
            logger.info(f"Loading model from {model_args.load_checkpoint_from}).")
            device = trainer.model.device
            trainer.model = trainer.model.from_pretrained(model_args.load_checkpoint_from, config=config).to(device)

        logger.info("*** Predict ***")

        test_output = trainer.predict(
            test_dataset=test_dataset,
            metric_key_prefix="test",
            max_length=data_args.max_target_length,
            num_beams=data_args.eval_beams,
        )
        metrics = test_output.metrics
        metrics["test_n_objs"] = data_args.n_test

        if trainer.is_world_process_zero():
            metrics["test_loss"] = round(metrics["test_loss"], 4)
            handle_metrics("test", metrics, training_args.output_dir)
            all_metrics.update(metrics)

            if training_args.predict_with_generate:
                test_preds = tokenizer.batch_decode(
                    test_output.predictions, skip_special_tokens=True, clean_up_tokenization_spaces=True
                )
                test_preds = lmap(str.strip, test_preds)
                write_txt_file(test_preds, os.path.join(training_args.output_dir, "test_generations.txt"))

    if trainer.is_world_process_zero():
        save_json(all_metrics, os.path.join(training_args.output_dir, "all_results.json"))

    return all_metrics


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


if __name__ == "__main__":
    main()