finetune.py

#!/usr/bin/env python
# coding=utf-8
"""
Fine-tuning CodeGen models on the input data.
Adapted from a HuggingFace transformers example for training seq2seq models.

Assumes that CodeGen model checkpoints are stored in {data_args.codegen_repo}/codegen-[6B|16B]-mono.
"""
import os

import sys

import logging
import torch
from dataclasses import dataclass, field
from typing import Dict, List, Optional

import datasets
from datasets import load_dataset, load_metric, DatasetDict

from jaxformer.hf import sample  # from the CodeGen repository
from jaxformer.hf.codegen import modeling_codegen  # from the CodeGen repository

import transformers
from transformers import (
    DataCollatorForSeq2Seq,
    HfArgumentParser,
    Seq2SeqTrainer,
    Seq2SeqTrainingArguments,
    set_seed,
)
from transformers.trainer_utils import (
    get_last_checkpoint,
)

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(
        default=None, metadata={"help": "Can be codegen-16B, or codegen-6B."}
    )
    config_name: Optional[str] = field(
        default=None,
        metadata={
            "help": "Pretrained config name or path if not the same as model_name"
        },
    )
    cache_dir: Optional[str] = field(
        default=None,
        metadata={
            "help": "Path to directory 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 `transformers-cli login` (necessary to use this script "
            "with private models)."
        },
    )
    parallelize: bool = field(
        default=False,
    )


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

    codegen_repo: Optional[str] = field(
        default=None,
        metadata={"help": "Path to the cloned SalesForce codegen repo."},
    )
    dataset_name: Optional[str] = field(
        default=None,
        metadata={"help": "The name of the dataset to use (via the datasets library)."},
    )
    dataset_config_name: Optional[str] = field(
        default=None,
        metadata={
            "help": "The configuration name of the dataset to use (via the datasets library)."
        },
    )
    prompt_column: Optional[str] = field(
        default="finetuning_prompt",
        metadata={
            "help": "The name of the column in the datasets containing the task prompt."
        },
    )
    completion_column: Optional[str] = field(
        default="finetuning_completion",
        metadata={
            "help": "The name of the column in the datasets containing the refinement of the code."
        },
    )
    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 to evaluate the perplexity on (a text file)."
        },
    )
    test_file: Optional[str] = field(
        default=None,
        metadata={
            "help": "An optional input test data file to evaluate the perplexity on (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: 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_answer_length: int = field(
        default=1024,
        metadata={
            "help": "The maximum length of an answer that can be generated. This is needed because the start "
            "and end predictions are not conditioned on one another."
        },
    )
    val_max_answer_length: Optional[int] = field(
        default=None,
        metadata={
            "help": "The maximum total sequence length for validation target text after tokenization. Sequences longer "
            "than this will be truncated, sequences shorter will be padded. Will default to `max_answer_length`."
            "This argument is also used to override the ``max_length`` param of ``model.generate``, which is used "
            "during ``evaluate`` and ``predict``."
        },
    )
    pad_to_max_length: bool = field(
        default=True,
        metadata={
            "help": "Whether to pad all samples to `max_seq_length`. "
            "If False, will pad the samples dynamically when batching to the maximum length in the batch (which can "
            "be faster on GPU but will be slower on 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."
        },
    )
    max_predict_samples: Optional[int] = field(
        default=None,
        metadata={
            "help": "For debugging purposes or quicker training, truncate the number of prediction examples to this "
            "value if set."
        },
    )
    version_2_with_negative: bool = field(
        default=False,
        metadata={"help": "If true, some of the examples do not have an answer."},
    )
    null_score_diff_threshold: float = field(
        default=0.0,
        metadata={
            "help": "The threshold used to select the null answer: if the best answer has a score that is less than "
            "the score of the null answer minus this threshold, the null answer is selected for this example. "
            "Only useful when `version_2_with_negative=True`."
        },
    )
    doc_stride: int = field(
        default=128,
        metadata={
            "help": "When splitting up a long document into chunks, how much stride to take between chunks."
        },
    )
    n_best_size: int = field(
        default=20,
        metadata={
            "help": "The total number of n-best predictions to generate when looking for an answer."
        },
    )
    num_beams: Optional[int] = field(
        default=5,
        metadata={
            "help": "Number of beams to use for evaluation. This argument will be passed to ``model.generate``, "
            "which is used during ``evaluate`` and ``predict``."
        },
    )
    ignore_pad_token_for_loss: bool = field(
        default=True,
        metadata={
            "help": "Whether to ignore the tokens corresponding to padded labels in the loss computation or not."
        },
    )

    def __post_init__(self):
        if (
            self.dataset_name is None
            and self.train_file is None
            and self.validation_file is None
            and self.test_file is None
        ):
            raise ValueError(
                "Need either a dataset name or a training/validation file/test_file."
            )
        else:
            if self.train_file is not None:
                extension = self.train_file.split(".")[-1]
                assert extension in [
                    "csv",
                    "json",
                    "jsonl",
                ], "`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."
            if self.test_file is not None:
                extension = self.test_file.split(".")[-1]
                assert extension in [
                    "csv",
                    "json",
                ], "`test_file` should be a csv or a json file."
        if self.val_max_answer_length is None:
            self.val_max_answer_length = self.max_answer_length


question_answering_column_name_mapping = {
    "squad_v2": ("question", "context", "answer"),
}


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()

    # 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.dataset_name is not None:
        # Downloading and loading a dataset from the hub.
        raw_datasets = load_dataset(
            data_args.dataset_name,
            data_args.dataset_config_name,
            cache_dir=model_args.cache_dir,
        )
    else:
        data_files = {}
        if data_args.train_file is not None:
            data_files["train"] = data_args.train_file
            extension = data_args.train_file.split(".")[-1]
            if extension == "jsonl":
                extension = "json"

        if data_args.validation_file is not None:
            data_files["validation"] = data_args.validation_file
            extension = data_args.validation_file.split(".")[-1]
        if data_args.test_file is not None:
            data_files["test"] = data_args.test_file
            extension = data_args.test_file.split(".")[-1]
        # raw_datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)
        if extension == "json":
            raw_datasets = DatasetDict.from_json(data_files)
        else:
            raw_datasets = DatasetDict.from_csv(data_files)
    # 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.

    if model_args.model_name_or_path.startswith("codegen-"):
        if last_checkpoint is not None:
            model = modeling_codegen.CodeGenForCausalLM.from_pretrained(
                last_checkpoint, low_cpu_mem_usage=True
            )
        else:
            model = modeling_codegen.CodeGenForCausalLM.from_pretrained(
                f"{data_args.codegen_repo}/{model_args.model_name_or_path}-mono",
                low_cpu_mem_usage=True,
            )
        ## IMPORTANT: DO NOT REMOVE
        model = model.to(torch.float32)

        tokenizer = sample.create_custom_gpt2_tokenizer()
        #         tokenizer.padding_side = 'left'
        tokenizer.pad_token = 50256
        if model_args.parallelize:
            model.parallelize()
        else:
            model = model.cuda()
    else:
        raise ValueError(
            f"{model_args.model_name_or_path} is not a valid model name or path."
        )

    model.resize_token_embeddings(len(tokenizer))

    # Preprocessing the datasets.
    # We need to generate and tokenize inputs and targets.
    if training_args.do_train:
        column_names = list(raw_datasets["train"].features.keys())
    elif training_args.do_eval:
        column_names = list(raw_datasets["validation"].features.keys())
    elif training_args.do_predict:
        column_names = list(raw_datasets["test"].features.keys())
    else:
        logger.info(
            "There is nothing to do. Please pass `do_train`, `do_eval` and/or `do_predict`."
        )
        return

    # Get the column names for input/target.
    dataset_columns = question_answering_column_name_mapping.get(
        data_args.dataset_name, None
    )
    if data_args.prompt_column is None:
        prompt_column = (
            dataset_columns[0] if dataset_columns is not None else column_names[0]
        )
    else:
        prompt_column = data_args.prompt_column
        if prompt_column not in column_names:
            raise ValueError(
                f"--prompt_column' value '{data_args.prompt_column}' needs to be one of: {', '.join(column_names)}"
            )
    if data_args.completion_column is None:
        completion_column = (
            dataset_columns[2] if dataset_columns is not None else column_names[2]
        )
    else:
        completion_column = data_args.completion_column
        if completion_column not in column_names:
            raise ValueError(
                f"--completion_column' value '{data_args.completion_column}' needs to be one of: {', '.join(column_names)}"
            )

    # Temporarily set max_answer_length for training.
    max_answer_length = data_args.max_answer_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.warning(
            "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"
        )

    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)

    def truncate(ex, tokenizer, max_length):
        return tokenizer.decode(
            tokenizer(ex, max_length=max_length, truncation=True).input_ids
        )

    def preprocess_example(example):
        input_str = truncate(example[prompt_column], tokenizer, max_seq_length)
        r = example[completion_column]
        input_token_ids = tokenizer.encode(input_str, verbose=False)
        target_token_ids = tokenizer.encode(r, verbose=False) + [tokenizer.eos_token_id]
        input_ids = input_token_ids + target_token_ids
        labels_input_ids = ([-100] * len(input_token_ids)) + target_token_ids

        if len(input_ids) > max_seq_length:
            input_ids = input_ids[:max_seq_length]
            labels_input_ids = labels_input_ids[:max_seq_length]
        return {
            "input_ids": torch.IntTensor(input_ids).cuda(),
            "labels": torch.IntTensor(labels_input_ids).cuda(),
        }

    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))
        with training_args.main_process_first(desc="train dataset map pre-processing"):
            train_dataset = train_dataset.map(
                preprocess_example,
                remove_columns=column_names,
            )
        if data_args.max_train_samples is not None:
            # Number of samples might increase during Feature Creation, We select only specified max samples
            max_train_samples = min(len(train_dataset), data_args.max_train_samples)
            train_dataset = train_dataset.select(range(max_train_samples))

    # 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,
    )

    # Initialize our Trainer
    trainer = Seq2SeqTrainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset if training_args.do_train else None,
        tokenizer=tokenizer,
        data_collator=data_collator,
    )

    old_collator = trainer.data_collator
    trainer.data_collator = lambda data: dict(old_collator(data))

    # Training
    if training_args.do_train:
        train_result = trainer.train()
        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()


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


if __name__ == "__main__":
    main()