train.py

# Adopted from tatsu-lab@stanford_alpaca. Below is the original copyright:
#    Copyright 2023 Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li
#
#    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.

from dataclasses import dataclass, field
import json
import pathlib
from typing import Dict, Optional, Sequence

import torch
from torch.utils.data import Dataset
import transformers
from transformers import Trainer
from transformers.trainer_pt_utils import LabelSmoother


IGNORE_TOKEN_ID = LabelSmoother.ignore_index
EOS_TOKEN = "</s>"


@dataclass
class ModelArguments:
    model_name_or_path: Optional[str] = field(default="facebook/opt-125m")


@dataclass
class DataArguments:
    data_path: str = field(default=None,
                           metadata={"help": "Path to the training data."})
    lazy_preprocess: bool = False


@dataclass
class TrainingArguments(transformers.TrainingArguments):
    cache_dir: Optional[str] = field(default=None)
    optim: str = field(default="adamw_torch")
    model_max_length: int = field(
        default=512,
        metadata={
            "help":
            "Maximum sequence length. Sequences will be right padded (and possibly truncated)."
        },
    )


local_rank = None

def rank0_print(*args):
    if local_rank == 0:
        print(*args)


def safe_save_model_for_hf_trainer(trainer: transformers.Trainer,
                                   output_dir: str):
    """Collects the state dict and dump to disk."""
    trainer.save_model(output_dir)

def preprocess(
    sources,
    tokenizer: transformers.PreTrainedTokenizer,
) -> Dict:

    # Apply prompt templates
    conversations = []
    trainables = []
    for i, source in enumerate(sources):
        source[0][-1] += " " + EOS_TOKEN
        conversations.append(source[0])
        trainables.append(source[1])
    # Tokenize conversations
    input_ids = tokenizer(
        ["".join(i) for i in conversations],
        return_tensors="pt",
        padding="max_length",
        max_length=2048,
        truncation=True,
    ).input_ids
    targets = input_ids.clone()

    # Mask targets
    for conversation, target, trainable in zip(conversations, targets, trainables):
        total_len = int(target.ne(tokenizer.pad_token_id).sum())
        cur_len = 1
        target[:cur_len] = IGNORE_TOKEN_ID
        for conv, train in zip(conversation, trainable):
            round_len = len(tokenizer(conv).input_ids) - 2
            if conv.endswith(EOS_TOKEN):
                round_len += 1
            if not train:
                target[cur_len:cur_len+round_len] = IGNORE_TOKEN_ID
            cur_len += round_len
        target[cur_len:] = IGNORE_TOKEN_ID

        if cur_len < tokenizer.model_max_length:
            if cur_len != total_len:
                rank0_print(f"WARNING: tokenization mismatch "
                            f"{cur_len} vs. {total_len}")
                rank0_print(conversation)

    return dict(input_ids=input_ids, labels=targets,
                attention_mask=input_ids.ne(tokenizer.pad_token_id))


class SupervisedDataset(Dataset):
    """Dataset for supervised fine-tuning."""

    def __init__(self, data_path: str,
                 tokenizer: transformers.PreTrainedTokenizer):
        super(SupervisedDataset, self).__init__()
        rank0_print("Loading data...")
        list_data_dict = json.load(open(data_path, "r"))

        rank0_print("Formatting inputs...")
        data_dict = preprocess(list_data_dict, tokenizer)

        self.input_ids = data_dict["input_ids"]
        self.labels = data_dict["labels"]
        self.attention_mask = data_dict["attention_mask"]

    def __len__(self):
        return len(self.input_ids)

    def __getitem__(self, i) -> Dict[str, torch.Tensor]:
        return dict(input_ids=self.input_ids[i],
                    labels=self.labels[i],
                    attention_mask=self.attention_mask[i])


class LazySupervisedDataset(Dataset):
    """Dataset for supervised fine-tuning."""

    def __init__(self, data_path: str,
                 tokenizer: transformers.PreTrainedTokenizer):
        super(LazySupervisedDataset, self).__init__()
        self.tokenizer = tokenizer

        rank0_print("Loading data...")
        list_data_dict = json.load(open(data_path, "r"))

        rank0_print("Formatting inputs...Skip in lazy mode")
        self.tokenizer = tokenizer
        self.list_data_dict = list_data_dict
        self.cached_data_dict = {}

    def __len__(self):
        return len(self.list_data_dict)

    def __getitem__(self, i) -> Dict[str, torch.Tensor]:
        if i in self.cached_data_dict:
            return self.cached_data_dict[i]

        ret = preprocess([self.list_data_dict[i]], self.tokenizer)
        ret = dict(
            input_ids=ret["input_ids"][0],
            labels=ret["labels"][0],
            attention_mask=ret["attention_mask"][0],
        )
        self.cached_data_dict[i] = ret

        return ret


def make_supervised_data_module(tokenizer: transformers.PreTrainedTokenizer,
                                data_args) -> Dict:
    """Make dataset and collator for supervised fine-tuning."""
    dataset_cls = (LazySupervisedDataset
                   if data_args.lazy_preprocess else SupervisedDataset)
    train_dataset = dataset_cls(tokenizer=tokenizer,
                                data_path=data_args.data_path)
    return dict(train_dataset=train_dataset,
                eval_dataset=None)


def train():
    global local_rank

    parser = transformers.HfArgumentParser(
        (ModelArguments, DataArguments, TrainingArguments))
    model_args, data_args, training_args = parser.parse_args_into_dataclasses()
    local_rank = training_args.local_rank
    model = transformers.AutoModelForCausalLM.from_pretrained(
        model_args.model_name_or_path,
        cache_dir=training_args.cache_dir,
    )
    tokenizer = transformers.AutoTokenizer.from_pretrained(
        model_args.model_name_or_path,
        cache_dir=training_args.cache_dir,
        model_max_length=training_args.model_max_length,
        padding_side="right",
        use_fast=False,
    )
    tokenizer.pad_token = tokenizer.unk_token

    data_module = make_supervised_data_module(tokenizer=tokenizer,
                                              data_args=data_args)
    trainer = Trainer(model=model,
                      tokenizer=tokenizer,
                      args=training_args,
                      **data_module)

    if list(pathlib.Path(training_args.output_dir).glob("checkpoint-*")):
        trainer.train(resume_from_checkpoint=True)
    else:
        trainer.train()
    trainer.save_state()
    safe_save_model_for_hf_trainer(trainer=trainer,
                                   output_dir=training_args.output_dir)


if __name__ == "__main__":
    train()