run_eval.py

#!/usr/bin/env python
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. 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 causal language modeling (GPT, GPT-2, CTRL, ...) on a text file or a dataset.

Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
https://huggingface.co/models?filter=text-generation
"""
# You can also adapt this script on your own causal language modeling task. Pointers for this are left as comments.

import logging
import math
import os
import sys
import warnings
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional

import datasets
import evaluate
import torch
from datasets import load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_CAUSAL_LM_MAPPING,
    AutoConfig,
    AutoModelForCausalLM,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    is_torch_tpu_available,
    set_seed,
)
from transformers.testing_utils import CaptureLogger
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version

import wandb
import copy
import builtins
from typing import Any, Dict
from transformers.trainer_pt_utils import _secs2timedelta
import pandas as pd

original_import = builtins.__import__


# def custom_import(name, globals=None, locals=None, fromlist=(), level=0):
#     if name == "flash_attn":
#         raise ImportError("Library is not available")
#     return original_import(name, globals, locals, fromlist, level)


# builtins.__import__ = custom_import

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
# check_min_version("4.36.0.dev0")

require_version(
    "datasets>=1.8.0",
    "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt",
)

logger = logging.getLogger(__name__)
wandb.login(key="<your-wandb-key>")
wandb.init(project="LLMUnlearn")

MODEL_CONFIG_CLASSES = list(MODEL_FOR_CAUSAL_LM_MAPPING.keys())
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)


@dataclass
class ModelArguments:
    """
    Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
    """

    model_name_or_path: Optional[str] = field(
        default=None,
        metadata={
            "help": (
                "The model checkpoint for weights initialization. Don't set if you want to train a model from scratch."
            )
        },
    )
    model_type: Optional[str] = field(
        default=None,
        metadata={
            "help": "If training from scratch, pass a model type from the list: "
            + ", ".join(MODEL_TYPES)
        },
    )
    config_overrides: Optional[str] = field(
        default=None,
        metadata={
            "help": (
                "Override some existing default config settings when a model is trained from scratch. Example: "
                "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
            )
        },
    )
    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"
        },
    )
    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)."
        },
    )
    token: str = field(
        default=None,
        metadata={
            "help": (
                "The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
                "generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
            )
        },
    )
    use_auth_token: bool = field(
        default=None,
        metadata={
            "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead."
        },
    )
    trust_remote_code: bool = field(
        default=False,
        metadata={
            "help": (
                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
                "should only be set to `True` for repositories you trust and in which you have read the code, as it will "
                "execute code present on the Hub on your local machine."
            )
        },
    )
    torch_dtype: Optional[str] = field(
        default=None,
        metadata={
            "help": (
                "Override the default `torch.dtype` and load the model under this dtype. If `auto` is passed, the "
                "dtype will be automatically derived from the model's weights."
            ),
            "choices": ["auto", "bfloat16", "float16", "float32"],
        },
    )
    low_cpu_mem_usage: bool = field(
        default=False,
        metadata={
            "help": (
                "It is an option to create the model as an empty shell, then only materialize its parameters when the pretrained weights are loaded. "
                "set True will benefit LLM loading time and RAM consumption."
            )
        },
    )
    model_max_length: int = field(
        default=512,
        metadata={
            "help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)."
        },
    )

    def __post_init__(self):
        if self.config_overrides is not None and (
            self.config_name is not None or self.model_name_or_path is not None
        ):
            raise ValueError(
                "--config_overrides can't be used in combination with --config_name or --model_name_or_path"
            )


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

    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)."
        },
    )
    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)."
        },
    )
    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."
            )
        },
    )
    streaming: bool = field(default=False, metadata={"help": "Enable streaming mode"})
    block_size: Optional[int] = field(
        default=None,
        metadata={
            "help": (
                "Optional input sequence length after tokenization. "
                "The training dataset will be truncated in block of this size for training. "
                "Default to the model max input length for single sentence inputs (take into account special tokens)."
            )
        },
    )
    overwrite_cache: bool = field(
        default=False,
        metadata={"help": "Overwrite the cached training and evaluation sets"},
    )
    validation_split_percentage: Optional[int] = field(
        default=5,
        metadata={
            "help": "The percentage of the train set used as validation set in case there's no validation split"
        },
    )
    preprocessing_num_workers: Optional[int] = field(
        default=None,
        metadata={"help": "The number of processes to use for the preprocessing."},
    )
    keep_linebreaks: bool = field(
        default=True,
        metadata={"help": "Whether to keep line breaks when using TXT files or not."},
    )
    domain: str =field(
        default = None,
        metadata={"help": "The unlearned domain."}
    )


class CustomTrainer(Trainer):
    def metrics_format(self, metrics: Dict[str, float]) -> Dict[str, float]:
        """
        Reformat Trainer metrics values to a human-readable format

        Args:
            metrics (`Dict[str, float]`):
                The metrics returned from train/evaluate/predict

        Returns:
            metrics (`Dict[str, float]`): The reformatted metrics
        """

        metrics_copy = metrics.copy()
        for k, v in metrics_copy.items():
            if "_mem_" in k:
                metrics_copy[k] = f"{ v >> 20 }MB"
            elif "_runtime" in k:
                metrics_copy[k] = _secs2timedelta(v)
            elif k == "total_flos":
                metrics_copy[k] = f"{ int(v) >> 30 }GF"
            elif type(metrics_copy[k]) == float:
                metrics_copy[k] = round(v, 2)

        return metrics_copy


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, TrainingArguments)
    )
    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()
    wandb.run.name = "eval-" + model_args.model_name_or_path.replace(
        "./output/", "", 1
    )
    if model_args.use_auth_token is not None:
        warnings.warn(
            "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead.",
            FutureWarning,
        )
        if model_args.token is not None:
            raise ValueError(
                "`token` and `use_auth_token` are both specified. Please set only the argument `token`."
            )
        model_args.token = model_args.use_auth_token

    # 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)],
    )

    if training_args.should_log:
        # The default of training_args.log_level is passive, so we set log level at info here to have that default.
        transformers.utils.logging.set_verbosity_info()

    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: {training_args.parallel_mode.value == 'distributed'}, 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)

    # 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_kwargs = {
        "cache_dir": model_args.cache_dir,
        "revision": model_args.model_revision,
        "token": model_args.token,
        "trust_remote_code": model_args.trust_remote_code,
    }
    if model_args.config_name:
        config = AutoConfig.from_pretrained(model_args.config_name, **config_kwargs)
    elif model_args.model_name_or_path:
        config = AutoConfig.from_pretrained(
            model_args.model_name_or_path, **config_kwargs
        )
    else:
        config = CONFIG_MAPPING[model_args.model_type]()
        logger.warning("You are instantiating a new config instance from scratch.")
        if model_args.config_overrides is not None:
            logger.info(f"Overriding config: {model_args.config_overrides}")
            config.update_from_string(model_args.config_overrides)
            logger.info(f"New config: {config}")

    tokenizer_kwargs = {
        "cache_dir": model_args.cache_dir,
        "use_fast": model_args.use_fast_tokenizer,
        "revision": model_args.model_revision,
        "token": model_args.token,
        "trust_remote_code": model_args.trust_remote_code,
        "padding_side": "right",
    }
    if model_args.tokenizer_name:
        tokenizer = AutoTokenizer.from_pretrained(
            model_args.tokenizer_name, **tokenizer_kwargs
        )
    elif model_args.model_name_or_path:
        tokenizer = AutoTokenizer.from_pretrained(
            model_args.model_name_or_path, **tokenizer_kwargs
        )
    else:
        raise ValueError(
            "You are instantiating a new tokenizer from scratch. This is not supported by this script. "
            "You can do it from another script, save it, and load it from here, using --tokenizer_name."
        )

    if model_args.model_name_or_path:
        torch_dtype = (
            model_args.torch_dtype
            if model_args.torch_dtype in ["auto", None]
            else getattr(torch, model_args.torch_dtype)
        )
        model = AutoModelForCausalLM.from_pretrained(
            model_args.model_name_or_path,
            from_tf=bool(".ckpt" in model_args.model_name_or_path),
            config=config,
            cache_dir=model_args.cache_dir,
            revision=model_args.model_revision,
            token=model_args.token,
            trust_remote_code=model_args.trust_remote_code,
            torch_dtype=torch_dtype,
            low_cpu_mem_usage=model_args.low_cpu_mem_usage,
        )
    else:
        model = AutoModelForCausalLM.from_config(
            config, trust_remote_code=model_args.trust_remote_code
        )
        n_params = sum({p.data_ptr(): p.numel() for p in model.parameters()}.values())
        logger.info(
            f"Training new model from scratch - Total size={n_params/2**20:.2f}M params"
        )

    # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch
    # on a small vocab and want a smaller embedding size, remove this test.
    embedding_size = model.get_input_embeddings().weight.shape[0]
    if len(tokenizer) > embedding_size:
        model.resize_token_embeddings(len(tokenizer))

    if data_args.domain == "arxiv":
        forget_dataset = torch.load(
            "./tokenized_dataset/arxiv/arxiv_forget_500/normal/tokenized_dataset.pt"
        )
    elif data_args.domain == "github":
        forget_dataset = torch.load(
            "./tokenized_dataset/github/github_forget_2k/normal/tokenized_dataset.pt"
        )
    else:
        raise ValueError(f"Invalid domain: {data_args.domain}. Supported domains are 'arxiv' and 'github'.")
    general_dataset = torch.load(
            "./tokenized_dataset/general/general_1k/normal/tokenized_dataset.pt"
        )
    dataset_dict = {
        "forget": forget_dataset,
        "general": general_dataset,
    }

    if training_args.do_eval:

        def preprocess_logits_for_metrics(logits, labels):
            if isinstance(logits, tuple):
                logits = logits[0]
            labels_cloned = labels.clone()[:, 1:]
            pad_token_mask = labels_cloned != -100
            input_ids_expanded = labels_cloned.unsqueeze(-1)
            log_probs = torch.log_softmax(logits, dim=-1)
            log_probs = log_probs[:, :-1]
            input_ids_expanded[input_ids_expanded == -100] = 0  
            selected_log_probs = log_probs.gather(
                2, input_ids_expanded
            ) * pad_token_mask.unsqueeze(-1)
            pred = logits.argmax(dim=-1)[:, :-1]

            return torch.cat((selected_log_probs.squeeze(-1), pred), 1)

        def compute_min_k_ppl_acc(selected_log_probs, mask, k, predicts_mask):
            average_log_probs = []
            average_accs = []
            for sample_log_probs, sample_mask, sample_predicts_mask in zip(
                selected_log_probs, mask, predicts_mask
            ):
                sample_log_probs_nonpad = sample_log_probs[sample_mask]
                sample_log_probs[~sample_mask] = 100
                k_value = int(k * sample_log_probs_nonpad.size)  
                if k_value > 0:
                    topk_results = torch.topk(
                        torch.tensor(sample_log_probs).squeeze(), k_value, largest=False
                    )
                    min_k_log_probs = topk_results.values
                    topk_indices = topk_results.indices
                    sample_average_log_prob = min_k_log_probs.mean()
                    average_log_probs.append(sample_average_log_prob)
                    sample_acc = sample_predicts_mask[topk_indices].mean()
                    average_accs.append(sample_acc)
            average_log_probs = torch.stack(average_log_probs)
            total_average_log_prob = average_log_probs.sum() / len(average_log_probs)
            ppl = torch.exp(-total_average_log_prob)

            # compute the acc:
            # average_accs = torch.stack(average_accs)
            acc = (sum(average_accs) / len(average_accs)) * 100
            return ppl.item(), acc.item()

        def compute_metrics(eval_preds):
            preds, labels = eval_preds
            selected_log_probs = preds[:, : int(preds.shape[1] / 2)]
            predicts = preds[:, int(preds.shape[1] / 2) :]
            labels_clone = labels.copy()[:, 1:]
            pad_token_mask = labels_clone != -100
            predicts_mask = predicts == labels_clone
            result = {}
            for ratio in [1]:
                ppl_value, acc_value = compute_min_k_ppl_acc(
                    selected_log_probs, pad_token_mask, ratio, predicts_mask
                )
                # result[f"min_{int(ratio*100)}_ppl"] = ppl_value
                # result[f"min_{int(ratio*100)}_acc"] = acc_value
                result[f"ppl"] = ppl_value
                result[f"acc"] = acc_value
            return result

    # Initialize our Trainer
    trainer = CustomTrainer(
        model=model,
        args=training_args,
        train_dataset=None,
        eval_dataset=None,
        tokenizer=tokenizer,
        # Data collator will default to DataCollatorWithPadding, so we change it.
        # data_collator=default_data_collator,
        compute_metrics=compute_metrics
        if training_args.do_eval and not is_torch_tpu_available()
        else None,
        preprocess_logits_for_metrics=preprocess_logits_for_metrics
        if training_args.do_eval and not is_torch_tpu_available()
        else None,
    )

    # Evaluation
    if training_args.do_eval:
        logger.info("*** Evaluate ***")
        for key, eval_dataset in dataset_dict.items():
            if data_args.max_eval_samples is not None:
                max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
                eval_dataset = eval_dataset.select(range(max_eval_samples))
            metrics = trainer.evaluate(eval_dataset)

            max_eval_samples = (
                data_args.max_eval_samples
                if data_args.max_eval_samples is not None
                else len(eval_dataset)
            )
            metrics[f"eval_samples"] = min(max_eval_samples, len(eval_dataset))
            try:
                perplexity = math.exp(metrics["eval_loss"])
            except OverflowError:
                perplexity = float("inf")
            metrics["perplexity"] = perplexity

            trainer.log_metrics(f"{key}_eval", metrics)
            trainer.save_metrics(f"{key}_eval", metrics)

    kwargs = {
        "finetuned_from": model_args.model_name_or_path,
        "tasks": "text-generation",
    }
    if data_args.dataset_name is not None:
        kwargs["dataset_tags"] = data_args.dataset_name
        if data_args.dataset_config_name is not None:
            kwargs["dataset_args"] = data_args.dataset_config_name
            kwargs[
                "dataset"
            ] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
        else:
            kwargs["dataset"] = data_args.dataset_name

    if training_args.push_to_hub:
        trainer.push_to_hub(**kwargs)
    else:
        trainer.create_model_card(**kwargs)


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


if __name__ == "__main__":
    main()