run_glue_prune_mod.py

import logging
import os
import sys
import time
import random
from copy import deepcopy

import datasets
import numpy as np
import torch
import transformers
from datasets import load_dataset, load_metric, DatasetDict
from transformers import AutoConfig, AutoTokenizer, EvalPrediction, default_data_collator, DataCollatorWithPadding
from transformers import (HfArgumentParser, TrainingArguments, PretrainedConfig,
                          glue_output_modes, glue_tasks_num_labels, set_seed)

from args import AdditionalArguments, DataTrainingArguments
from utils.cofi_utils import *
from models.l0_module import L0Module
from models.modeling_bert import CoFiBertForSequenceClassification
from models.modeling_roberta import CoFiRobertaForSequenceClassification
from trainer.trainer_mod import CoFiModTrainer 
from utils.utils import *
from models.model_args import ModelArguments

import wandb

task_to_keys = {
    "cola": ("sentence", None),
    "mnli": ("premise", "hypothesis"),
    "mrpc": ("sentence1", "sentence2"),
    "qnli": ("question", "sentence"),
    "qqp": ("question1", "question2"),
    "rte": ("sentence1", "sentence2"),
    "sst2": ("sentence", None),
    "stsb": ("sentence1", "sentence2"),
    "wnli": ("sentence1", "sentence2"),
}

logger = logging.getLogger(__name__)


def main():
    parser = HfArgumentParser(
        (ModelArguments, DataTrainingArguments, TrainingArguments, AdditionalArguments))

    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, additional_args = parser.parse_json_file(
            json_file=os.path.abspath(sys.argv[1]))
        print(sys.argv)
    else:
        model_args, data_args, training_args, additional_args = parser.parse_args_into_dataclasses()
    
    os.makedirs(training_args.output_dir, exist_ok=True)

     # 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}")

    # save args
    torch.save(data_args, os.path.join(
        training_args.output_dir, "data_args.bin"))
    torch.save(model_args, os.path.join(
        training_args.output_dir, "model_args.bin"))
    torch.save(additional_args, os.path.join(
        training_args.output_dir, "additional_args.bin"))

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

    # print all arguments
    log_all_parameters(logger, model_args, data_args,
                       training_args, additional_args)

    
    t_name = None 
    if data_args.task_name is not None:
        # Downloading and loading a dataset from the hub.
        raw_datasets = load_dataset(
            "./glue.py", data_args.task_name.replace("-", ""), cache_dir=model_args.cache_dir)
        t_name = data_args.task_name
    elif 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
        )
        t_name = data_args.dataset_name
    else:
        # Loading a dataset from your local files.
        # CSV/JSON training and evaluation files are needed.
        t_name = data_args.t_name
        data_files = {"train": data_args.train_file,
                      "validation": data_args.validation_file}

        # Get the test dataset: you can provide your own CSV/JSON test file (see below)
        # when you use `do_predict` without specifying a GLUE benchmark task.
        if training_args.do_predict:
            if data_args.test_file is not None:
                train_extension = data_args.train_file.split(".")[-1]
                test_extension = data_args.test_file.split(".")[-1]
                assert (
                    test_extension == train_extension
                ), "`test_file` should have the same extension (csv or json) as `train_file`."
                data_files["test"] = data_args.test_file
            else:
                raise ValueError(
                    "Need either a GLUE task or a test file for `do_predict`.")

        for key in data_files.keys():
            logger.info(f"load a local file for {key}: {data_files[key]}")

        if data_args.train_file.endswith(".csv"):
            # Loading a dataset from local csv files
            raw_datasets = load_dataset(
                "csv", data_files=data_files, cache_dir=model_args.cache_dir)
        elif data_args.train_file.endswith(".tsv"):
            dataset_dict = {}
            for key in data_files:
                dataset_dict[key] = load_from_tsv(data_files[key])
            raw_datasets = DatasetDict(dataset_dict)
        else:
            # Loading a dataset from local json files
            raw_datasets = load_dataset(
                "json", data_files=data_files, cache_dir=model_args.cache_dir)
    # See more about loading any type of standard or custom dataset at
    # https://huggingface.co/docs/datasets/loading_datasets.html.

    # Labels
    if data_args.task_name is not None:
        is_regression = data_args.task_name == "stsb"
        if not is_regression:
            label_list = raw_datasets["train"].features["label"].names
            num_labels = len(label_list)
        else:
            num_labels = 1
    else:
        # Trying to have good defaults here, don't hesitate to tweak to your needs.
        is_regression = raw_datasets["train"].features["label"].dtype in [
            "float32", "float64"]
        if is_regression:
            num_labels = 1
        else:
            # A useful fast method:
            # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.unique
            label_list = raw_datasets["train"].unique("label")
            label_list.sort()  # Let's sort it for determinism
            num_labels = len(label_list)

    config = AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
        num_labels=num_labels,
        finetuning_task=t_name,
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
        use_auth_token=True if model_args.use_auth_token else None,
    )
    
    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,
        local_files_only=True,
    )

    # set up configuration for distillation
    if additional_args.do_distill:
        config.output_attentions = True
        config.output_hidden_states = True

    Model = CoFiBertForSequenceClassification if model_args.model_name_or_path.startswith(
        "bert") else CoFiRobertaForSequenceClassification

    teacher_model = None
    if additional_args.do_distill:
        teacher_model = Model.from_pretrained(
            additional_args.distillation_path,
            config=deepcopy(config)
        )
        teacher_model.eval() #! inside has a cofibertmodel #! CofiBertForSequenceClassification

    config.do_layer_distill = additional_args.do_layer_distill #! True

    model = Model.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,
        use_auth_token=True if model_args.use_auth_token else None,
    ) #! inside the function, we get the original struct  #! CofiBertForSequenceClassification

    # initialize the layer transformation matrix to be an identity matrix
    if additional_args.do_layer_distill:
        initialize_layer_transformation(model)

    logger.info(model)
    logger.info(f"Model size: {calculate_parameters(model)}")

    zs = None
    
    if additional_args.pretrained_pruned_model is not None:
        if additional_args.pruning_type is not None:
            zs = load_zs(additional_args.pretrained_pruned_model)
            model = load_model(additional_args.pretrained_pruned_model, Model, zs)
            print(
                f"Model Size after pruning: {calculate_parameters(model)}")
        else:
            zs = load_zs(os.path.join(additional_args.pretrained_pruned_model, "best"))
            model = load_model(os.path.join(additional_args.pretrained_pruned_model, "best"), Model, zs)
            print(
                f"Model Size after pruning: {calculate_parameters(model)}")

    l0_module = None
    if additional_args.pruning_type is not None:
        l0_module = L0Module(config=config,
                             droprate_init=additional_args.droprate_init,
                             temperature=additional_args.temperature,
                             target_sparsity=additional_args.target_sparsity,
                             pruning_type=additional_args.pruning_type)

    if data_args.task_name is not None:
        sentence1_key, sentence2_key = task_to_keys[data_args.task_name]
    else:
        # Again, we try to have some nice defaults but don't hesitate to tweak to your use case.
        non_label_column_names = [name for name in raw_datasets["train"].column_names if name != "label"]
        if "sentence1" in non_label_column_names and "sentence2" in non_label_column_names:
            sentence1_key, sentence2_key = "sentence1", "sentence2"
        else:
            if len(non_label_column_names) >= 2:
                sentence1_key, sentence2_key = non_label_column_names[:2]
            else:
                sentence1_key, sentence2_key = non_label_column_names[0], None

    # Padding strategy
    if data_args.pad_to_max_length:
        padding = "max_length"
    else:
        # We will pad later, dynamically at batch creation, to the max sequence length in each batch
        padding = False

    # Some models have set the order of the labels to use, so let's make sure we do use it.
    label_to_id = None
    if (
        model.config.label2id != PretrainedConfig(num_labels=num_labels).label2id
        and data_args.task_name is not None
        and not is_regression
    ):
        # Some have all caps in their config, some don't.
        label_name_to_id = {k.lower(): v for k, v in model.config.label2id.items()}
        if list(sorted(label_name_to_id.keys())) == list(sorted(label_list)):
            label_to_id = {i: int(label_name_to_id[label_list[i]]) for i in range(num_labels)}
        else:
            logger.warning(
                "Your model seems to have been trained with labels, but they don't match the dataset: ",
                f"model labels: {list(sorted(label_name_to_id.keys()))}, dataset labels: {list(sorted(label_list))}."
                "\nIgnoring the model labels as a result.",
            )
    elif data_args.task_name is None and not is_regression:
        label_to_id = {v: i for i, v in enumerate(label_list)}

    if label_to_id is not None:
        model.config.label2id = label_to_id
        model.config.id2label = {id: label for label, id in config.label2id.items()}
    elif data_args.task_name is not None and not is_regression:
        model.config.label2id = {l: i for i, l in enumerate(label_list)}
        model.config.id2label = {id: label for label, id in config.label2id.items()}

    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 preprocess_function(examples):
        # Tokenize the texts
        args = (
            (examples[sentence1_key],) if sentence2_key is None else (examples[sentence1_key], examples[sentence2_key])
        )
        result = tokenizer(*args, padding=padding, max_length=max_seq_length, truncation=True)

        # Map labels to IDs (not necessary for GLUE tasks)
        if label_to_id is not None and "label" in examples:
            result["label"] = [(label_to_id[l] if l != -1 else -1) for l in examples["label"]]
        return result

    with training_args.main_process_first(desc="dataset map pre-processing"):
        raw_datasets = raw_datasets.map(
            preprocess_function,
            batched=True,
            load_from_cache_file=not data_args.overwrite_cache,
            desc="Running tokenizer on dataset",
        ) #! get dataset
    
    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:
            train_dataset = train_dataset.select(range(data_args.max_train_samples))

    if training_args.do_eval:
        if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
            raise ValueError("--do_eval requires a validation dataset")
        eval_dataset = raw_datasets["validation_matched" if data_args.task_name == "mnli" else "validation"]
        if data_args.max_eval_samples is not None:
            eval_dataset = eval_dataset.select(range(data_args.max_eval_samples))

    if training_args.do_predict or data_args.task_name is not None or data_args.test_file is not None:
        if "test" not in raw_datasets and "test_matched" not in raw_datasets:
            raise ValueError("--do_predict requires a test dataset")
        predict_dataset = raw_datasets["test_matched" if data_args.task_name == "mnli" else "test"]
        if data_args.max_predict_samples is not None:
            predict_dataset = predict_dataset.select(range(data_args.max_predict_samples))

    # Log a few random samples from the training set:
    if training_args.do_train:
        for index in random.sample(range(len(train_dataset)), 3):
            logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")

    # Get the metric function
    if data_args.task_name is not None:
        metric = load_metric("glue", data_args.task_name)
    else:
        metric = load_metric("accuracy")

    # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
    # predictions and label_ids field) and has to return a dictionary string to float.
    def compute_metrics(p: EvalPrediction):
        preds = p.predictions[0] if isinstance(p.predictions, tuple) else p.predictions
        preds = np.squeeze(preds) if is_regression else np.argmax(preds, axis=1)
        if data_args.task_name is not None:
            result = metric.compute(predictions=preds, references=p.label_ids)
            if len(result) > 1:
                result["combined_score"] = np.mean(list(result.values())).item()
            return result
        elif is_regression:
            return {"mse": ((preds - p.label_ids) ** 2).mean().item()}
        else:
            return {"accuracy": (preds == p.label_ids).astype(np.float32).mean().item()}

    # Data collator will default to DataCollatorWithPadding when the tokenizer is passed to Trainer, so we change it if
    # we already did the padding.
    if data_args.pad_to_max_length:
        data_collator = default_data_collator
    elif training_args.fp16:
        data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8)
    else:
        data_collator = None

    logger.info(
        f"************* {len(train_dataset)} Training Examples Loaded *************")
    logger.info(
        f"************* {len(eval_dataset)} Evaluation Examples Loaded *************")


    trainer = CoFiModTrainer(
        model=model,
        args=training_args,
        additional_args=additional_args,
        train_dataset=train_dataset if training_args.do_train else None,
        eval_dataset=eval_dataset if training_args.do_eval else None,
        compute_metrics=compute_metrics,
        tokenizer=tokenizer,
        data_collator=data_collator,
        l0_module=l0_module,
        teacher_model=teacher_model
    )

    if training_args.do_train:
        trainer.train()
        trainer.save_model()
        tokenizer.save_pretrained(training_args.output_dir)


if __name__ == "__main__":
    # wandb.init(project='Cofi')
    os.environ["WANDB_DISABLED"] = "true"
    t_start = time.time()
    main()
    t_end = time.time()
    logger.info(f"Training took {round(t_end - t_start, 2)} seconds.")