next-event-prediction/test.py

import argparse
import json
import math
import os
import random
from dataclasses import dataclass
from itertools import chain
from pathlib import Path
from typing import Optional, Union

import datasets
import torch
from datasets import load_dataset
from torch.utils.data import DataLoader
from tqdm.auto import tqdm

import evaluate
import transformers
from accelerate import Accelerator
from accelerate.utils import set_seed
from huggingface_hub import Repository
from transformers import (
    CONFIG_MAPPING,
    MODEL_MAPPING,
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    PreTrainedTokenizerBase,
    SchedulerType,
    default_data_collator,
    get_scheduler,
)
from transformers.utils import PaddingStrategy, check_min_version, get_full_repo_name, send_example_telemetry


# You should update this to your particular problem to have better documentation of `model_type`
MODEL_CONFIG_CLASSES = list(MODEL_MAPPING.keys())
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)


def parse_args():
    parser = argparse.ArgumentParser(description="Finetune a transformers model on a multiple choice task")
    parser.add_argument(
        "--dataset_name",
        type=str,
        default=None,
        help="The name of the dataset to use (via the datasets library).",
    )
    parser.add_argument(
        "--dataset_config_name",
        type=str,
        default=None,
        help="The configuration name of the dataset to use (via the datasets library).",
    )
    parser.add_argument(
        "--test_file", type=str, default=None, help="A csv or a json file containing the test data."
    )
    parser.add_argument(
        "--max_seq_length",
        type=int,
        default=128,
        help=(
            "The maximum total input sequence length after tokenization. Sequences longer than this will be truncated,"
            " sequences shorter will be padded if `--pad_to_max_lengh` is passed."
        ),
    )
    parser.add_argument(
        "--pad_to_max_length",
        action="store_true",
        help="If passed, pad all samples to `max_length`. Otherwise, dynamic padding is used.",
    )
    parser.add_argument(
        "--model_name_or_path",
        type=str,
        help="Path to pretrained model or model identifier from huggingface.co/models.",
        required=False,
    )
    parser.add_argument(
        "--config_name",
        type=str,
        default=None,
        help="Pretrained config name or path if not the same as model_name",
    )
    parser.add_argument(
        "--tokenizer_name",
        type=str,
        default=None,
        help="Pretrained tokenizer name or path if not the same as model_name",
    )
    parser.add_argument(
        "--per_device_test_batch_size",
        type=int,
        default=8,
        help="Batch size (per device) for the test dataloader.",
    )
    parser.add_argument(
        "--gradient_accumulation_steps",
        type=int,
        default=1,
        help="Gradient accumulation steps."
    )
    parser.add_argument("--use_slow_tokenizer")
    parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
    parser.add_argument(
        "--model_type",
        type=str,
        default=None,
        help="Model type to use if training from scratch.",
        choices=MODEL_TYPES,
    )
    parser.add_argument(
        "--debug",
        action="store_true",
        help="Activate debug mode and run training only with a subset of data.",
    )

    args = parser.parse_args()
    return args


@dataclass
class DataCollatorForMultipleChoice:
    """
    Data collator that will dynamically pad the inputs for multiple choice received.
    Args:
        tokenizer ([`PreTrainedTokenizer`] or [`PreTrainedTokenizerFast`]):
            The tokenizer used for encoding the data.
        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `True`):
            Select a strategy to pad the returned sequences (according to the model's padding side and padding index)
            among:
            - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
              if provided).
            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided.
            - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
              lengths).
        max_length (`int`, *optional*):
            Maximum length of the returned list and optionally padding length (see above).
        pad_to_multiple_of (`int`, *optional*):
            If set will pad the sequence to a multiple of the provided value.
            This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
            7.5 (Volta).
    """

    tokenizer: PreTrainedTokenizerBase
    padding: Union[bool, str, PaddingStrategy] = True
    max_length: Optional[int] = None
    pad_to_multiple_of: Optional[int] = None

    def __call__(self, features):
        label_name = "label" if "label" in features[0].keys() else "labels"
        labels = [feature.pop(label_name) for feature in features]
        batch_size = len(features)
        num_choices = len(features[0]["input_ids"])
        flattened_features = [
            [{k: v[i] for k, v in feature.items()} for i in range(num_choices)] for feature in features
        ]
        flattened_features = list(chain(*flattened_features))

        batch = self.tokenizer.pad(
            flattened_features,
            padding=self.padding,
            max_length=self.max_length,
            pad_to_multiple_of=self.pad_to_multiple_of,
            return_tensors="pt",
        )

        # Un-flatten
        batch = {k: v.view(batch_size, num_choices, -1) for k, v in batch.items()}
        # Add back labels
        batch["labels"] = torch.tensor(labels, dtype=torch.int64)
        return batch


def main():
    args = parse_args()

    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
    # information sent is the one passed as arguments along with your Python/PyTorch versions.
    send_example_telemetry("run_swag_no_trainer", args)

    # Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
    # If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers
    # in the environment
    accelerator_log_kwargs = {}
    accelerator = Accelerator(gradient_accumulation_steps=args.gradient_accumulation_steps, **accelerator_log_kwargs)

    # If passed along, set the training seed now.
    if args.seed is not None:
        set_seed(args.seed)

    accelerator.wait_for_everyone()

    # 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 args.dataset_name is not None:
        # Downloading and loading a dataset from the hub.
        raw_datasets = load_dataset(args.dataset_name, args.dataset_config_name)
    else:
        data_files = {}
        if args.test_file is not None:
            data_files["test"] = args.test_file
        extension = args.test_file.split(".")[-1]
        raw_datasets = load_dataset(extension, data_files=data_files)

    if args.debug:
        for split in raw_datasets.keys():
            raw_datasets[split] = raw_datasets[split].select(range(100))
    # 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.


    column_names = raw_datasets["test"].column_names

    # When using your own dataset or a different dataset from swag, you will probably need to change this.
    ending_names = [f"ending{i}" for i in range(4)]
    context_name = "sent1"
    question_header_name = "sent2"
    label_column_name = "label" if "label" in column_names else "labels"

    # Load pretrained model and tokenizer
    #
    # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    if args.config_name:
        config = AutoConfig.from_pretrained(args.model_name_or_path)
    elif args.model_name_or_path:
        config = AutoConfig.from_pretrained(args.model_name_or_path)
    else:
        config = CONFIG_MAPPING[args.model_type]()

    if args.tokenizer_name:
        tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=not args.use_slow_tokenizer)
    elif args.model_name_or_path:
        tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, use_fast=not args.use_slow_tokenizer)
    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 args.model_name_or_path:
        model = AutoModelForMultipleChoice.from_pretrained(
            args.model_name_or_path,
            from_tf=bool(".ckpt" in args.model_name_or_path),
            config=config,
        )
    else:
        model = AutoModelForMultipleChoice.from_config(config)

    # 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))

    # Preprocessing the datasets.
    # First we tokenize all the texts.
    padding = "max_length" if args.pad_to_max_length else False

    def preprocess_function(examples):
        first_sentences = [[context] * 4 for context in examples[context_name]]
        question_headers = examples[question_header_name]
        second_sentences = [
            [f"{header} {examples[end][i]}" for end in ending_names] for i, header in enumerate(question_headers)
        ]
        labels = examples[label_column_name]

        # Flatten out
        first_sentences = list(chain(*first_sentences))
        second_sentences = list(chain(*second_sentences))

        # Tokenize
        tokenized_examples = tokenizer(
            first_sentences,
            second_sentences,
            max_length=args.max_seq_length,
            padding=padding,
            truncation=True,
        )
        # Un-flatten
        tokenized_inputs = {k: [v[i : i + 4] for i in range(0, len(v), 4)] for k, v in tokenized_examples.items()}
        tokenized_inputs["labels"] = labels
        return tokenized_inputs

    with accelerator.main_process_first():
        processed_datasets = raw_datasets.map(
            preprocess_function, batched=True, remove_columns=raw_datasets["test"].column_names
        )

    test_dataset = processed_datasets["test"]

    # DataLoaders creation:
    if args.pad_to_max_length:
        # If padding was already done ot max length, we use the default data collator that will just convert everything
        # to tensors.
        data_collator = default_data_collator

    else:
        # Otherwise, `DataCollatorWithPadding` will apply dynamic padding for us (by padding to the maximum length of
        # the samples passed). When using mixed precision, we add `pad_to_multiple_of=8` to pad all tensors to multiple
        # of 8s, which will enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).
        data_collator = DataCollatorForMultipleChoice(
            tokenizer, pad_to_multiple_of=(8 if accelerator.use_fp16 else None)
        )

    test_dataloader = DataLoader(test_dataset, collate_fn=data_collator, batch_size=args.per_device_test_batch_size)

    # Use the device given by the `accelerator` object.
    device = accelerator.device
    model.to(device)

    # Prepare everything with our `accelerator`.
    model, test_dataloader = accelerator.prepare(
        model, test_dataloader
    )

    # Metrics

    acc = evaluate.load("accuracy")	
    prec = evaluate.load("precision")
    rec = evaluate.load("recall")
    f1 = evaluate.load("f1")

    model.eval()
    for step, batch in tqdm(enumerate(test_dataloader)):
        with torch.no_grad():
            outputs = model(**batch)
        predictions = outputs.logits.argmax(dim=-1)
        predictions, references = accelerator.gather_for_metrics((predictions, batch["labels"]))
        acc.add_batch(
            predictions=predictions,
            references=references,
        )
        prec.add_batch(
            predictions=predictions,
            references=references,
        )
        rec.add_batch(
            predictions=predictions,
            references=references,
        )
        f1.add_batch(
            predictions=predictions,
            references=references,
        )
    acc_metric = acc.compute()
    prec_metric = prec.compute(average="weighted")
    rec_metric = rec.compute(average="weighted")
    f1_metric = f1.compute(average="weighted")
    accelerator.print(args.model_name_or_path)
    accelerator.print(f"Accuracy: {acc_metric}, Precision:{prec_metric}, Recall:{rec_metric}, F1:{f1_metric}")

if __name__ == "__main__":
    main()