utils_ner.py

import logging
import os

import numpy as np
from seqeval.metrics import f1_score, precision_score, recall_score, classification_report
from torch.utils.data import DataLoader, SequentialSampler, TensorDataset
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm
import torch.nn as nn
from lit_nlp.api import dataset as lit_dataset
from lit_nlp.api import types as lit_types

logger = logging.getLogger(__name__)
import torch


class InputExample(object):
    """A single training/test example for token classification."""

    def __init__(self, guid, words, labels):
        """Constructs a InputExample.

        Args:
            guid: Unique id for the example.
            words: list. The words of the sequence.
            labels: (Optional) list. The labels for each word of the sequence. This should be
            specified for train and dev examples, but not for test examples.
        """
        self.guid = guid
        self.words = words
        self.labels = labels


class InputFeatures(object):
    """A single set of features of data."""

    def __init__(self, input_ids, input_mask, segment_ids, label_ids):
        self.input_ids = input_ids
        self.input_mask = input_mask
        self.segment_ids = segment_ids
        self.label_ids = label_ids


def get_labels(path):
    if path:
        with open(path, "r") as f:
            labels = f.read().splitlines()
        if "O" not in labels:
            labels = ["O"] + labels
        return labels
    else:
        return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]


def read_examples_from_file(data_dir, mode):
    file_path = os.path.join(data_dir, "{}.txt".format(mode))
    guid_index = 1
    examples = []
    with open(file_path, encoding="utf-8") as f:
        words = []
        labels = []
        for line in f:
            if line.startswith("-DOCSTART-") or line == "" or line == "\n":
                if words:
                    examples.append(InputExample(guid="{}-{}".format(mode, guid_index), words=words, labels=labels))
                    guid_index += 1
                    words = []
                    labels = []
            else:
                splits = line.split(" ")
                words.append(splits[0])
                if len(splits) > 1:
                    labels.append(splits[-1].replace("\n", ""))
                else:
                    # Examples could have no label for mode = "test"
                    labels.append("O")
        if words:
            examples.append(InputExample(guid="{}-{}".format(mode, guid_index), words=words, labels=labels))
    return examples


def convert_examples_to_features(
        examples,
        label_list,
        max_seq_length,
        tokenizer,
        cls_token_at_end=False,
        cls_token="[CLS]",
        cls_token_segment_id=1,
        sep_token="[SEP]",
        sep_token_extra=False,
        pad_on_left=False,
        pad_token=0,
        pad_token_segment_id=0,
        pad_token_label_id=-100,
        sequence_a_segment_id=0,
        mask_padding_with_zero=True,
):
    """ Loads a data file into a list of `InputBatch`s
        `cls_token_at_end` define the location of the CLS token:
            - False (Default, BERT/XLM pattern): [CLS] + A + [SEP] + B + [SEP]
            - True (XLNet/GPT pattern): A + [SEP] + B + [SEP] + [CLS]
        `cls_token_segment_id` define the segment id associated to the CLS token (0 for BERT, 2 for XLNet)
    """

    label_map = {label: i for i, label in enumerate(label_list)}

    features = []
    all_tokens = []
    for (ex_index, example) in enumerate(examples):
        if ex_index % 10000 == 0:
            logger.info("Writing example %d of %d", ex_index, len(examples))

        tokens = []
        label_ids = []
        for word, label in zip(example.words, example.labels):
            word_tokens = tokenizer.tokenize(word)

            # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
            if len(word_tokens) > 0:
                tokens.extend(word_tokens)
                # Use the real label id for the first token of the word, and padding ids for the remaining tokens
                label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(word_tokens) - 1))

        # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
        special_tokens_count = tokenizer.num_special_tokens_to_add()
        if len(tokens) > max_seq_length - special_tokens_count:
            tokens = tokens[: (max_seq_length - special_tokens_count)]
            label_ids = label_ids[: (max_seq_length - special_tokens_count)]

        # The convention in BERT is:
        # (a) For sequence pairs:
        #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
        #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
        # (b) For single sequences:
        #  tokens:   [CLS] the dog is hairy . [SEP]
        #  type_ids:   0   0   0   0  0     0   0
        #
        # Where "type_ids" are used to indicate whether this is the first
        # sequence or the second sequence. The embedding vectors for `type=0` and
        # `type=1` were learned during pre-training and are added to the wordpiece
        # embedding vector (and position vector). This is not *strictly* necessary
        # since the [SEP] token unambiguously separates the sequences, but it makes
        # it easier for the model to learn the concept of sequences.
        #
        # For classification tasks, the first vector (corresponding to [CLS]) is
        # used as as the "sentence vector". Note that this only makes sense because
        # the entire model is fine-tuned.
        tokens += [sep_token]
        label_ids += [pad_token_label_id]
        if sep_token_extra:
            # roberta uses an extra separator b/w pairs of sentences
            tokens += [sep_token]
            label_ids += [pad_token_label_id]
        segment_ids = [sequence_a_segment_id] * len(tokens)

        if cls_token_at_end:
            tokens += [cls_token]
            label_ids += [pad_token_label_id]
            segment_ids += [cls_token_segment_id]
        else:
            tokens = [cls_token] + tokens
            label_ids = [pad_token_label_id] + label_ids
            segment_ids = [cls_token_segment_id] + segment_ids

        all_tokens.append(tokens)
        input_ids = tokenizer.convert_tokens_to_ids(tokens)

        # The mask has 1 for real tokens and 0 for padding tokens. Only real
        # tokens are attended to.
        input_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)

        # Zero-pad up to the sequence length.
        padding_length = max_seq_length - len(input_ids)
        if pad_on_left:
            input_ids = ([pad_token] * padding_length) + input_ids
            input_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
            segment_ids = ([pad_token_segment_id] * padding_length) + segment_ids
            label_ids = ([pad_token_label_id] * padding_length) + label_ids
        else:
            input_ids += [pad_token] * padding_length
            input_mask += [0 if mask_padding_with_zero else 1] * padding_length
            segment_ids += [pad_token_segment_id] * padding_length
            label_ids += [pad_token_label_id] * padding_length

        assert len(input_ids) == max_seq_length
        assert len(input_mask) == max_seq_length
        assert len(segment_ids) == max_seq_length
        assert len(label_ids) == max_seq_length

        if ex_index < 5:
            logger.info("*** Example ***")
            logger.info("guid: %s", example.guid)
            logger.info("tokens: %s", " ".join([str(x) for x in tokens]))
            logger.info("input_ids: %s", " ".join([str(x) for x in input_ids]))
            logger.info("input_mask: %s", " ".join([str(x) for x in input_mask]))
            logger.info("segment_ids: %s", " ".join([str(x) for x in segment_ids]))
            logger.info("label_ids: %s", " ".join([str(x) for x in label_ids]))

        features.append(
            InputFeatures(input_ids=input_ids, input_mask=input_mask, segment_ids=segment_ids, label_ids=label_ids)
        )
    return features, all_tokens


def load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, mode):
    max_seq_length = 512 if mode == "test" else args.max_seq_length
    if args.local_rank not in [-1, 0] and not evaluate:
        torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache

    # Load data features from cache or dataset file
    cached_features_file = os.path.join(
        args.data_dir,
        "cached_{}_{}_{}".format(
            mode, list(filter(None, args.model_name_or_path.split("/"))).pop(), str(args.max_seq_length)
        ),
    )
    if os.path.exists(cached_features_file) and not args.overwrite_cache:
        logger.info("Loading features from cached file %s", cached_features_file)
        features = torch.load(cached_features_file)
    else:
        logger.info("Creating features from dataset file at %s", args.data_dir)
        examples = read_examples_from_file(args.data_dir, mode)
        features = convert_examples_to_features(
            examples,
            labels,
            max_seq_length,
            tokenizer,
            cls_token_at_end=bool(args.model_type in ["xlnet"]),
            # xlnet has a cls token at the end
            cls_token=tokenizer.cls_token,
            cls_token_segment_id=2 if args.model_type in ["xlnet"] else 0,
            sep_token=tokenizer.sep_token,
            sep_token_extra=bool(args.model_type in ["roberta"]),
            # roberta uses an extra separator b/w pairs of sentences, cf. github.com/pytorch/fairseq/commit/1684e166e3da03f5b600dbb7855cb98ddfcd0805
            pad_on_left=bool(args.model_type in ["xlnet"]),
            # pad on the left for xlnet
            pad_token=tokenizer.pad_token_id,
            pad_token_segment_id=tokenizer.pad_token_type_id,
            pad_token_label_id=pad_token_label_id,
        )
        if args.local_rank in [-1, 0]:
            logger.info("Saving features into cached file %s", cached_features_file)
            torch.save(features, cached_features_file)

    if args.local_rank == 0 and not evaluate:
        torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache

    # Convert to Tensors and build dataset
    all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
    all_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long)
    all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long)
    all_label_ids = torch.tensor([f.label_ids for f in features], dtype=torch.long)

    dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)
    return dataset


def evaluate(args, model, tokenizer, labels, pad_token_label_id, mode, prefix=""):
    eval_dataset = load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, mode=mode)

    args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
    # Note that DistributedSampler samples randomly
    eval_sampler = SequentialSampler(eval_dataset) if args.local_rank == -1 else DistributedSampler(eval_dataset)
    eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)

    # multi-gpu evaluate
    if args.n_gpu > 1:
        model = torch.nn.DataParallel(model)

    # Eval!
    logger.info("***** Running evaluation %s *****", prefix)
    logger.info("  Num examples = %d", len(eval_dataset))
    logger.info("  Batch size = %d", args.eval_batch_size)
    eval_loss = 0.0
    nb_eval_steps = 0
    preds = None
    out_label_ids = None
    model.eval()
    for batch in tqdm(eval_dataloader, desc="Evaluating"):
        batch = tuple(t.to(args.device) for t in batch)

        input_ids = batch[0]
        attention_mask = batch[1]
        segment_ids = batch[2]
        batch_labels = batch[3]

        with torch.no_grad():
            inputs = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
            if args.model_type != "distilbert":
                inputs["token_type_ids"] = (
                    batch[2] if args.model_type in ["bert", "xlnet"] else None
                )  # XLM and RoBERTa don"t use segment_ids
            outputs = model(**inputs)
            tmp_eval_loss, logits = outputs[:2]

            if args.n_gpu > 1:
                tmp_eval_loss = tmp_eval_loss.mean()  # mean() to average on multi-gpu parallel evaluating

            eval_loss += tmp_eval_loss.item()
        nb_eval_steps += 1
        if preds is None:
            preds = logits.detach().cpu().numpy()
            out_label_ids = inputs["labels"].detach().cpu().numpy()
        else:
            preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)
            out_label_ids = np.append(out_label_ids, inputs["labels"].detach().cpu().numpy(), axis=0)

    eval_loss = eval_loss / nb_eval_steps
    preds = np.argmax(preds, axis=2)

    label_map = {i: label for i, label in enumerate(labels)}

    out_label_list = [[] for _ in range(out_label_ids.shape[0])]
    preds_list = [[] for _ in range(out_label_ids.shape[0])]

    for i in range(out_label_ids.shape[0]):
        for j in range(out_label_ids.shape[1]):
            if out_label_ids[i, j] != pad_token_label_id:
                out_label_list[i].append(label_map[out_label_ids[i][j]])
                preds_list[i].append(label_map[preds[i][j]])

    results = {
        "loss": eval_loss,
        "precision": precision_score(out_label_list, preds_list),
        "recall": recall_score(out_label_list, preds_list),
        "f1": f1_score(out_label_list, preds_list),
    }

    logger.info("***** Eval results %s *****", prefix)
    for key in sorted(results.keys()):
        logger.info("  %s = %s", key, str(results[key]))

    if mode == "test":
        print(classification_report(out_label_list, preds_list))

    return results, preds_list


def predict(input_ids, model, labels):
    output = model(input_ids=input_ids, labels=labels)
    _, logits = output[:2]
    logits.requires_grad_()
    return logits


def predict_with_embeddings(inputs, model, labels):
    output = model(inputs_embeds=inputs, labels=labels)
    _, logits = output[:2]
    logits.requires_grad_()
    return logits


class NerModel(nn.Module):

    def __init__(self, model):
        super().__init__()
        self.model = model

    def forward(self, inputs, labels):
        output = self.model(inputs_embeds=inputs, labels=labels)
        _, logits = output[:2]
        logits.requires_grad_()
        return logits


class I2b2Dataset(lit_dataset.Dataset):

    def __init__(self, split="test", data_dir='data'):
        """Dataset constructor, loads the data into memory."""
        raw_examples = read_examples_from_file(data_dir, split)
        self._examples = []  # populate this with data records
        for record in raw_examples:
            self._examples.append({
                "Sentence": ' '.join(record.words),
                "Token Index to Explain": -1
            })

    def spec(self) -> lit_types.Spec:
        """Dataset spec, which should match the model"s input_spec()."""
        return {
            "Sentence": lit_types.TextSegment(),
            "Token Index to Explain": lit_types.Scalar()
        }