constituency / Parser

import os
import subprocess

from itertools import chain

import tensorflow as tf
from tensorflow.keras import backend

from datasets import Dataset
from datasets.features import Features
from transformers import AutoTokenizer
from transformers.modeling_tf_utils import TFTokenClassificationLoss
from transformers.tf_utils import shape_list

from .constituency_parser import ConstituencyParser, category_names
from .data_utils import DataCollator
from .dataset_utils import (
    add_lemma_rules,
    add_spines_and_attachments,
    add_adjacency_matrix,
    preprocess_ne_heads,
    morfeusz_retokenize,
    bert_tokenize_and_align
)
from .constants import (
    FIRST,
    MASK_VALUE,
    TOKENS,
    SEG_BEGIN,
    SEG_INSIDE,
    SEGS,
    LEMMAS,
    LEMMA_CASES,
    LEMMA_RULES,
    TAGS,
    NONTERMINALS,
    HEADS,
    DEPRELS,
    ADJACENCY_MATRIX,
    NE_NONTERMINALS,
    NE_HEADS,
    NE_SPINES,
    NE_ANCHORS,
    NE_ANCHOR_HS,
    NE_ADJACENCY_MATRIX,
)

SPLIT = 'split'
DROP = 'drop'

class AvgAccuracy(tf.keras.callbacks.Callback):
    def __init__(self):
        super(AvgAccuracy, self).__init__()
    def on_epoch_begin(self, epoch, logs={}):
        return
    def on_epoch_end(self, epoch, logs={}):
        accs = []
        val_accs = []
        for k, v in logs.items():
            if k.endswith('_acc'):
                if k.startswith('val_'):
                    val_accs.append(v)
                else:
                    accs.append(v)
        logs['avg_acc'] = sum(accs) / len(accs)
        logs['val_avg_acc'] = sum(val_accs) / len(val_accs)

def _masked_sparse_categorical_accuracy(y_true, y_pred):
    y_pred = tf.convert_to_tensor(y_pred)
    y_true = tf.convert_to_tensor(y_true)
    y_pred_rank = y_pred.shape.ndims
    y_true_rank = y_true.shape.ndims
    # If the shape of y_true is (num_samples, 1), squeeze to (num_samples,)
    if (y_true_rank is not None) and (y_pred_rank is not None) and (len(
            backend.int_shape(y_true)) == len(backend.int_shape(y_pred))):
        y_true = tf.squeeze(y_true, [-1])
    y_pred = tf.compat.v1.argmax(y_pred, axis=-1)
    mask = tf.reshape(y_true, (-1,)) != MASK_VALUE
    y_true = tf.boolean_mask(tf.reshape(y_true, (-1,)), mask)
    y_pred = tf.boolean_mask(tf.reshape(y_pred, (-1,)), mask)
    # If the predicted output and actual output types don't match, force cast them
    # to match.
    if backend.dtype(y_pred) != backend.dtype(y_true):
        y_pred = tf.cast(y_pred, backend.dtype(y_true))
    ret = tf.cast(tf.equal(y_true, y_pred), backend.floatx())
    return ret
    
def _matrix_accuracy(y_true, y_pred):
    y_pred = tf.convert_to_tensor(y_pred)
    y_true = tf.convert_to_tensor(y_true)
    row_mask = tf.math.reduce_any(y_true != MASK_VALUE, axis=-1)
    masked_true = tf.cast(tf.boolean_mask(y_true, row_mask), backend.floatx())
    masked_pred = tf.boolean_mask(y_pred, row_mask)
    argmax_true = tf.compat.v1.argmax(masked_true, axis=-1)
    argmax_pred = tf.compat.v1.argmax(masked_pred, axis=-1)
    ret = tf.cast(tf.equal(argmax_true, argmax_pred), backend.floatx())
    return ret
    
def _matrix_loss(labels, logits):
    loss_fn = tf.keras.losses.CategoricalCrossentropy(
        from_logits=True, reduction=tf.keras.losses.Reduction.NONE
    )
    row_mask = tf.math.reduce_any(labels != MASK_VALUE, axis=-1)
    masked_labels = tf.cast(tf.boolean_mask(labels, row_mask), backend.floatx())
    masked_logits = tf.boolean_mask(logits, row_mask)
    # add -MASK_VALUE where labels == MASK_VALUE to replace it with zero
    masked_labels = masked_labels + tf.cast(masked_labels == MASK_VALUE, backend.floatx()) * -MASK_VALUE
    loss = loss_fn(masked_labels, masked_logits)
    return loss

class Trainer(object):
    
    def __init__(
            self,
            bert_path,
            dataset,
            segmentation=True,
            lemmatisation=True,
            tagging=True,
            dependency=True,
            spines=True,
            nonterminal_features=[],
            named_entities=False,
            #masking_strategy=FIRST,
            #TODO?: long sequence handling
            #long_sequences=SPLIT,
            #max_seq_len=80,
            batch_size=32,
            ):
        #if long_sequences not in (SPLIT, DROP):
        #    raise ValueError(f'<long_sequences> must be one of: {SPLIT}, {DROP}')
        self.bert_path = bert_path
        self.dataset = dataset
        self.segmentation = segmentation
        self.lemmatisation = lemmatisation
        self.tagging = tagging
        self.dependency = dependency
        self.spines = spines
        self.nonterminal_features = nonterminal_features
        self.named_entities = named_entities
        self.masking_strategy = FIRST
        #TODO?
        #self._handle_long_sequences(long_sequences, max_seq_len)
        self.batch_size = batch_size
        
        self.categories = category_names(self.segmentation, self.lemmatisation, self.tagging, self.dependency, self.spines, self.nonterminal_features, self.named_entities)
        
        if self.lemmatisation:
            if LEMMAS not in self.dataset['train'].features:
                raise RuntimeError(f'Can’t train lemmatisation without "{LEMMAS}" column in the dataset!')
            print('Preprocessing the dataset for lemmatisation...')
            self.dataset = add_lemma_rules(self.dataset)
        
        if self.tagging:
            if TAGS not in self.dataset['train'].features:
                raise RuntimeError(f'Can’t train tagging without "{TAGS}" column in the dataset!')
        
        if self.spines:
            if NONTERMINALS not in self.dataset['train'].features:
                raise RuntimeError(f'Can’t train spines without "{NONTERMINALS}" column in the dataset!')
            print('Preprocessing the dataset for spines and attachments...')
            self.dataset = add_spines_and_attachments(self.dataset)
        
        if self.dependency:
            if HEADS not in self.dataset['train'].features or DEPRELS not in self.dataset['train'].features:
                raise RuntimeError(f'Can’t train dependency without "{HEADS}" and "{DEPRELS}" columns in the dataset!')
            print('Preprocessing the dataset for dependency...')
            self.dataset = add_adjacency_matrix(self.dataset)
        
        if self.nonterminal_features:
            if not self.spines or not self.dependency:
                raise RuntimeError(f'Can’t train nonterminal features without training spines and dependency!')
            for ntf in self.nonterminal_features:
                if ntf not in self.dataset['train'].features:
                    raise RuntimeError(f'Can’t train "{ntf}" feature without "{ntf}" column in the dataset!')
        
        if self.named_entities:
            if NE_HEADS not in self.dataset['train'].features or NE_NONTERMINALS not in self.dataset['train'].features:
                raise RuntimeError(f'Can’t train NER without "{NE_HEADS}" and "{NE_NONTERMINALS}" columns in the dataset!')
            print('Preprocessing the dataset for NER...')
            self.dataset = add_spines_and_attachments(self.dataset, NER=True)
            self.dataset = preprocess_ne_heads(self.dataset)
            self.dataset = add_adjacency_matrix(self.dataset, NER=True)
        
        cols_to_remove = [c for c in self.dataset['train'].column_names if c not in self.categories + [TOKENS, LEMMAS, 'sent_id']]
        print('Removing columns:', ', '.join(cols_to_remove))
        self.dataset = self.dataset.remove_columns(cols_to_remove)
        
        if self.segmentation:
            print('Preprocessing the dataset for segmentation...')
            self.dataset = morfeusz_retokenize(self.dataset)
        
        self.features = Features({cat : self.dataset['train'].features[cat] for cat in self.categories})
        
        print('Loading BERT tokenizer...')
        self.bert_tokenizer = AutoTokenizer.from_pretrained(self.bert_path)
        
        print('Preprocessing the dataset for BERT...')
        self.dataset = self.dataset.map(lambda x: bert_tokenize_and_align(x, self.bert_tokenizer, self.masking_strategy))
        
        self.train_data = self._prepare_tf_data(self.dataset['train'], shuffle=True)
        self.dev_data = self._prepare_tf_data(self.dataset['validation'])
        
    def _prepare_tf_data(self, dataset, shuffle=False):
        collator = DataCollator(self.bert_tokenizer, self.features)
        return Dataset.to_tf_dataset(
            dataset,
            columns=['input_ids', 'token_type_ids', 'attention_mask'],
            label_cols=self.categories,
            batch_size=self.batch_size, shuffle=shuffle, collate_fn=collator
        )
    
    '''
    def _do_handle_long_sequences(self, tokens, tags, long_sequences, max_seq_len):
        if long_sequences == SPLIT:
            # TODO? add overlap
            #cut = (max_seq_len - overlap) if overlap else max_seq_len
            cut = max_seq_len
            new_tokens, new_tags = [], []
            for x, y in zip(tokens, tags):
                while x:
                    assert(len(x) == len(y))
                    new_tokens.append(x[:max_seq_len])
                    new_tags.append(y[:max_seq_len])
                    x, y = x[cut:], y[cut:]
            return new_tokens, new_tags
        elif long_sequences == DROP:
            return [x for x in tokens if len(x) <= max_seq_len], [y for y in tags if len(y) <= max_seq_len]
    
    def _handle_long_sequences(self, long_sequences, max_seq_len):
        print(f'\nhandling sequences longer than {max_seq_len} ({long_sequences})...')
        self.train_tokens, self.train_tags = self._do_handle_long_sequences(
            self.train_tokens, self.train_tags, long_sequences, max_seq_len)
        print(f'train sentences: {len(self.train_tokens)}')
    '''
    
    def _prepare_output_dir(self, path):
        if not os.path.exists(path):
            subprocess.call(f'mkdir {path}', shell=True)
        else:
            if not os.path.isdir(path):
                raise ValueError(f'{path} is not a directory')
            elif os.listdir(path):
                print(f'emptying {path}')
                subprocess.call(f'rm -r {path}/*', shell=True)
    
    
    def _make_callbacks(self, log_dir):
        callbacks = [
            AvgAccuracy(),
            tf.keras.callbacks.EarlyStopping(monitor='val_avg_acc', patience=4, verbose=1, restore_best_weights=True)
        ]
        if log_dir is not None:
            callbacks.append(tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=0, update_freq=50))
        return callbacks
    
    def train(self, epochs=10, lr=0.00001, log_dir=None, model_dir=None, tree_weight=1):
        
        if log_dir is not None:
            self._prepare_output_dir(log_dir)
        if model_dir is not None:
            self._prepare_output_dir(model_dir)
        
        parser = ConstituencyParser.create(
            self.bert_path,
            self.features,
            segmentation=self.segmentation,
            lemmatisation=self.lemmatisation,
            tagging=self.tagging,
            dependency=self.dependency,
            spines=self.spines,
            nonterminal_features=self.nonterminal_features,
            named_entities=self.named_entities,
            bert_tokenizer=self.bert_tokenizer
        )
        
        parser.model.config.tf_legacy_loss = True
        hf_loss = parser.model.hf_compute_loss
        
        def _loss(labels, logits):
            print('LABELS:', labels)
            print('LOGITS:', logits)
            l = hf_loss(labels, logits)
            print('LOSS:', l)
            return l
        
        # wrap in eager execution so that tensor values can be printed
        def debug_loss(y_true, y_pred):
            return tf.py_function(func=_loss, inp=[tf.cast(y_true, tf.float32), y_pred], Tout=tf.float32)
        
        loss = hf_loss
        #loss = debug_loss
        matrix_loss = _matrix_loss
        
        accuracy_metric = lambda: tf.keras.metrics.MeanMetricWrapper(
            fn=_masked_sparse_categorical_accuracy, name='acc'
        )
        
        matrix_accuracy_metric = lambda: tf.keras.metrics.MeanMetricWrapper(
            fn=_matrix_accuracy, name='acc'
        )
        
        matrix_cats = (ADJACENCY_MATRIX, NE_ADJACENCY_MATRIX)
        
        metrics = {cat : [accuracy_metric() if cat not in matrix_cats else matrix_accuracy_metric()] for cat in self.categories}
        
        losses = {cat : loss if cat not in matrix_cats else matrix_loss for cat in self.categories}
        
        loss_weights = None
        if tree_weight != 1:
            loss_weights = {
                cat : (1 if cat in (SEGS, LEMMA_CASES, LEMMA_RULES, TAGS) else tree_weight)
                for cat in self.categories
            }
        
        initial_epoch = 0
        
        parser.model.compile(
            optimizer=tf.keras.optimizers.Adam(learning_rate=lr),
            loss=losses,
            loss_weights=loss_weights,
            metrics=metrics
        )
        parser.model.fit(
            x=self.train_data,
            validation_data=self.dev_data,
            epochs=epochs,
            callbacks=self._make_callbacks(log_dir),
            initial_epoch=initial_epoch,
        )
        
        if model_dir is not None:
            parser.save(model_dir)
            with open(f'{model_dir}/trainer.info', 'w') as f:
                print(f'dataset: {self.dataset}', file=f)
                print(f'tree_weight: {tree_weight}', file=f)
                print(f'batch_size: {self.batch_size}', file=f)
                print(f'epochs: {epochs}', file=f)
                print(f'lr: {lr}', file=f)
        
        return parser