BoghdadyJR/codesearch_python · Data Studio

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def standard_lstm_lm_200(dataset_name=None, vocab=None, pretrained=False, ctx=cpu(), root=os.path.join(get_home_dir(), 'models'), **kwargs): r"""Standard 2-layer LSTM language model with tied embedding and output weights. Both embedding and hidden dimensions are 200. Parameters ...
def big_rnn_lm_2048_512(dataset_name=None, vocab=None, pretrained=False, ctx=cpu(), root=os.path.join(get_home_dir(), 'models'), **kwargs): r"""Big 1-layer LSTMP language model. Both embedding and projection size are 512. Hidden size is 2048. Parameters ---------- dataset_n...
def forward(self, inputs, begin_state=None): # pylint: disable=arguments-differ """Implement forward computation. Parameters ----------- inputs : NDArray input tensor with shape `(sequence_length, batch_size)` when `layout` is "TNC". begin_state : list ...
def forward(self, inputs, begin_state): # pylint: disable=arguments-differ """Implement forward computation. Parameters ----------- inputs : NDArray input tensor with shape `(sequence_length, batch_size)` when `layout` is "TNC". begin_state : list ...
def _get_cell_type(cell_type): """Get the object type of the cell by parsing the input Parameters ---------- cell_type : str or type Returns ------- cell_constructor: type The constructor of the RNNCell """ if isinstance(cell_type, str): if cell_type == 'lstm': ...
def _get_context(center_idx, sentence_boundaries, window_size, random_window_size, seed): """Compute the context with respect to a center word in a sentence. Takes an numpy array of sentences boundaries. """ random.seed(seed + center_idx) sentence_index = np.searchsorted(sentence...
def model(dropout, vocab, model_mode, output_size): """Construct the model.""" textCNN = SentimentNet(dropout=dropout, vocab_size=len(vocab), model_mode=model_mode,\ output_size=output_size) textCNN.hybridize() return textCNN
def init(textCNN, vocab, model_mode, context, lr): """Initialize parameters.""" textCNN.initialize(mx.init.Xavier(), ctx=context, force_reinit=True) if model_mode != 'rand': textCNN.embedding.weight.set_data(vocab.embedding.idx_to_vec) if model_mode == 'multichannel': textCNN.embedding_...
def preprocess_dataset(dataset, transform, num_workers=8): """Use multiprocessing to perform transform for dataset. Parameters ---------- dataset: dataset-like object Source dataset. transform: callable Transformer function. num_workers: int, default 8 The number of mult...
def get_model(name, dataset_name='wikitext-2', **kwargs): """Returns a pre-defined model by name. Parameters ---------- name : str Name of the model. dataset_name : str or None, default 'wikitext-2'. The dataset name on which the pre-trained model is trained. For language mo...
def _masked_softmax(F, att_score, mask, dtype): """Ignore the masked elements when calculating the softmax Parameters ---------- F : symbol or ndarray att_score : Symborl or NDArray Shape (batch_size, query_length, memory_length) mask : Symbol or NDArray or None Shape (batch_siz...
def _read_by_weight(self, F, att_weights, value): """Read from the value matrix given the attention weights. Parameters ---------- F : symbol or ndarray att_weights : Symbol or NDArray Attention weights. For single-head attention, Shape (b...
def translate(self, src_seq, src_valid_length): """Get the translation result given the input sentence. Parameters ---------- src_seq : mx.nd.NDArray Shape (batch_size, length) src_valid_length : mx.nd.NDArray Shape (batch_size,) Returns ...
def evaluate_official_script(parser, vocab, num_buckets_test, test_batch_size, test_file, output_file, debug=False): """Evaluate parser on a data set Parameters ---------- parser : BiaffineParser biaffine parser vocab : ParserVocabulary vocabulary built ...
def parameter_from_numpy(self, name, array): """ Create parameter with its value initialized according to a numpy tensor Parameters ---------- name : str parameter name array : np.ndarray initiation value Returns ------- mxnet.glu...
def parameter_init(self, name, shape, init): """Create parameter given name, shape and initiator Parameters ---------- name : str parameter name shape : tuple parameter shape init : mxnet.initializer an initializer Returns ...
def forward(self, word_inputs, tag_inputs, arc_targets=None, rel_targets=None): """Run decoding Parameters ---------- word_inputs : mxnet.ndarray.NDArray word indices of seq_len x batch_size tag_inputs : mxnet.ndarray.NDArray tag indices of seq_len x batc...
def save_parameters(self, filename): """Save model Parameters ---------- filename : str path to model file """ params = self._collect_params_with_prefix() if self.pret_word_embs: # don't save word embeddings inside model params.pop('pret_...
def _worker_fn(samples, batchify_fn, dataset=None): """Function for processing data in worker process.""" # pylint: disable=unused-argument # it is required that each worker process has to fork a new MXIndexedRecordIO handle # preserving dataset as global variable can save tons of overhead and is safe i...
def _thread_worker_fn(samples, batchify_fn, dataset): """Threadpool worker function for processing data.""" if isinstance(samples[0], (list, tuple)): batch = [batchify_fn([dataset[i] for i in shard]) for shard in samples] else: batch = batchify_fn([dataset[i] for i in samples]) return ba...
def create(embedding_name, **kwargs): """Creates an instance of token embedding. Creates a token embedding instance by loading embedding vectors from an externally hosted pre-trained token embedding file, such as those of GloVe and FastText. To get all the valid `embedding_name` and `source`, use :fun...
def list_sources(embedding_name=None): """Get valid token embedding names and their pre-trained file names. To load token embedding vectors from an externally hosted pre-trained token embedding file, such as those of GloVe and FastText, one should use `gluonnlp.embedding.create(embedding_name, source)...
def _load_embedding(self, pretrained_file_path, elem_delim, encoding='utf8'): """Load embedding vectors from a pre-trained token embedding file. Both text files and TokenEmbedding serialization files are supported. elem_delim and encoding are ignored for non-text files. ...
def _load_embedding_txt(self, pretrained_file_path, elem_delim, encoding='utf8'): """Load embedding vectors from a pre-trained token embedding file. For every unknown token, if its representation `self.unknown_token` is encountered in the pre-trained token embedding file, index 0 of `self.idx_t...
def _load_embedding_serialized(self, pretrained_file_path): """Load embedding vectors from a pre-trained token embedding file. For every unknown token, if its representation `self.unknown_token` is encountered in the pre-trained token embedding file, index 0 of `self.idx_to_vec` maps to the pre...
def _check_vector_update(self, tokens, new_embedding): """Check that tokens and embedding are in the format for __setitem__.""" assert self._idx_to_vec is not None, '`idx_to_vec` has not been initialized.' if not isinstance(tokens, (list, tuple)) or len(tokens) == 1: assert isinstan...
def _check_source(cls, source_file_hash, source): """Checks if a pre-trained token embedding source name is valid. Parameters ---------- source : str The pre-trained token embedding source. """ embedding_name = cls.__name__.lower() if source not in s...
def from_file(file_path, elem_delim=' ', encoding='utf8', **kwargs): """Creates a user-defined token embedding from a pre-trained embedding file. This is to load embedding vectors from a user-defined pre-trained token embedding file. For example, if `elem_delim` = ' ', the expected format of a...
def serialize(self, file_path, compress=True): """Serializes the TokenEmbedding to a file specified by file_path. TokenEmbedding is serialized by converting the list of tokens, the array of word embeddings and other metadata to numpy arrays, saving all in a single (optionally compressed...
def deserialize(cls, file_path, **kwargs): """Create a new TokenEmbedding from a serialized one. TokenEmbedding is serialized by converting the list of tokens, the array of word embeddings and other metadata to numpy arrays, saving all in a single (optionally compressed) Zipfile. See ...
def evaluate(data_source): """Evaluate the model on a mini-batch. """ log.info('Start predict') tic = time.time() for batch in data_source: inputs, token_types, valid_length = batch out = net(inputs.astype('float32').as_in_context(ctx), token_types.astype('float32')...
def register(class_=None, **kwargs): """Registers a dataset with segment specific hyperparameters. When passing keyword arguments to `register`, they are checked to be valid keyword arguments for the registered Dataset class constructor and are saved in the registry. Registered keyword arguments can be...
def create(name, **kwargs): """Creates an instance of a registered dataset. Parameters ---------- name : str The dataset name (case-insensitive). Returns ------- An instance of :class:`mxnet.gluon.data.Dataset` constructed with the keyword arguments passed to the create functio...
def list_datasets(name=None): """Get valid datasets and registered parameters. Parameters ---------- name : str or None, default None Return names and registered parameters of registered datasets. If name is specified, only registered parameters of the respective dataset are ret...
def parse_args(): """Parse command line arguments.""" parser = argparse.ArgumentParser( description='Vocabulary extractor.', formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument('--max-size', type=int, default=None) parser.add_argument('--min-freq', type=int, defau...
def get_vocab(args): """Compute the vocabulary.""" counter = nlp.data.Counter() start = time.time() for filename in args.files: print('Starting processing of {} after {:.1f} seconds.'.format( filename, time.time() - start)) with open(filename, 'r') as f: ...
def forward(self, inputs, token_types, valid_length=None): # pylint: disable=arguments-differ """Generate the unnormalized score for the given the input sequences. Parameters ---------- inputs : NDArray, shape (batch_size, seq_length) Input words for the sequences. ...
def add_parameters(parser): """Add evaluation specific parameters to parser.""" group = parser.add_argument_group('Evaluation arguments') group.add_argument('--eval-batch-size', type=int, default=1024) # Datasets group.add_argument( '--similarity-datasets', type=str, default=nlp.da...
def validate_args(args): """Validate provided arguments and act on --help.""" # Check correctness of similarity dataset names for dataset_name in args.similarity_datasets: if dataset_name.lower() not in map( str.lower, nlp.data.word_embedding_evaluation.word_similarit...
def iterate_similarity_datasets(args): """Generator over all similarity evaluation datasets. Iterates over dataset names, keyword arguments for their creation and the created dataset. """ for dataset_name in args.similarity_datasets: parameters = nlp.data.list_datasets(dataset_name) ...
def iterate_analogy_datasets(args): """Generator over all analogy evaluation datasets. Iterates over dataset names, keyword arguments for their creation and the created dataset. """ for dataset_name in args.analogy_datasets: parameters = nlp.data.list_datasets(dataset_name) for key...
def get_similarity_task_tokens(args): """Returns a set of all tokens occurring the evaluation datasets.""" tokens = set() for _, _, dataset in iterate_similarity_datasets(args): tokens.update( itertools.chain.from_iterable((d[0], d[1]) for d in dataset)) return tokens
def get_analogy_task_tokens(args): """Returns a set of all tokens occuring the evaluation datasets.""" tokens = set() for _, _, dataset in iterate_analogy_datasets(args): tokens.update( itertools.chain.from_iterable( (d[0], d[1], d[2], d[3]) for d in dataset)) return ...
def evaluate_similarity(args, token_embedding, ctx, logfile=None, global_step=0): """Evaluate on specified similarity datasets.""" results = [] for similarity_function in args.similarity_functions: evaluator = nlp.embedding.evaluation.WordEmbeddingSimilarity( idx...
def evaluate_analogy(args, token_embedding, ctx, logfile=None, global_step=0): """Evaluate on specified analogy datasets. The analogy task is an open vocabulary task, make sure to pass a token_embedding with a sufficiently large number of supported tokens. """ results = [] exclude_question_wor...
def log_similarity_result(logfile, result): """Log a similarity evaluation result dictionary as TSV to logfile.""" assert result['task'] == 'similarity' if not logfile: return with open(logfile, 'a') as f: f.write('\t'.join([ str(result['global_step']), result['...
def get_model_loss(ctx, model, pretrained, dataset_name, dtype, ckpt_dir=None, start_step=None): """Get model for pre-training.""" # model model, vocabulary = nlp.model.get_model(model, dataset_name=dataset_name, pretrai...
def get_pretrain_dataset(data, batch_size, num_ctxes, shuffle, use_avg_len, num_buckets, num_parts=1, part_idx=0, prefetch=True): """create dataset for pretraining.""" num_files = len(glob.glob(os.path.expanduser(data))) logging.debug('%d files found.', num_files) assert num_fil...
def get_dummy_dataloader(dataloader, target_shape): """Return a dummy data loader which returns a fixed data batch of target shape""" data_iter = enumerate(dataloader) _, data_batch = next(data_iter) logging.debug('Searching target batch shape: %s', target_shape) while data_batch[0].shape != target_...
def save_params(step_num, model, trainer, ckpt_dir): """Save the model parameter, marked by step_num.""" param_path = os.path.join(ckpt_dir, '%07d.params'%step_num) trainer_path = os.path.join(ckpt_dir, '%07d.states'%step_num) logging.info('[step %d] Saving checkpoints to %s, %s.', step...
def log(begin_time, running_num_tks, running_mlm_loss, running_nsp_loss, step_num, mlm_metric, nsp_metric, trainer, log_interval): """Log training progress.""" end_time = time.time() duration = end_time - begin_time throughput = running_num_tks / duration / 1000.0 running_mlm_loss = running_...
def split_and_load(arrs, ctx): """split and load arrays to a list of contexts""" assert isinstance(arrs, (list, tuple)) # split and load loaded_arrs = [mx.gluon.utils.split_and_load(arr, ctx, even_split=False) for arr in arrs] return zip(*loaded_arrs)
def forward(data, model, mlm_loss, nsp_loss, vocab_size, dtype): """forward computation for evaluation""" (input_id, masked_id, masked_position, masked_weight, \ next_sentence_label, segment_id, valid_length) = data num_masks = masked_weight.sum() + 1e-8 valid_length = valid_length.reshape(-1) ...
def evaluate(data_eval, model, nsp_loss, mlm_loss, vocab_size, ctx, log_interval, dtype): """Evaluation function.""" mlm_metric = MaskedAccuracy() nsp_metric = MaskedAccuracy() mlm_metric.reset() nsp_metric.reset() eval_begin_time = time.time() begin_time = time.time() step_num = 0 ...
def get_argparser(): """Argument parser""" parser = argparse.ArgumentParser(description='BERT pretraining example.') parser.add_argument('--num_steps', type=int, default=20, help='Number of optimization steps') parser.add_argument('--num_buckets', type=int, default=1, help='Numbe...
def _cache_dataset(dataset, prefix): """Cache the processed npy dataset the dataset into a npz Parameters ---------- dataset : SimpleDataset file_path : str """ if not os.path.exists(_constants.CACHE_PATH): os.makedirs(_constants.CACHE_PATH) src_data = np.concatenate([e[0] for e...
def load_translation_data(dataset, bleu, args): """Load translation dataset Parameters ---------- dataset : str args : argparse result Returns ------- """ src_lang, tgt_lang = args.src_lang, args.tgt_lang if dataset == 'IWSLT2015': common_prefix = 'IWSLT2015_{}_{}_{}_{...
def make_dataloader(data_train, data_val, data_test, args, use_average_length=False, num_shards=0, num_workers=8): """Create data loaders for training/validation/test.""" data_train_lengths = get_data_lengths(data_train) data_val_lengths = get_data_lengths(data_val) data_test_lengths...
def run(self): """Method representing the process’s activity.""" random.seed(self.seed) np.random.seed(self.np_seed) if not isinstance(self, multiprocessing.Process): # Calling mxnet methods in a subprocess will raise an exception if # mxnet is built with GPU supp...
def forward(self, inputs, states=None): # pylint: disable=arguments-differ """Defines the forward computation. Arguments can be either :py:class:`NDArray` or :py:class:`Symbol`.""" batch_size = inputs.shape[self._batch_axis] skip_states = states is None if skip_states: ...
def hybrid_forward(self, F, words, weight): """Compute embedding of words in batch. Parameters ---------- words : mx.nd.NDArray Array of token indices. """ #pylint: disable=arguments-differ embeddings = F.sparse.dot(words, weight) return embe...
def load_fasttext_format(cls, path, ctx=cpu(), **kwargs): """Create an instance of the class and load weights. Load the weights from the fastText binary format created by https://github.com/facebookresearch/fastText Parameters ---------- path : str Path to t...
def logging_config(logpath=None, level=logging.DEBUG, console_level=logging.INFO, no_console=False): """ Config the logging. """ logger = logging.getLogger('nli') # Remove all the current handlers for handler in logger.handlers: lo...
def parse_args(): """Parse command line arguments.""" parser = argparse.ArgumentParser( description='GloVe with GluonNLP', formatter_class=argparse.ArgumentDefaultsHelpFormatter) # Data options group = parser.add_argument_group('Data arguments') group.add_argument( 'cooccurr...
def get_train_data(args): """Helper function to get training data.""" counter = dict() with io.open(args.vocab, 'r', encoding='utf-8') as f: for line in f: token, count = line.split('\t') counter[token] = int(count) vocab = nlp.Vocab(counter, unknown_token=None, padding_t...
def train(args): """Training helper.""" vocab, row, col, counts = get_train_data(args) model = GloVe(token_to_idx=vocab.token_to_idx, output_dim=args.emsize, dropout=args.dropout, x_max=args.x_max, alpha=args.alpha, weight_initializer=mx.init.Uniform(scale=1 / args.emsize...
def log(args, kwargs): """Log to a file.""" logfile = os.path.join(args.logdir, 'log.tsv') if 'log_created' not in globals(): if os.path.exists(logfile): logging.error('Logfile %s already exists.', logfile) sys.exit(1) global log_created log_created = sorte...
def hybrid_forward(self, F, row, col, counts): """Compute embedding of words in batch. Parameters ---------- row : mxnet.nd.NDArray or mxnet.sym.Symbol Array of token indices for source words. Shape (batch_size, ). row : mxnet.nd.NDArray or mxnet.sym.Symbol ...
def update(self, labels, preds, masks=None): # pylint: disable=arguments-differ """Updates the internal evaluation result. Parameters ---------- labels : list of `NDArray` The labels of the data with class indices as values, one per sample. preds : list of `N...
def hybrid_forward(self, F, sentence1, sentence2): """ Predict the relation of two sentences. Parameters ---------- sentence1 : NDArray Shape (batch_size, length) sentence2 : NDArray Shape (batch_size, length) Returns ------- ...
def hybrid_forward(self, F, feature_a): """ Compute intra-sentence attention given embedded words. Parameters ---------- feature_a : NDArray Shape (batch_size, length, hidden_size) Returns ------- alpha : NDArray Shape (batch_size...
def hybrid_forward(self, F, a, b): """ Forward of Decomposable Attention layer """ # a.shape = [B, L1, H] # b.shape = [B, L2, H] # extract features tilde_a = self.f(a) # shape = [B, L1, H] tilde_b = self.f(b) # shape = [B, L2, H] # attention ...
def count_tokens(tokens, to_lower=False, counter=None): r"""Counts tokens in the specified string. For token_delim='(td)' and seq_delim='(sd)', a specified string of two sequences of tokens may look like:: (td)token1(td)token2(td)token3(td)(sd)(td)token4(td)token5(td)(sd) Parameters ----...
def slice_sequence(sequence, length, pad_last=False, pad_val=C.PAD_TOKEN, overlap=0): """Slice a flat sequence of tokens into sequences tokens, with each inner sequence's length equal to the specified `length`, taking into account the requested sequence overlap. Parameters ---------- sequence :...
def _slice_pad_length(num_items, length, overlap=0): """Calculate the padding length needed for sliced samples in order not to discard data. Parameters ---------- num_items : int Number of items in dataset before collating. length : int The length of each of the samples. overlap...
def train_valid_split(dataset, valid_ratio=0.05): """Split the dataset into training and validation sets. Parameters ---------- dataset : list A list of training samples. valid_ratio : float, default 0.05 Proportion of training samples to use for validation set range: [0, 1]...
def _load_pretrained_vocab(name, root=os.path.join(get_home_dir(), 'models'), cls=None): """Load the accompanying vocabulary object for pre-trained model. Parameters ---------- name : str Name of the vocabulary, usually the name of the dataset. root : str, default '$MXNET_HOME/models' ...
def _extract_archive(file, target_dir): """Extract archive file Parameters ---------- file : str Absolute path of the archive file. target_dir : str Target directory of the archive to be uncompressed """ if file.endswith('.gz') or file.endswith('.tar') or file.endswith('.tg...
def discard(self, min_freq, unknown_token): """Discards tokens with frequency below min_frequency and represents them as `unknown_token`. Parameters ---------- min_freq: int Tokens whose frequency is under min_freq is counted as `unknown_token` in the Cou...
def train(): """Training function.""" trainer = gluon.Trainer(model.collect_params(), args.optimizer, {'learning_rate': args.lr, 'beta2': 0.98, 'epsilon': 1e-9}) train_data_loader, val_data_loader, test_data_loader \ = dataprocessor.make_dataloader(data_train, data_val, ...
def hybrid_forward(self, F, inputs, **kwargs): # pylint: disable=unused-argument r""" Forward computation for highway layer Parameters ---------- inputs: NDArray The input tensor is of shape `(..., input_size)`. Returns ---------- out...
def forward(self, inputs, token_types, valid_length=None): # pylint: disable=arguments-differ """Generate the unnormalized score for the given the input sequences. Parameters ---------- inputs : NDArray, shape (batch_size, seq_length) Input words for the sequences. ...
def hybrid_forward(self, F, inputs, states=None, mask=None): # pylint: disable=arguments-differ # pylint: disable=unused-argument """Defines the forward computation for cache cell. Arguments can be either :py:class:`NDArray` or :py:class:`Symbol`. Parameters ---------- ...
def main(args): """ Read tokens from the provided parse tree in the SNLI dataset. Illegal examples are removed. """ examples = [] with open(args.input, 'r') as fin: reader = csv.DictReader(fin, delimiter='\t') for cols in reader: s1 = read_tokens(cols['sentence1_parse...
def _recenter(self): """ one iteration of k-means """ for split_idx in range(len(self._splits)): split = self._splits[split_idx] len_idx = self._split2len_idx[split] if split == self._splits[-1]: continue right_split = self....
def _reindex(self): """ Index every sentence into a cluster """ self._len2split_idx = {} last_split = -1 for split_idx, split in enumerate(self._splits): self._len2split_idx.update( dict(list(zip(list(range(last_split + 1, split)), [split_idx] ...
def get_gnmt_encoder_decoder(cell_type='lstm', attention_cell='scaled_luong', num_layers=2, num_bi_layers=1, hidden_size=128, dropout=0.0, use_residual=False, i2h_weight_initializer=None, h2h_weight_initializer=None, i2h_bias_initial...
def init_state_from_encoder(self, encoder_outputs, encoder_valid_length=None): """Initialize the state from the encoder outputs. Parameters ---------- encoder_outputs : list encoder_valid_length : NDArray or None Returns ------- decoder_states : list ...
def decode_seq(self, inputs, states, valid_length=None): """Decode the decoder inputs. This function is only used for training. Parameters ---------- inputs : NDArray, Shape (batch_size, length, C_in) states : list of NDArrays or None Initial states. The list of init...
def transform(instance, tokenizer, max_seq_length, max_predictions_per_seq, do_pad=True): """Transform instance to inputs for MLM and NSP.""" pad = tokenizer.convert_tokens_to_ids(['[PAD]'])[0] input_ids = tokenizer.convert_tokens_to_ids(instance.tokens) input_mask = [1] * len(input_ids) segment_ids...
def write_to_files_np(features, tokenizer, max_seq_length, max_predictions_per_seq, output_files): # pylint: disable=unused-argument """Write to numpy files from `TrainingInstance`s.""" next_sentence_labels = [] valid_lengths = [] assert len(output_files) == 1, 'numpy format o...
def write_to_files_rec(instances, tokenizer, max_seq_length, max_predictions_per_seq, output_files): """Create IndexedRecordIO files from `TrainingInstance`s.""" writers = [] for output_file in output_files: writers.append( mx.recordio.MXIndexedRecordIO( ...
def create_training_instances(x): """Create `TrainingInstance`s from raw text.""" (input_files, out, tokenizer, max_seq_length, dupe_factor, short_seq_prob, masked_lm_prob, max_predictions_per_seq, rng) = x time_start = time.time() logging.info('Processing %s', input_files) all_documents = [[]]...
def create_instances_from_document( all_documents, document_index, max_seq_length, short_seq_prob, masked_lm_prob, max_predictions_per_seq, vocab_words, rng): """Creates `TrainingInstance`s for a single document.""" document = all_documents[document_index] # Account for [CLS], [SEP], [SEP] ...
def create_masked_lm_predictions(tokens, masked_lm_prob, max_predictions_per_seq, vocab_words, rng): """Creates the predictions for the masked LM objective.""" cand_indexes = [] for (i, token) in enumerate(tokens): if token in ['[CLS]', '[SEP]']: continu...
def truncate_seq_pair(tokens_a, tokens_b, max_num_tokens, rng): """Truncates a pair of sequences to a maximum sequence length.""" while True: total_length = len(tokens_a) + len(tokens_b) if total_length <= max_num_tokens: break trunc_tokens = tokens_a if len(tokens_a) > len(...
def main(): """Main function.""" time_start = time.time() logging.info('loading vocab file from dataset: %s', args.vocab) vocab_obj = nlp.data.utils._load_pretrained_vocab(args.vocab) tokenizer = BERTTokenizer( vocab=vocab_obj, lower='uncased' in args.vocab) input_files = [] for inp...
def convert_vocab(vocab_file): """GluonNLP specific code to convert the original vocabulary to nlp.vocab.BERTVocab.""" original_vocab = load_vocab(vocab_file) token_to_idx = dict(original_vocab) num_tokens = len(token_to_idx) idx_to_token = [None] * len(original_vocab) for word in original_vocab...
def read_tf_checkpoint(path): """read tensorflow checkpoint""" from tensorflow.python import pywrap_tensorflow tensors = {} reader = pywrap_tensorflow.NewCheckpointReader(path) var_to_shape_map = reader.get_variable_to_shape_map() for key in sorted(var_to_shape_map): tensor = reader.get_...
def profile(curr_step, start_step, end_step, profile_name='profile.json', early_exit=True): """profile the program between [start_step, end_step).""" if curr_step == start_step: mx.nd.waitall() mx.profiler.set_config(profile_memory=False, profile_symbolic=True, ...

def standard_lstm_lm_200(dataset_name=None, vocab=None, pretrained=False, ctx=cpu(), root=os.path.join(get_home_dir(), 'models'), **kwargs): r"""Standard 2-layer LSTM language model with tied embedding and output weights. Both embedding and hidden dimensions are 200. Parameters ...

def big_rnn_lm_2048_512(dataset_name=None, vocab=None, pretrained=False, ctx=cpu(), root=os.path.join(get_home_dir(), 'models'), **kwargs): r"""Big 1-layer LSTMP language model. Both embedding and projection size are 512. Hidden size is 2048. Parameters ---------- dataset_n...

def forward(self, inputs, begin_state=None): # pylint: disable=arguments-differ """Implement forward computation. Parameters ----------- inputs : NDArray input tensor with shape `(sequence_length, batch_size)` when `layout` is "TNC". begin_state : list ...

def forward(self, inputs, begin_state): # pylint: disable=arguments-differ """Implement forward computation. Parameters ----------- inputs : NDArray input tensor with shape `(sequence_length, batch_size)` when `layout` is "TNC". begin_state : list ...

def _get_cell_type(cell_type): """Get the object type of the cell by parsing the input Parameters ---------- cell_type : str or type Returns ------- cell_constructor: type The constructor of the RNNCell """ if isinstance(cell_type, str): if cell_type == 'lstm': ...

def _get_context(center_idx, sentence_boundaries, window_size, random_window_size, seed): """Compute the context with respect to a center word in a sentence. Takes an numpy array of sentences boundaries. """ random.seed(seed + center_idx) sentence_index = np.searchsorted(sentence...

def model(dropout, vocab, model_mode, output_size): """Construct the model.""" textCNN = SentimentNet(dropout=dropout, vocab_size=len(vocab), model_mode=model_mode,\ output_size=output_size) textCNN.hybridize() return textCNN

def init(textCNN, vocab, model_mode, context, lr): """Initialize parameters.""" textCNN.initialize(mx.init.Xavier(), ctx=context, force_reinit=True) if model_mode != 'rand': textCNN.embedding.weight.set_data(vocab.embedding.idx_to_vec) if model_mode == 'multichannel': textCNN.embedding_...

def preprocess_dataset(dataset, transform, num_workers=8): """Use multiprocessing to perform transform for dataset. Parameters ---------- dataset: dataset-like object Source dataset. transform: callable Transformer function. num_workers: int, default 8 The number of mult...

def get_model(name, dataset_name='wikitext-2', **kwargs): """Returns a pre-defined model by name. Parameters ---------- name : str Name of the model. dataset_name : str or None, default 'wikitext-2'. The dataset name on which the pre-trained model is trained. For language mo...

def _masked_softmax(F, att_score, mask, dtype): """Ignore the masked elements when calculating the softmax Parameters ---------- F : symbol or ndarray att_score : Symborl or NDArray Shape (batch_size, query_length, memory_length) mask : Symbol or NDArray or None Shape (batch_siz...

def _read_by_weight(self, F, att_weights, value): """Read from the value matrix given the attention weights. Parameters ---------- F : symbol or ndarray att_weights : Symbol or NDArray Attention weights. For single-head attention, Shape (b...

def translate(self, src_seq, src_valid_length): """Get the translation result given the input sentence. Parameters ---------- src_seq : mx.nd.NDArray Shape (batch_size, length) src_valid_length : mx.nd.NDArray Shape (batch_size,) Returns ...

def evaluate_official_script(parser, vocab, num_buckets_test, test_batch_size, test_file, output_file, debug=False): """Evaluate parser on a data set Parameters ---------- parser : BiaffineParser biaffine parser vocab : ParserVocabulary vocabulary built ...

def parameter_from_numpy(self, name, array): """ Create parameter with its value initialized according to a numpy tensor Parameters ---------- name : str parameter name array : np.ndarray initiation value Returns ------- mxnet.glu...

def parameter_init(self, name, shape, init): """Create parameter given name, shape and initiator Parameters ---------- name : str parameter name shape : tuple parameter shape init : mxnet.initializer an initializer Returns ...

def forward(self, word_inputs, tag_inputs, arc_targets=None, rel_targets=None): """Run decoding Parameters ---------- word_inputs : mxnet.ndarray.NDArray word indices of seq_len x batch_size tag_inputs : mxnet.ndarray.NDArray tag indices of seq_len x batc...

def save_parameters(self, filename): """Save model Parameters ---------- filename : str path to model file """ params = self._collect_params_with_prefix() if self.pret_word_embs: # don't save word embeddings inside model params.pop('pret_...

def _worker_fn(samples, batchify_fn, dataset=None): """Function for processing data in worker process.""" # pylint: disable=unused-argument # it is required that each worker process has to fork a new MXIndexedRecordIO handle # preserving dataset as global variable can save tons of overhead and is safe i...

def _thread_worker_fn(samples, batchify_fn, dataset): """Threadpool worker function for processing data.""" if isinstance(samples[0], (list, tuple)): batch = [batchify_fn([dataset[i] for i in shard]) for shard in samples] else: batch = batchify_fn([dataset[i] for i in samples]) return ba...

def create(embedding_name, **kwargs): """Creates an instance of token embedding. Creates a token embedding instance by loading embedding vectors from an externally hosted pre-trained token embedding file, such as those of GloVe and FastText. To get all the valid `embedding_name` and `source`, use :fun...

def list_sources(embedding_name=None): """Get valid token embedding names and their pre-trained file names. To load token embedding vectors from an externally hosted pre-trained token embedding file, such as those of GloVe and FastText, one should use `gluonnlp.embedding.create(embedding_name, source)...

def _load_embedding(self, pretrained_file_path, elem_delim, encoding='utf8'): """Load embedding vectors from a pre-trained token embedding file. Both text files and TokenEmbedding serialization files are supported. elem_delim and encoding are ignored for non-text files. ...

def _load_embedding_txt(self, pretrained_file_path, elem_delim, encoding='utf8'): """Load embedding vectors from a pre-trained token embedding file. For every unknown token, if its representation `self.unknown_token` is encountered in the pre-trained token embedding file, index 0 of `self.idx_t...

def _load_embedding_serialized(self, pretrained_file_path): """Load embedding vectors from a pre-trained token embedding file. For every unknown token, if its representation `self.unknown_token` is encountered in the pre-trained token embedding file, index 0 of `self.idx_to_vec` maps to the pre...

def _check_vector_update(self, tokens, new_embedding): """Check that tokens and embedding are in the format for __setitem__.""" assert self._idx_to_vec is not None, '`idx_to_vec` has not been initialized.' if not isinstance(tokens, (list, tuple)) or len(tokens) == 1: assert isinstan...

def _check_source(cls, source_file_hash, source): """Checks if a pre-trained token embedding source name is valid. Parameters ---------- source : str The pre-trained token embedding source. """ embedding_name = cls.__name__.lower() if source not in s...

def from_file(file_path, elem_delim=' ', encoding='utf8', **kwargs): """Creates a user-defined token embedding from a pre-trained embedding file. This is to load embedding vectors from a user-defined pre-trained token embedding file. For example, if `elem_delim` = ' ', the expected format of a...

def serialize(self, file_path, compress=True): """Serializes the TokenEmbedding to a file specified by file_path. TokenEmbedding is serialized by converting the list of tokens, the array of word embeddings and other metadata to numpy arrays, saving all in a single (optionally compressed...

def deserialize(cls, file_path, **kwargs): """Create a new TokenEmbedding from a serialized one. TokenEmbedding is serialized by converting the list of tokens, the array of word embeddings and other metadata to numpy arrays, saving all in a single (optionally compressed) Zipfile. See ...

def evaluate(data_source): """Evaluate the model on a mini-batch. """ log.info('Start predict') tic = time.time() for batch in data_source: inputs, token_types, valid_length = batch out = net(inputs.astype('float32').as_in_context(ctx), token_types.astype('float32')...

def register(class_=None, **kwargs): """Registers a dataset with segment specific hyperparameters. When passing keyword arguments to `register`, they are checked to be valid keyword arguments for the registered Dataset class constructor and are saved in the registry. Registered keyword arguments can be...

def create(name, **kwargs): """Creates an instance of a registered dataset. Parameters ---------- name : str The dataset name (case-insensitive). Returns ------- An instance of :class:`mxnet.gluon.data.Dataset` constructed with the keyword arguments passed to the create functio...

def list_datasets(name=None): """Get valid datasets and registered parameters. Parameters ---------- name : str or None, default None Return names and registered parameters of registered datasets. If name is specified, only registered parameters of the respective dataset are ret...

def parse_args(): """Parse command line arguments.""" parser = argparse.ArgumentParser( description='Vocabulary extractor.', formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument('--max-size', type=int, default=None) parser.add_argument('--min-freq', type=int, defau...

def get_vocab(args): """Compute the vocabulary.""" counter = nlp.data.Counter() start = time.time() for filename in args.files: print('Starting processing of {} after {:.1f} seconds.'.format( filename, time.time() - start)) with open(filename, 'r') as f: ...

def forward(self, inputs, token_types, valid_length=None): # pylint: disable=arguments-differ """Generate the unnormalized score for the given the input sequences. Parameters ---------- inputs : NDArray, shape (batch_size, seq_length) Input words for the sequences. ...

def add_parameters(parser): """Add evaluation specific parameters to parser.""" group = parser.add_argument_group('Evaluation arguments') group.add_argument('--eval-batch-size', type=int, default=1024) # Datasets group.add_argument( '--similarity-datasets', type=str, default=nlp.da...

def validate_args(args): """Validate provided arguments and act on --help.""" # Check correctness of similarity dataset names for dataset_name in args.similarity_datasets: if dataset_name.lower() not in map( str.lower, nlp.data.word_embedding_evaluation.word_similarit...

def iterate_similarity_datasets(args): """Generator over all similarity evaluation datasets. Iterates over dataset names, keyword arguments for their creation and the created dataset. """ for dataset_name in args.similarity_datasets: parameters = nlp.data.list_datasets(dataset_name) ...

def iterate_analogy_datasets(args): """Generator over all analogy evaluation datasets. Iterates over dataset names, keyword arguments for their creation and the created dataset. """ for dataset_name in args.analogy_datasets: parameters = nlp.data.list_datasets(dataset_name) for key...

def get_similarity_task_tokens(args): """Returns a set of all tokens occurring the evaluation datasets.""" tokens = set() for _, _, dataset in iterate_similarity_datasets(args): tokens.update( itertools.chain.from_iterable((d[0], d[1]) for d in dataset)) return tokens

def get_analogy_task_tokens(args): """Returns a set of all tokens occuring the evaluation datasets.""" tokens = set() for _, _, dataset in iterate_analogy_datasets(args): tokens.update( itertools.chain.from_iterable( (d[0], d[1], d[2], d[3]) for d in dataset)) return ...

def evaluate_similarity(args, token_embedding, ctx, logfile=None, global_step=0): """Evaluate on specified similarity datasets.""" results = [] for similarity_function in args.similarity_functions: evaluator = nlp.embedding.evaluation.WordEmbeddingSimilarity( idx...

def evaluate_analogy(args, token_embedding, ctx, logfile=None, global_step=0): """Evaluate on specified analogy datasets. The analogy task is an open vocabulary task, make sure to pass a token_embedding with a sufficiently large number of supported tokens. """ results = [] exclude_question_wor...

def log_similarity_result(logfile, result): """Log a similarity evaluation result dictionary as TSV to logfile.""" assert result['task'] == 'similarity' if not logfile: return with open(logfile, 'a') as f: f.write('\t'.join([ str(result['global_step']), result['...

def get_model_loss(ctx, model, pretrained, dataset_name, dtype, ckpt_dir=None, start_step=None): """Get model for pre-training.""" # model model, vocabulary = nlp.model.get_model(model, dataset_name=dataset_name, pretrai...

def get_pretrain_dataset(data, batch_size, num_ctxes, shuffle, use_avg_len, num_buckets, num_parts=1, part_idx=0, prefetch=True): """create dataset for pretraining.""" num_files = len(glob.glob(os.path.expanduser(data))) logging.debug('%d files found.', num_files) assert num_fil...

def get_dummy_dataloader(dataloader, target_shape): """Return a dummy data loader which returns a fixed data batch of target shape""" data_iter = enumerate(dataloader) _, data_batch = next(data_iter) logging.debug('Searching target batch shape: %s', target_shape) while data_batch[0].shape != target_...

def save_params(step_num, model, trainer, ckpt_dir): """Save the model parameter, marked by step_num.""" param_path = os.path.join(ckpt_dir, '%07d.params'%step_num) trainer_path = os.path.join(ckpt_dir, '%07d.states'%step_num) logging.info('[step %d] Saving checkpoints to %s, %s.', step...

def log(begin_time, running_num_tks, running_mlm_loss, running_nsp_loss, step_num, mlm_metric, nsp_metric, trainer, log_interval): """Log training progress.""" end_time = time.time() duration = end_time - begin_time throughput = running_num_tks / duration / 1000.0 running_mlm_loss = running_...

def split_and_load(arrs, ctx): """split and load arrays to a list of contexts""" assert isinstance(arrs, (list, tuple)) # split and load loaded_arrs = [mx.gluon.utils.split_and_load(arr, ctx, even_split=False) for arr in arrs] return zip(*loaded_arrs)

def forward(data, model, mlm_loss, nsp_loss, vocab_size, dtype): """forward computation for evaluation""" (input_id, masked_id, masked_position, masked_weight, \ next_sentence_label, segment_id, valid_length) = data num_masks = masked_weight.sum() + 1e-8 valid_length = valid_length.reshape(-1) ...

def evaluate(data_eval, model, nsp_loss, mlm_loss, vocab_size, ctx, log_interval, dtype): """Evaluation function.""" mlm_metric = MaskedAccuracy() nsp_metric = MaskedAccuracy() mlm_metric.reset() nsp_metric.reset() eval_begin_time = time.time() begin_time = time.time() step_num = 0 ...

def get_argparser(): """Argument parser""" parser = argparse.ArgumentParser(description='BERT pretraining example.') parser.add_argument('--num_steps', type=int, default=20, help='Number of optimization steps') parser.add_argument('--num_buckets', type=int, default=1, help='Numbe...

def _cache_dataset(dataset, prefix): """Cache the processed npy dataset the dataset into a npz Parameters ---------- dataset : SimpleDataset file_path : str """ if not os.path.exists(_constants.CACHE_PATH): os.makedirs(_constants.CACHE_PATH) src_data = np.concatenate([e[0] for e...

def load_translation_data(dataset, bleu, args): """Load translation dataset Parameters ---------- dataset : str args : argparse result Returns ------- """ src_lang, tgt_lang = args.src_lang, args.tgt_lang if dataset == 'IWSLT2015': common_prefix = 'IWSLT2015_{}_{}_{}_{...

def make_dataloader(data_train, data_val, data_test, args, use_average_length=False, num_shards=0, num_workers=8): """Create data loaders for training/validation/test.""" data_train_lengths = get_data_lengths(data_train) data_val_lengths = get_data_lengths(data_val) data_test_lengths...

def run(self): """Method representing the process’s activity.""" random.seed(self.seed) np.random.seed(self.np_seed) if not isinstance(self, multiprocessing.Process): # Calling mxnet methods in a subprocess will raise an exception if # mxnet is built with GPU supp...

def forward(self, inputs, states=None): # pylint: disable=arguments-differ """Defines the forward computation. Arguments can be either :py:class:`NDArray` or :py:class:`Symbol`.""" batch_size = inputs.shape[self._batch_axis] skip_states = states is None if skip_states: ...

def hybrid_forward(self, F, words, weight): """Compute embedding of words in batch. Parameters ---------- words : mx.nd.NDArray Array of token indices. """ #pylint: disable=arguments-differ embeddings = F.sparse.dot(words, weight) return embe...

def load_fasttext_format(cls, path, ctx=cpu(), **kwargs): """Create an instance of the class and load weights. Load the weights from the fastText binary format created by https://github.com/facebookresearch/fastText Parameters ---------- path : str Path to t...

def logging_config(logpath=None, level=logging.DEBUG, console_level=logging.INFO, no_console=False): """ Config the logging. """ logger = logging.getLogger('nli') # Remove all the current handlers for handler in logger.handlers: lo...

def parse_args(): """Parse command line arguments.""" parser = argparse.ArgumentParser( description='GloVe with GluonNLP', formatter_class=argparse.ArgumentDefaultsHelpFormatter) # Data options group = parser.add_argument_group('Data arguments') group.add_argument( 'cooccurr...

def get_train_data(args): """Helper function to get training data.""" counter = dict() with io.open(args.vocab, 'r', encoding='utf-8') as f: for line in f: token, count = line.split('\t') counter[token] = int(count) vocab = nlp.Vocab(counter, unknown_token=None, padding_t...

def train(args): """Training helper.""" vocab, row, col, counts = get_train_data(args) model = GloVe(token_to_idx=vocab.token_to_idx, output_dim=args.emsize, dropout=args.dropout, x_max=args.x_max, alpha=args.alpha, weight_initializer=mx.init.Uniform(scale=1 / args.emsize...

def log(args, kwargs): """Log to a file.""" logfile = os.path.join(args.logdir, 'log.tsv') if 'log_created' not in globals(): if os.path.exists(logfile): logging.error('Logfile %s already exists.', logfile) sys.exit(1) global log_created log_created = sorte...

def hybrid_forward(self, F, row, col, counts): """Compute embedding of words in batch. Parameters ---------- row : mxnet.nd.NDArray or mxnet.sym.Symbol Array of token indices for source words. Shape (batch_size, ). row : mxnet.nd.NDArray or mxnet.sym.Symbol ...

def update(self, labels, preds, masks=None): # pylint: disable=arguments-differ """Updates the internal evaluation result. Parameters ---------- labels : list of `NDArray` The labels of the data with class indices as values, one per sample. preds : list of `N...

def hybrid_forward(self, F, sentence1, sentence2): """ Predict the relation of two sentences. Parameters ---------- sentence1 : NDArray Shape (batch_size, length) sentence2 : NDArray Shape (batch_size, length) Returns ------- ...

def hybrid_forward(self, F, feature_a): """ Compute intra-sentence attention given embedded words. Parameters ---------- feature_a : NDArray Shape (batch_size, length, hidden_size) Returns ------- alpha : NDArray Shape (batch_size...

def hybrid_forward(self, F, a, b): """ Forward of Decomposable Attention layer """ # a.shape = [B, L1, H] # b.shape = [B, L2, H] # extract features tilde_a = self.f(a) # shape = [B, L1, H] tilde_b = self.f(b) # shape = [B, L2, H] # attention ...

def count_tokens(tokens, to_lower=False, counter=None): r"""Counts tokens in the specified string. For token_delim='(td)' and seq_delim='(sd)', a specified string of two sequences of tokens may look like:: (td)token1(td)token2(td)token3(td)(sd)(td)token4(td)token5(td)(sd) Parameters ----...

def slice_sequence(sequence, length, pad_last=False, pad_val=C.PAD_TOKEN, overlap=0): """Slice a flat sequence of tokens into sequences tokens, with each inner sequence's length equal to the specified `length`, taking into account the requested sequence overlap. Parameters ---------- sequence :...

def _slice_pad_length(num_items, length, overlap=0): """Calculate the padding length needed for sliced samples in order not to discard data. Parameters ---------- num_items : int Number of items in dataset before collating. length : int The length of each of the samples. overlap...

def train_valid_split(dataset, valid_ratio=0.05): """Split the dataset into training and validation sets. Parameters ---------- dataset : list A list of training samples. valid_ratio : float, default 0.05 Proportion of training samples to use for validation set range: [0, 1]...

def _load_pretrained_vocab(name, root=os.path.join(get_home_dir(), 'models'), cls=None): """Load the accompanying vocabulary object for pre-trained model. Parameters ---------- name : str Name of the vocabulary, usually the name of the dataset. root : str, default '$MXNET_HOME/models' ...

def _extract_archive(file, target_dir): """Extract archive file Parameters ---------- file : str Absolute path of the archive file. target_dir : str Target directory of the archive to be uncompressed """ if file.endswith('.gz') or file.endswith('.tar') or file.endswith('.tg...

def discard(self, min_freq, unknown_token): """Discards tokens with frequency below min_frequency and represents them as `unknown_token`. Parameters ---------- min_freq: int Tokens whose frequency is under min_freq is counted as `unknown_token` in the Cou...

def train(): """Training function.""" trainer = gluon.Trainer(model.collect_params(), args.optimizer, {'learning_rate': args.lr, 'beta2': 0.98, 'epsilon': 1e-9}) train_data_loader, val_data_loader, test_data_loader \ = dataprocessor.make_dataloader(data_train, data_val, ...

def hybrid_forward(self, F, inputs, **kwargs): # pylint: disable=unused-argument r""" Forward computation for highway layer Parameters ---------- inputs: NDArray The input tensor is of shape `(..., input_size)`. Returns ---------- out...

def forward(self, inputs, token_types, valid_length=None): # pylint: disable=arguments-differ """Generate the unnormalized score for the given the input sequences. Parameters ---------- inputs : NDArray, shape (batch_size, seq_length) Input words for the sequences. ...

def hybrid_forward(self, F, inputs, states=None, mask=None): # pylint: disable=arguments-differ # pylint: disable=unused-argument """Defines the forward computation for cache cell. Arguments can be either :py:class:`NDArray` or :py:class:`Symbol`. Parameters ---------- ...

def main(args): """ Read tokens from the provided parse tree in the SNLI dataset. Illegal examples are removed. """ examples = [] with open(args.input, 'r') as fin: reader = csv.DictReader(fin, delimiter='\t') for cols in reader: s1 = read_tokens(cols['sentence1_parse...

def _recenter(self): """ one iteration of k-means """ for split_idx in range(len(self._splits)): split = self._splits[split_idx] len_idx = self._split2len_idx[split] if split == self._splits[-1]: continue right_split = self....

def _reindex(self): """ Index every sentence into a cluster """ self._len2split_idx = {} last_split = -1 for split_idx, split in enumerate(self._splits): self._len2split_idx.update( dict(list(zip(list(range(last_split + 1, split)), [split_idx] ...

def get_gnmt_encoder_decoder(cell_type='lstm', attention_cell='scaled_luong', num_layers=2, num_bi_layers=1, hidden_size=128, dropout=0.0, use_residual=False, i2h_weight_initializer=None, h2h_weight_initializer=None, i2h_bias_initial...

def init_state_from_encoder(self, encoder_outputs, encoder_valid_length=None): """Initialize the state from the encoder outputs. Parameters ---------- encoder_outputs : list encoder_valid_length : NDArray or None Returns ------- decoder_states : list ...

def decode_seq(self, inputs, states, valid_length=None): """Decode the decoder inputs. This function is only used for training. Parameters ---------- inputs : NDArray, Shape (batch_size, length, C_in) states : list of NDArrays or None Initial states. The list of init...

def transform(instance, tokenizer, max_seq_length, max_predictions_per_seq, do_pad=True): """Transform instance to inputs for MLM and NSP.""" pad = tokenizer.convert_tokens_to_ids(['[PAD]'])[0] input_ids = tokenizer.convert_tokens_to_ids(instance.tokens) input_mask = [1] * len(input_ids) segment_ids...

def write_to_files_np(features, tokenizer, max_seq_length, max_predictions_per_seq, output_files): # pylint: disable=unused-argument """Write to numpy files from `TrainingInstance`s.""" next_sentence_labels = [] valid_lengths = [] assert len(output_files) == 1, 'numpy format o...

def write_to_files_rec(instances, tokenizer, max_seq_length, max_predictions_per_seq, output_files): """Create IndexedRecordIO files from `TrainingInstance`s.""" writers = [] for output_file in output_files: writers.append( mx.recordio.MXIndexedRecordIO( ...

def create_training_instances(x): """Create `TrainingInstance`s from raw text.""" (input_files, out, tokenizer, max_seq_length, dupe_factor, short_seq_prob, masked_lm_prob, max_predictions_per_seq, rng) = x time_start = time.time() logging.info('Processing %s', input_files) all_documents = [[]]...

def create_instances_from_document( all_documents, document_index, max_seq_length, short_seq_prob, masked_lm_prob, max_predictions_per_seq, vocab_words, rng): """Creates `TrainingInstance`s for a single document.""" document = all_documents[document_index] # Account for [CLS], [SEP], [SEP] ...

def create_masked_lm_predictions(tokens, masked_lm_prob, max_predictions_per_seq, vocab_words, rng): """Creates the predictions for the masked LM objective.""" cand_indexes = [] for (i, token) in enumerate(tokens): if token in ['[CLS]', '[SEP]']: continu...

def truncate_seq_pair(tokens_a, tokens_b, max_num_tokens, rng): """Truncates a pair of sequences to a maximum sequence length.""" while True: total_length = len(tokens_a) + len(tokens_b) if total_length <= max_num_tokens: break trunc_tokens = tokens_a if len(tokens_a) > len(...

def main(): """Main function.""" time_start = time.time() logging.info('loading vocab file from dataset: %s', args.vocab) vocab_obj = nlp.data.utils._load_pretrained_vocab(args.vocab) tokenizer = BERTTokenizer( vocab=vocab_obj, lower='uncased' in args.vocab) input_files = [] for inp...

def convert_vocab(vocab_file): """GluonNLP specific code to convert the original vocabulary to nlp.vocab.BERTVocab.""" original_vocab = load_vocab(vocab_file) token_to_idx = dict(original_vocab) num_tokens = len(token_to_idx) idx_to_token = [None] * len(original_vocab) for word in original_vocab...

def read_tf_checkpoint(path): """read tensorflow checkpoint""" from tensorflow.python import pywrap_tensorflow tensors = {} reader = pywrap_tensorflow.NewCheckpointReader(path) var_to_shape_map = reader.get_variable_to_shape_map() for key in sorted(var_to_shape_map): tensor = reader.get_...

def profile(curr_step, start_step, end_step, profile_name='profile.json', early_exit=True): """profile the program between [start_step, end_step).""" if curr_step == start_step: mx.nd.waitall() mx.profiler.set_config(profile_memory=False, profile_symbolic=True, ...