JorgeVB/code_unlearning_dataset · Datasets at Hugging Face

text stringlengths 81 112k
r"""Standard 2-layer LSTM language model with tied embedding and output weights. Both embedding and hidden dimensions are 200. Parameters ---------- dataset_name : str or None, default None The dataset name on which the pre-trained model is trained. Options are 'wikitext-2'. If specifi...
r"""Big 1-layer LSTMP language model. Both embedding and projection size are 512. Hidden size is 2048. Parameters ---------- dataset_name : str or None, default None The dataset name on which the pre-trained model is trained. Options are 'gbw'. If specified, then the returned vocabular...
Implement forward computation. Parameters ----------- inputs : NDArray input tensor with shape `(sequence_length, batch_size)` when `layout` is "TNC". begin_state : list initial recurrent state tensor with length equals to num_layers. the ...
Implement forward computation. Parameters ----------- inputs : NDArray input tensor with shape `(sequence_length, batch_size)` when `layout` is "TNC". begin_state : list initial recurrent state tensor with length equals to num_layers*2. Fo...
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 def _get_cell_type(cell_type): """Get the object type of the cell by parsing the input Parameters -----...
Compute the context with respect to a center word in a sentence. Takes an numpy array of sentences boundaries. 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 arra...
Construct the model. 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
Initialize parameters. 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': ...
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 multiprocessing workers to use for data preprocessing. def preprocess...
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 model, options are 'wikitext-2'. For ELMo, Options are 'gbw...
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_size, query_length, memory_length) Returns -------...
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 (batch_size, query_length, memory_length). For mult...
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 ------- samples : NDArray Samples draw ...
Evaluate parser on a data set Parameters ---------- parser : BiaffineParser biaffine parser vocab : ParserVocabulary vocabulary built from data set num_buckets_test : int size of buckets (cluster sentences into this number of clusters) test_batch_size : int batch...
Create parameter with its value initialized according to a numpy tensor Parameters ---------- name : str parameter name array : np.ndarray initiation value Returns ------- mxnet.gluon.parameter a parameter object def paramete...
Create parameter given name, shape and initiator Parameters ---------- name : str parameter name shape : tuple parameter shape init : mxnet.initializer an initializer Returns ------- mxnet.gluon.parameter a...
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 batch_size arc_targets : mxnet.ndarray.NDArray gold arc indices of seq_len ...
Save model Parameters ---------- filename : str path to model file def save_parameters(self, filename): """Save model Parameters ---------- filename : str path to model file """ params = self._collect_params_with_prefix()...
Function for processing data in worker process. 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...
Threadpool worker function for processing data. 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 = batchif...
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 :func:`gluonnlp.embedding.list_sources`. Pa...
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)`. This method returns all the valid names...
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. For every unknown token, if its representation `self.unknown_token` is encountered in the pre-tr...
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-trained token embedding vector loaded from the file; otherwise...
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-trained token embedding vector loaded from the file; otherwise...
Check that tokens and embedding are in the format for __setitem__. 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(tok...
Checks if a pre-trained token embedding source name is valid. Parameters ---------- source : str The pre-trained token embedding source. def _check_source(cls, source_file_hash, source): """Checks if a pre-trained token embedding source name is valid. Parameters ...
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 custom pre-trained token embedding file may look like: 'hello ...
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) Zipfile. See https://docs.scipy.org/doc/numpy-1....
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 https://docs.scipy.org/doc/numpy-1.14.2/neps/npy...
Evaluate the model on a mini-batch. 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), ...
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 retrieved with the `list_datasets` func...
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 function. def create(name, **kwargs): ...
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 returned. Returns ------- d...
Parse command line arguments. 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_argumen...
Compute the vocabulary. 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(filen...
Generate the unnormalized score for the given the input sequences. Parameters ---------- inputs : NDArray, shape (batch_size, seq_length) Input words for the sequences. token_types : NDArray, shape (batch_size, seq_length) Token types for the sequences, used to i...
Add evaluation specific parameters to parser. 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( '--similar...
Validate provided arguments and act on --help. 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, n...
Generator over all similarity evaluation datasets. Iterates over dataset names, keyword arguments for their creation and the created dataset. def iterate_similarity_datasets(args): """Generator over all similarity evaluation datasets. Iterates over dataset names, keyword arguments for their creation ...
Generator over all analogy evaluation datasets. Iterates over dataset names, keyword arguments for their creation and the created dataset. def iterate_analogy_datasets(args): """Generator over all analogy evaluation datasets. Iterates over dataset names, keyword arguments for their creation and the ...
Returns a set of all tokens occurring the evaluation datasets. 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((...
Returns a set of all tokens occuring the evaluation datasets. 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( ...
Evaluate on specified similarity datasets. 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.evaluat...
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. def evaluate_analogy(args, token_embedding, ctx, logfile=None, global_step=0): """Evaluate on specified analogy datasets. The ...
Log a similarity evaluation result dictionary as TSV to logfile. 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'.j...
Get model for pre-training. 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, ...
create dataset for pretraining. 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...
Return a dummy data loader which returns a fixed data batch of target shape 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 ta...
Save the model parameter, marked by step_num. 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...
Log training progress. 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 run...
split and load arrays to a list of contexts 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)
forward computation for evaluation 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_le...
Evaluation function. 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()...
Argument parser 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, ...
Cache the processed npy dataset the dataset into a npz Parameters ---------- dataset : SimpleDataset file_path : str def _cache_dataset(dataset, prefix): """Cache the processed npy dataset the dataset into a npz Parameters ---------- dataset : SimpleDataset file_path : str """...
Load translation dataset Parameters ---------- dataset : str args : argparse result Returns ------- def load_translation_data(dataset, bleu, args): """Load translation dataset Parameters ---------- dataset : str args : argparse result Returns ------- """ ...
Create data loaders for training/validation/test. 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...
Method representing the process’s activity. 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 ...
Defines the forward computation. Arguments can be either :py:class:`NDArray` or :py:class:`Symbol`. 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_...
Compute embedding of words in batch. Parameters ---------- words : mx.nd.NDArray Array of token indices. def hybrid_forward(self, F, words, weight): """Compute embedding of words in batch. Parameters ---------- words : mx.nd.NDArray Arra...
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 the .bin model file. ctx : mx.Context, default mx.cpu() ...
Config the logging. 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...
Parse command line arguments. 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....
Helper function to get training data. 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...
Training helper. 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(sca...
Log to a file. 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...
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 Array of token indices for context words. Shape (batch_...
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 `NDArray` Prediction values for samples. Each prediction value can either be the class in...
Predict the relation of two sentences. Parameters ---------- sentence1 : NDArray Shape (batch_size, length) sentence2 : NDArray Shape (batch_size, length) Returns ------- pred : NDArray Shape (batch_size, num_classes). num_cla...
Compute intra-sentence attention given embedded words. Parameters ---------- feature_a : NDArray Shape (batch_size, length, hidden_size) Returns ------- alpha : NDArray Shape (batch_size, length, hidden_size) def hybrid_forward(self, F, feature_...
Forward of Decomposable Attention layer 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) # sh...
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 ---------- tokens : list of str A source list of tok...
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 : list of object A flat list of tokens. length : int The length of each of...
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 : int, default 0 The extra number of items in curre...
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] Returns ------- train : SimpleDataset v...
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' Location for keeping the model parameters. MXNET_HOME defaults to '~/.mxnet'. cls...
Extract archive file Parameters ---------- file : str Absolute path of the archive file. target_dir : str Target directory of the archive to be uncompressed def _extract_archive(file, target_dir): """Extract archive file Parameters ---------- file : str Absolut...
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 Counter returned. unknown_token: str T...
Training function. 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(da...
r""" Forward computation for highway layer Parameters ---------- inputs: NDArray The input tensor is of shape `(..., input_size)`. Returns ---------- outputs: NDArray The output tensor is of the same shape with input tensor `(..., input_s...
Generate the unnormalized score for the given the input sequences. Parameters ---------- inputs : NDArray, shape (batch_size, seq_length) Input words for the sequences. token_types : NDArray, shape (batch_size, seq_length) Token types for the sequences, used to i...
Defines the forward computation for cache cell. Arguments can be either :py:class:`NDArray` or :py:class:`Symbol`. Parameters ---------- inputs : NDArray The input data layout='TNC'. states : Tuple[List[List[NDArray]]] The states. including: s...
Read tokens from the provided parse tree in the SNLI dataset. Illegal examples are removed. 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(fi...
one iteration of k-means 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 ...
Index every sentence into a cluster 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(...
Build a pair of GNMT encoder/decoder Parameters ---------- cell_type : str or type attention_cell : str or AttentionCell num_layers : int num_bi_layers : int hidden_size : int dropout : float use_residual : bool i2h_weight_initializer : mx.init.Initializer or None h2h_weight...
Initialize the state from the encoder outputs. Parameters ---------- encoder_outputs : list encoder_valid_length : NDArray or None Returns ------- decoder_states : list The decoder states, includes: - rnn_states : NDArray - a...
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 initial decoder states valid_length : NDArray or None ...
Transform instance to inputs for MLM and NSP. 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) in...
Write to numpy files from `TrainingInstance`s. 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 = [] ...
Create IndexedRecordIO files from `TrainingInstance`s. 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.appen...
Create `TrainingInstance`s from raw text. 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...
Creates `TrainingInstance`s for a single document. 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[doc...
Creates the predictions for the masked LM objective. 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): ...
Truncates a pair of sequences to a maximum sequence length. 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: b...
Main function. 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 ...
GluonNLP specific code to convert the original vocabulary to nlp.vocab.BERTVocab. 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_...
read tensorflow checkpoint 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): ...
profile the program between [start_step, end_step). 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...

r"""Standard 2-layer LSTM language model with tied embedding and output weights. Both embedding and hidden dimensions are 200. Parameters ---------- dataset_name : str or None, default None The dataset name on which the pre-trained model is trained. Options are 'wikitext-2'. If specifi...

r"""Big 1-layer LSTMP language model. Both embedding and projection size are 512. Hidden size is 2048. Parameters ---------- dataset_name : str or None, default None The dataset name on which the pre-trained model is trained. Options are 'gbw'. If specified, then the returned vocabular...

Implement forward computation. Parameters ----------- inputs : NDArray input tensor with shape `(sequence_length, batch_size)` when `layout` is "TNC". begin_state : list initial recurrent state tensor with length equals to num_layers. the ...

Implement forward computation. Parameters ----------- inputs : NDArray input tensor with shape `(sequence_length, batch_size)` when `layout` is "TNC". begin_state : list initial recurrent state tensor with length equals to num_layers*2. Fo...

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 def _get_cell_type(cell_type): """Get the object type of the cell by parsing the input Parameters -----...

Compute the context with respect to a center word in a sentence. Takes an numpy array of sentences boundaries. 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 arra...

Construct the model. 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

Initialize parameters. 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': ...

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 multiprocessing workers to use for data preprocessing. def preprocess...

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 model, options are 'wikitext-2'. For ELMo, Options are 'gbw...

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_size, query_length, memory_length) Returns -------...

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 (batch_size, query_length, memory_length). For mult...

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 ------- samples : NDArray Samples draw ...

Evaluate parser on a data set Parameters ---------- parser : BiaffineParser biaffine parser vocab : ParserVocabulary vocabulary built from data set num_buckets_test : int size of buckets (cluster sentences into this number of clusters) test_batch_size : int batch...

Create parameter with its value initialized according to a numpy tensor Parameters ---------- name : str parameter name array : np.ndarray initiation value Returns ------- mxnet.gluon.parameter a parameter object def paramete...

Create parameter given name, shape and initiator Parameters ---------- name : str parameter name shape : tuple parameter shape init : mxnet.initializer an initializer Returns ------- mxnet.gluon.parameter a...

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 batch_size arc_targets : mxnet.ndarray.NDArray gold arc indices of seq_len ...

Save model Parameters ---------- filename : str path to model file def save_parameters(self, filename): """Save model Parameters ---------- filename : str path to model file """ params = self._collect_params_with_prefix()...

Function for processing data in worker process. 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...

Threadpool worker function for processing data. 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 = batchif...

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 :func:`gluonnlp.embedding.list_sources`. Pa...

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)`. This method returns all the valid names...

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. For every unknown token, if its representation `self.unknown_token` is encountered in the pre-tr...

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-trained token embedding vector loaded from the file; otherwise...

Check that tokens and embedding are in the format for __setitem__. 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(tok...

Checks if a pre-trained token embedding source name is valid. Parameters ---------- source : str The pre-trained token embedding source. def _check_source(cls, source_file_hash, source): """Checks if a pre-trained token embedding source name is valid. Parameters ...

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 custom pre-trained token embedding file may look like: 'hello ...

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) Zipfile. See https://docs.scipy.org/doc/numpy-1....

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 https://docs.scipy.org/doc/numpy-1.14.2/neps/npy...

Evaluate the model on a mini-batch. 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), ...

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 retrieved with the `list_datasets` func...

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 function. def create(name, **kwargs): ...

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 returned. Returns ------- d...

Parse command line arguments. 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_argumen...

Compute the vocabulary. 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(filen...

Generate the unnormalized score for the given the input sequences. Parameters ---------- inputs : NDArray, shape (batch_size, seq_length) Input words for the sequences. token_types : NDArray, shape (batch_size, seq_length) Token types for the sequences, used to i...

Add evaluation specific parameters to parser. 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( '--similar...

Validate provided arguments and act on --help. 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, n...

Generator over all similarity evaluation datasets. Iterates over dataset names, keyword arguments for their creation and the created dataset. def iterate_similarity_datasets(args): """Generator over all similarity evaluation datasets. Iterates over dataset names, keyword arguments for their creation ...

Generator over all analogy evaluation datasets. Iterates over dataset names, keyword arguments for their creation and the created dataset. def iterate_analogy_datasets(args): """Generator over all analogy evaluation datasets. Iterates over dataset names, keyword arguments for their creation and the ...

Returns a set of all tokens occurring the evaluation datasets. 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((...

Returns a set of all tokens occuring the evaluation datasets. 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( ...

Evaluate on specified similarity datasets. 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.evaluat...

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. def evaluate_analogy(args, token_embedding, ctx, logfile=None, global_step=0): """Evaluate on specified analogy datasets. The ...

Log a similarity evaluation result dictionary as TSV to logfile. 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'.j...

Get model for pre-training. 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, ...

create dataset for pretraining. 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...

Return a dummy data loader which returns a fixed data batch of target shape 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 ta...

Save the model parameter, marked by step_num. 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...

Log training progress. 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 run...

split and load arrays to a list of contexts 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)

forward computation for evaluation 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_le...

Evaluation function. 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()...

Argument parser 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, ...

Cache the processed npy dataset the dataset into a npz Parameters ---------- dataset : SimpleDataset file_path : str def _cache_dataset(dataset, prefix): """Cache the processed npy dataset the dataset into a npz Parameters ---------- dataset : SimpleDataset file_path : str """...

Load translation dataset Parameters ---------- dataset : str args : argparse result Returns ------- def load_translation_data(dataset, bleu, args): """Load translation dataset Parameters ---------- dataset : str args : argparse result Returns ------- """ ...

Create data loaders for training/validation/test. 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...

Method representing the process’s activity. 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 ...

Defines the forward computation. Arguments can be either :py:class:`NDArray` or :py:class:`Symbol`. 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_...

Compute embedding of words in batch. Parameters ---------- words : mx.nd.NDArray Array of token indices. def hybrid_forward(self, F, words, weight): """Compute embedding of words in batch. Parameters ---------- words : mx.nd.NDArray Arra...

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 the .bin model file. ctx : mx.Context, default mx.cpu() ...

Config the logging. 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...

Parse command line arguments. 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....

Helper function to get training data. 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...

Training helper. 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(sca...

Log to a file. 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...

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 Array of token indices for context words. Shape (batch_...

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 `NDArray` Prediction values for samples. Each prediction value can either be the class in...

Predict the relation of two sentences. Parameters ---------- sentence1 : NDArray Shape (batch_size, length) sentence2 : NDArray Shape (batch_size, length) Returns ------- pred : NDArray Shape (batch_size, num_classes). num_cla...

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

Forward of Decomposable Attention layer 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) # sh...

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 ---------- tokens : list of str A source list of tok...

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 : list of object A flat list of tokens. length : int The length of each of...

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 : int, default 0 The extra number of items in curre...

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] Returns ------- train : SimpleDataset v...

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' Location for keeping the model parameters. MXNET_HOME defaults to '~/.mxnet'. cls...

Extract archive file Parameters ---------- file : str Absolute path of the archive file. target_dir : str Target directory of the archive to be uncompressed def _extract_archive(file, target_dir): """Extract archive file Parameters ---------- file : str Absolut...

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 Counter returned. unknown_token: str T...

Training function. 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(da...

r""" Forward computation for highway layer Parameters ---------- inputs: NDArray The input tensor is of shape `(..., input_size)`. Returns ---------- outputs: NDArray The output tensor is of the same shape with input tensor `(..., input_s...

Generate the unnormalized score for the given the input sequences. Parameters ---------- inputs : NDArray, shape (batch_size, seq_length) Input words for the sequences. token_types : NDArray, shape (batch_size, seq_length) Token types for the sequences, used to i...

Defines the forward computation for cache cell. Arguments can be either :py:class:`NDArray` or :py:class:`Symbol`. Parameters ---------- inputs : NDArray The input data layout='TNC'. states : Tuple[List[List[NDArray]]] The states. including: s...

Read tokens from the provided parse tree in the SNLI dataset. Illegal examples are removed. 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(fi...

one iteration of k-means 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 ...

Index every sentence into a cluster 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(...

Build a pair of GNMT encoder/decoder Parameters ---------- cell_type : str or type attention_cell : str or AttentionCell num_layers : int num_bi_layers : int hidden_size : int dropout : float use_residual : bool i2h_weight_initializer : mx.init.Initializer or None h2h_weight...

Initialize the state from the encoder outputs. Parameters ---------- encoder_outputs : list encoder_valid_length : NDArray or None Returns ------- decoder_states : list The decoder states, includes: - rnn_states : NDArray - a...

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 initial decoder states valid_length : NDArray or None ...

Transform instance to inputs for MLM and NSP. 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) in...

Write to numpy files from `TrainingInstance`s. 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 = [] ...

Create IndexedRecordIO files from `TrainingInstance`s. 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.appen...

Create `TrainingInstance`s from raw text. 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...

Creates `TrainingInstance`s for a single document. 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[doc...

Creates the predictions for the masked LM objective. 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): ...

Truncates a pair of sequences to a maximum sequence length. 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: b...

Main function. 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 ...

GluonNLP specific code to convert the original vocabulary to nlp.vocab.BERTVocab. 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_...

read tensorflow checkpoint 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): ...

profile the program between [start_step, end_step). 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...