import modeling import tokenization from graph import optimize_graph import args from queue import Queue from threading import Thread import tensorflow as tf import os os.environ['CUDA_VISIBLE_DEVICES'] = '0' class InputExample(object): def __init__(self, unique_id, text_a, text_b): self.unique_id = unique_id self.text_a = text_a self.text_b = text_b class InputFeatures(object): """A single set of features of data.""" def __init__(self, unique_id, tokens, input_ids, input_mask, input_type_ids): self.unique_id = unique_id self.tokens = tokens self.input_ids = input_ids self.input_mask = input_mask self.input_type_ids = input_type_ids class BertVector: def __init__(self, batch_size=32): """ init BertVector :param batch_size: Depending on your memory default is 32 """ self.max_seq_length = args.max_seq_len self.layer_indexes = args.layer_indexes self.gpu_memory_fraction = 1 if os.path.exists(args.graph_file): self.graph_path = args.graph_file else: self.graph_path = optimize_graph() self.tokenizer = tokenization.FullTokenizer(vocab_file=args.vocab_file, do_lower_case=True) self.batch_size = batch_size self.estimator = self.get_estimator() self.input_queue = Queue(maxsize=1) self.output_queue = Queue(maxsize=1) self.predict_thread = Thread(target=self.predict_from_queue, daemon=True) self.predict_thread.start() def get_estimator(self): from tensorflow.python.estimator.estimator import Estimator from tensorflow.python.estimator.run_config import RunConfig from tensorflow.python.estimator.model_fn import EstimatorSpec def model_fn(features, labels, mode, params): with tf.gfile.GFile(self.graph_path, 'rb') as f: graph_def = tf.GraphDef() graph_def.ParseFromString(f.read()) input_names = ['input_ids', 'input_mask', 'input_type_ids'] output = tf.import_graph_def(graph_def, input_map={k + ':0': features[k] for k in input_names}, return_elements=['final_encodes:0']) return EstimatorSpec(mode=mode, predictions={ 'encodes': output[0] }) config = tf.ConfigProto() config.gpu_options.allow_growth = True config.gpu_options.per_process_gpu_memory_fraction = self.gpu_memory_fraction config.log_device_placement = False config.graph_options.optimizer_options.global_jit_level = tf.OptimizerOptions.ON_1 return Estimator(model_fn=model_fn, config=RunConfig(session_config=config), params={'batch_size': self.batch_size}, model_dir='../tmp') def predict_from_queue(self): prediction = self.estimator.predict(input_fn=self.queue_predict_input_fn, yield_single_examples=False) for i in prediction: self.output_queue.put(i) def encode(self, sentence): self.input_queue.put(sentence) prediction = self.output_queue.get()['encodes'] return prediction def queue_predict_input_fn(self): return (tf.data.Dataset.from_generator( self.generate_from_queue, output_types={'unique_ids': tf.int32, 'input_ids': tf.int32, 'input_mask': tf.int32, 'input_type_ids': tf.int32}, output_shapes={ 'unique_ids': (None,), 'input_ids': (None, self.max_seq_length), 'input_mask': (None, self.max_seq_length), 'input_type_ids': (None, self.max_seq_length)}).prefetch(10)) def generate_from_queue(self): while True: features = list(self.convert_examples_to_features(seq_length=self.max_seq_length, tokenizer=self.tokenizer)) yield { 'unique_ids': [f.unique_id for f in features], 'input_ids': [f.input_ids for f in features], 'input_mask': [f.input_mask for f in features], 'input_type_ids': [f.input_type_ids for f in features] } def input_fn_builder(self, features, seq_length): """Creates an `input_fn` closure to be passed to Estimator.""" all_unique_ids = [] all_input_ids = [] all_input_mask = [] all_input_type_ids = [] for feature in features: all_unique_ids.append(feature.unique_id) all_input_ids.append(feature.input_ids) all_input_mask.append(feature.input_mask) all_input_type_ids.append(feature.input_type_ids) def input_fn(params): """The actual input function.""" batch_size = params["batch_size"] num_examples = len(features) # This is for demo purposes and does NOT scale to large data sets. We do # not use Dataset.from_generator() because that uses tf.py_func which is # not TPU compatible. The right way to load data is with TFRecordReader. d = tf.data.Dataset.from_tensor_slices({ "unique_ids": tf.constant(all_unique_ids, shape=[num_examples], dtype=tf.int32), "input_ids": tf.constant( all_input_ids, shape=[num_examples, seq_length], dtype=tf.int32), "input_mask": tf.constant( all_input_mask, shape=[num_examples, seq_length], dtype=tf.int32), "input_type_ids": tf.constant( all_input_type_ids, shape=[num_examples, seq_length], dtype=tf.int32), }) d = d.batch(batch_size=batch_size, drop_remainder=False) return d return input_fn def model_fn_builder(self, bert_config, init_checkpoint, layer_indexes): """Returns `model_fn` closure for TPUEstimator.""" def model_fn(features, labels, mode, params): # pylint: disable=unused-argument """The `model_fn` for TPUEstimator.""" unique_ids = features["unique_ids"] input_ids = features["input_ids"] input_mask = features["input_mask"] input_type_ids = features["input_type_ids"] jit_scope = tf.contrib.compiler.jit.experimental_jit_scope with jit_scope(): model = modeling.BertModel( config=bert_config, is_training=False, input_ids=input_ids, input_mask=input_mask, token_type_ids=input_type_ids) if mode != tf.estimator.ModeKeys.PREDICT: raise ValueError("Only PREDICT modes are supported: %s" % (mode)) tvars = tf.trainable_variables() (assignment_map, initialized_variable_names) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint) tf.logging.info("**** Trainable Variables ****") for var in tvars: init_string = "" if var.name in initialized_variable_names: init_string = ", *INIT_FROM_CKPT*" tf.logging.info(" name = %s, shape = %s%s", var.name, var.shape, init_string) all_layers = model.get_all_encoder_layers() predictions = { "unique_id": unique_ids, } for (i, layer_index) in enumerate(layer_indexes): predictions["layer_output_%d" % i] = all_layers[layer_index] from tensorflow.python.estimator.model_fn import EstimatorSpec output_spec = EstimatorSpec(mode=mode, predictions=predictions) return output_spec return model_fn def convert_examples_to_features(self, seq_length, tokenizer): """Loads a data file into a list of `InputBatch`s.""" features = [] input_masks = [] examples = self._to_example(self.input_queue.get()) for (ex_index, example) in enumerate(examples): tokens_a = tokenizer.tokenize(example.text_a) # if the sentences's length is more than seq_length, only use sentence's left part if len(tokens_a) > seq_length - 2: tokens_a = tokens_a[0:(seq_length - 2)] # The convention in BERT is: # (a) For sequence pairs: # tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP] # type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1 # (b) For single sequences: # tokens: [CLS] the dog is hairy . [SEP] # type_ids: 0 0 0 0 0 0 0 # # Where "type_ids" are used to indicate whether this is the first # sequence or the second sequence. The embedding vectors for `type=0` and # `type=1` were learned during pre-training and are added to the wordpiece # embedding vector (and position vector). This is not *strictly* necessary # since the [SEP] token unambiguously separates the sequences, but it makes # it easier for the model to learn the concept of sequences. # # For classification tasks, the first vector (corresponding to [CLS]) is # used as as the "sentence vector". Note that this only makes sense because # the entire model is fine-tuned. tokens = [] input_type_ids = [] tokens.append("[CLS]") input_type_ids.append(0) for token in tokens_a: tokens.append(token) input_type_ids.append(0) tokens.append("[SEP]") input_type_ids.append(0) # Where "input_ids" are tokens's index in vocabulary input_ids = tokenizer.convert_tokens_to_ids(tokens) # The mask has 1 for real tokens and 0 for padding tokens. Only real # tokens are attended to. input_mask = [1] * len(input_ids) input_masks.append(input_mask) # Zero-pad up to the sequence length. while len(input_ids) < seq_length: input_ids.append(0) input_mask.append(0) input_type_ids.append(0) assert len(input_ids) == seq_length assert len(input_mask) == seq_length assert len(input_type_ids) == seq_length if ex_index < 5: tf.logging.info("*** Example ***") tf.logging.info("unique_id: %s" % (example.unique_id)) tf.logging.info("tokens: %s" % " ".join( [tokenization.printable_text(x) for x in tokens])) tf.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids])) tf.logging.info("input_mask: %s" % " ".join([str(x) for x in input_mask])) tf.logging.info( "input_type_ids: %s" % " ".join([str(x) for x in input_type_ids])) yield InputFeatures( unique_id=example.unique_id, tokens=tokens, input_ids=input_ids, input_mask=input_mask, input_type_ids=input_type_ids) def _truncate_seq_pair(self, tokens_a, tokens_b, max_length): """Truncates a sequence pair in place to the maximum length.""" # This is a simple heuristic which will always truncate the longer sequence # one token at a time. This makes more sense than truncating an equal percent # of tokens from each, since if one sequence is very short then each token # that's truncated likely contains more information than a longer sequence. while True: total_length = len(tokens_a) + len(tokens_b) if total_length <= max_length: break if len(tokens_a) > len(tokens_b): tokens_a.pop() else: tokens_b.pop() @staticmethod def _to_example(sentences): import re """ sentences to InputExample :param sentences: list of strings :return: list of InputExample """ unique_id = 0 for ss in sentences: line = tokenization.convert_to_unicode(ss) if not line: continue line = line.strip() text_a = None text_b = None m = re.match(r"^(.*) \|\|\| (.*)$", line) if m is None: text_a = line else: text_a = m.group(1) text_b = m.group(2) yield InputExample(unique_id=unique_id, text_a=text_a, text_b=text_b) unique_id += 1 if __name__ == "__main__": bert = BertVector() while True: question = input('question: ') v = bert.encode([question]) print(str(v))