diff --git a/extract_features_pytorch.py b/extract_features_pytorch.py new file mode 100644 index 000000000..80fff700f --- /dev/null +++ b/extract_features_pytorch.py @@ -0,0 +1,390 @@ +# coding=utf-8 +# Copyright 2018 The Google AI Language Team Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Extract pre-computed feature vectors from BERT.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import codecs +import collections +import json +import re + +import modeling +import tokenization +import tensorflow as tf + +import argparse + +parser = argparse.ArgumentParser() + +## Required parameters +parser.add_argument("--input_file", default=None, type=str, required=True) +parser.add_argument("--vocab_file", default=None, type=str, required=True, + help="The vocabulary file that the BERT model was trained on.") +parser.add_argument("--output_file", default=None, type=str, required=True) +parser.add_argument("--bert_config_file", default=None, type=str, required=True, + help="The config json file corresponding to the pre-trained BERT model. " + "This specifies the model architecture.") +parser.add_argument("--init_checkpoint", default=None, type=str, required=True, + help="Initial checkpoint (usually from a pre-trained BERT model).") + +## Other parameters +parser.add_argument("--layers", default="-1,-2,-3,-4", type=str) +parser.add_argument("--max_seq_length", default=128, type=int, + help="The maximum total input sequence length after WordPiece tokenization. Sequences longer " + "than this will be truncated, and sequences shorter than this will be padded.") +parser.add_argument("--do_lower_case", default=True, type=bool, + help="Whether to lower case the input text. Should be True for uncased " + "models and False for cased models.") +parser.add_argument("--batch_size", default=32, type=int, help="Batch size for predictions.") +### BEGIN - TO DELETE EVENTUALLY --> NO SENSE IN PYTORCH ### +parser.add_argument("--use_tpu", default=False, type=bool, help="Whether to use TPU or GPU/CPU.") +parser.add_argument("--master", default=None, type=str, help="If using a TPU, the address of the master.") +parser.add_argument("--num_tpu_cores", default=8, type=int, + help="Only used if `use_tpu` is True. Total number of TPU cores to use.") +### END - TO DELETE EVENTUALLY --> NO SENSE IN PYTORCH ### +parser.add_argument("--use_one_hot_embeddings", default=False, type=bool, + help="If True, tf.one_hot will be used for embedding lookups, otherwise tf.nn.embedding_lookup " + "will be used. On TPUs, this should be True since it is much faster.") + +args = parser.parse_args() + + +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 + + +def input_fn_builder(features, seq_length): + """Creates an `input_fn` closure to be passed to TPUEstimator.""" + + 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(bert_config, init_checkpoint, layer_indexes, use_tpu, + use_one_hot_embeddings): + """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"] + + model = modeling.BertModel( + config=bert_config, + is_training=False, + input_ids=input_ids, + input_mask=input_mask, + token_type_ids=input_type_ids, + use_one_hot_embeddings=use_one_hot_embeddings) + + if mode != tf.estimator.ModeKeys.PREDICT: + raise ValueError("Only PREDICT modes are supported: %s" % (mode)) + + tvars = tf.trainable_variables() + scaffold_fn = None + (assignment_map, _) = modeling.get_assigment_map_from_checkpoint( + tvars, init_checkpoint) + if use_tpu: + + def tpu_scaffold(): + tf.train.init_from_checkpoint(init_checkpoint, assignment_map) + return tf.train.Scaffold() + + scaffold_fn = tpu_scaffold + else: + tf.train.init_from_checkpoint(init_checkpoint, assignment_map) + + 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] + + output_spec = tf.contrib.tpu.TPUEstimatorSpec( + mode=mode, predictions=predictions, scaffold_fn=scaffold_fn) + return output_spec + + return model_fn + + +def convert_examples_to_features(examples, seq_length, tokenizer): + """Loads a data file into a list of `InputBatch`s.""" + + features = [] + for (ex_index, example) in enumerate(examples): + tokens_a = tokenizer.tokenize(example.text_a) + + tokens_b = None + if example.text_b: + tokens_b = tokenizer.tokenize(example.text_b) + + if tokens_b: + # Modifies `tokens_a` and `tokens_b` in place so that the total + # length is less than the specified length. + # Account for [CLS], [SEP], [SEP] with "- 3" + _truncate_seq_pair(tokens_a, tokens_b, seq_length - 3) + else: + # Account for [CLS] and [SEP] with "- 2" + 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 unambigiously 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) + + if tokens_b: + for token in tokens_b: + tokens.append(token) + input_type_ids.append(1) + tokens.append("[SEP]") + input_type_ids.append(1) + + 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) + + # 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([str(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])) + + features.append( + InputFeatures( + unique_id=example.unique_id, + tokens=tokens, + input_ids=input_ids, + input_mask=input_mask, + input_type_ids=input_type_ids)) + return features + + +def _truncate_seq_pair(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() + + +def read_examples(input_file): + """Read a list of `InputExample`s from an input file.""" + examples = [] + unique_id = 0 + with tf.gfile.GFile(input_file, "r") as reader: + while True: + line = tokenization.convert_to_unicode(reader.readline()) + if not line: + break + 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) + examples.append( + InputExample(unique_id=unique_id, text_a=text_a, text_b=text_b)) + unique_id += 1 + return examples + + +def main(_): + tf.logging.set_verbosity(tf.logging.INFO) + + layer_indexes = [int(x) for x in args.layers.split(",")] + + bert_config = modeling.BertConfig.from_json_file(args.bert_config_file) + + tokenizer = tokenization.FullTokenizer( + vocab_file=args.vocab_file, do_lower_case=args.do_lower_case) + + is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2 + run_config = tf.contrib.tpu.RunConfig( + master=args.master, + tpu_config=tf.contrib.tpu.TPUConfig( + num_shards=args.num_tpu_cores, + per_host_input_for_training=is_per_host)) + + examples = read_examples(args.input_file) + + features = convert_examples_to_features( + examples=examples, seq_length=args.max_seq_length, tokenizer=tokenizer) + + unique_id_to_feature = {} + for feature in features: + unique_id_to_feature[feature.unique_id] = feature + + model_fn = model_fn_builder( + bert_config=bert_config, + init_checkpoint=args.init_checkpoint, + layer_indexes=layer_indexes, + use_tpu=args.use_tpu, + use_one_hot_embeddings=args.use_one_hot_embeddings) + + # If TPU is not available, this will fall back to normal Estimator on CPU + # or GPU. + estimator = tf.contrib.tpu.TPUEstimator( + use_tpu=args.use_tpu, + model_fn=model_fn, + config=run_config, + predict_batch_size=args.batch_size) + + input_fn = input_fn_builder( + features=features, seq_length=args.max_seq_length) + + with codecs.getwriter("utf-8")(tf.gfile.Open(args.output_file, + "w")) as writer: + for result in estimator.predict(input_fn, yield_single_examples=True): + unique_id = int(result["unique_id"]) + feature = unique_id_to_feature[unique_id] + output_json = collections.OrderedDict() + output_json["linex_index"] = unique_id + all_features = [] + for (i, token) in enumerate(feature.tokens): + all_layers = [] + for (j, layer_index) in enumerate(layer_indexes): + layer_output = result["layer_output_%d" % j] + layers = collections.OrderedDict() + layers["index"] = layer_index + layers["values"] = [ + round(float(x), 6) for x in layer_output[i:(i + 1)].flat + ] + all_layers.append(layers) + features = collections.OrderedDict() + features["token"] = token + features["layers"] = all_layers + all_features.append(features) + output_json["features"] = all_features + writer.write(json.dumps(output_json) + "\n") + + +if __name__ == "__main__": + tf.app.run()