Convert flags to argparse in 'extract_features_pytorch.py'

extract_features_pytorch.py

+# 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()