# 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. """BERT finetuning runner.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import csv import os # import modeling_pytorch # import optimization import tokenization_pytorch import torch import logging logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt = '%m/%d/%Y %H:%M:%S', level = logging.INFO) logger = logging.getLogger(__name__) import argparse parser = argparse.ArgumentParser() ## Required parameters parser.add_argument("--data_dir", default = None, type = str, required = True, help = "The input data dir. Should contain the .tsv files (or other data files) for the task.") parser.add_argument("--bert_config_file", default = None, type = str, required = True, help = "The config json file corresponding to the pre-trained BERT model. \n" "This specifies the model architecture.") parser.add_argument("--task_name", default = None, type = str, required = True, help = "The name of the task to train.") 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_dir", default = None, type = str, required = True, help = "The output directory where the model checkpoints will be written.") ## Other parameters parser.add_argument("--init_checkpoint", default = None, type = str, help = "Initial checkpoint (usually from a pre-trained BERT model).") 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("--max_seq_length", default = 128, type = int, help = "The maximum total input sequence length after WordPiece tokenization. \n" "Sequences longer than this will be truncated, and sequences shorter \n" "than this will be padded.") parser.add_argument("--do_train", default = False, type = bool, help = "Whether to run training.") parser.add_argument("--do_eval", default = False, type = bool, help = "Whether to run eval on the dev set.") parser.add_argument("--train_batch_size", default = 32, type = int, help = "Total batch size for training.") parser.add_argument("--eval_batch_size", default = 8, type = int, help = "Total batch size for eval.") parser.add_argument("--learning_rate", default = 5e-5, type = float, help = "The initial learning rate for Adam.") parser.add_argument("--num_train_epochs", default = 3.0, type = float, help = "Total number of training epochs to perform.") parser.add_argument("--warmup_proportion", default = 0.1, type = float, help = "Proportion of training to perform linear learning rate warmup for. " "E.g., 0.1 = 10%% of training.") parser.add_argument("--save_checkpoints_steps", default = 1000, type = int, help = "How often to save the model checkpoint.") parser.add_argument("--iterations_per_loop", default = 1000, type = int, help = "How many steps to make in each estimator call.") ### 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("--tpu_name", default = None, type = str, help = "The Cloud TPU to use for training. This should be either the name " "used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 " "url.") parser.add_argument("--tpu_zone", default = None, type = str, help = "[Optional] GCE zone where the Cloud TPU is located in. If not " "specified, we will attempt to automatically detect the GCE project from " "metadata.") parser.add_argument("--gcp_project", default = None, type = str, help = "[Optional] Project name for the Cloud TPU-enabled project. If not " "specified, we will attempt to automatically detect the GCE project from " "metadata.") parser.add_argument("--master", default = None, type = str, help = "[Optional] TensorFlow master URL.") 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 ### args = parser.parse_args() class InputExample(object): """A single training/test example for simple sequence classification.""" def __init__(self, guid, text_a, text_b=None, label=None): """Constructs a InputExample. Args: guid: Unique id for the example. text_a: string. The untokenized text of the first sequence. For single sequence tasks, only this sequence must be specified. text_b: (Optional) string. The untokenized text of the second sequence. Only must be specified for sequence pair tasks. label: (Optional) string. The label of the example. This should be specified for train and dev examples, but not for test examples. """ self.guid = guid self.text_a = text_a self.text_b = text_b self.label = label class InputFeatures(object): """A single set of features of data.""" def __init__(self, input_ids, input_mask, segment_ids, label_id): self.input_ids = input_ids self.input_mask = input_mask self.segment_ids = segment_ids self.label_id = label_id class DataProcessor(object): """Base class for data converters for sequence classification data sets.""" def get_train_examples(self, data_dir): """Gets a collection of `InputExample`s for the train set.""" raise NotImplementedError() def get_dev_examples(self, data_dir): """Gets a collection of `InputExample`s for the dev set.""" raise NotImplementedError() def get_labels(self): """Gets the list of labels for this data set.""" raise NotImplementedError() @classmethod def _read_tsv(cls, input_file, quotechar=None): """Reads a tab separated value file.""" with open(input_file, "r") as f: reader = csv.reader(f, delimiter="\t", quotechar=quotechar) lines = [] for line in reader: lines.append(line) return lines class MnliProcessor(DataProcessor): """Processor for the MultiNLI data set (GLUE version).""" def get_train_examples(self, data_dir): """See base class.""" return self._create_examples( self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") def get_dev_examples(self, data_dir): """See base class.""" return self._create_examples( self._read_tsv(os.path.join(data_dir, "dev_matched.tsv")), "dev_matched") def get_labels(self): """See base class.""" return ["contradiction", "entailment", "neutral"] def _create_examples(self, lines, set_type): """Creates examples for the training and dev sets.""" examples = [] for (i, line) in enumerate(lines): if i == 0: continue guid = "%s-%s" % (set_type, tokenization_pytorch.convert_to_unicode(line[0])) text_a = tokenization_pytorch.convert_to_unicode(line[8]) text_b = tokenization_pytorch.convert_to_unicode(line[9]) label = tokenization_pytorch.convert_to_unicode(line[-1]) examples.append( InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) return examples class ColaProcessor(DataProcessor): """Processor for the CoLA data set (GLUE version).""" def get_train_examples(self, data_dir): """See base class.""" return self._create_examples( self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") def get_dev_examples(self, data_dir): """See base class.""" return self._create_examples( self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev") def get_labels(self): """See base class.""" return ["0", "1"] def _create_examples(self, lines, set_type): """Creates examples for the training and dev sets.""" examples = [] for (i, line) in enumerate(lines): guid = "%s-%s" % (set_type, i) text_a = tokenization_pytorch.convert_to_unicode(line[3]) label = tokenization_pytorch.convert_to_unicode(line[1]) examples.append( InputExample(guid=guid, text_a=text_a, text_b=None, label=label)) return examples def convert_examples_to_features(examples, label_list, max_seq_length, tokenizer): """Loads a data file into a list of `InputBatch`s.""" label_map = {} for (i, label) in enumerate(label_list): label_map[label] = i 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, max_seq_length - 3) else: # Account for [CLS] and [SEP] with "- 2" if len(tokens_a) > max_seq_length - 2: tokens_a = tokens_a[0:(max_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 = [] segment_ids = [] tokens.append("[CLS]") segment_ids.append(0) for token in tokens_a: tokens.append(token) segment_ids.append(0) tokens.append("[SEP]") segment_ids.append(0) if tokens_b: for token in tokens_b: tokens.append(token) segment_ids.append(1) tokens.append("[SEP]") segment_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) < max_seq_length: input_ids.append(0) input_mask.append(0) segment_ids.append(0) assert len(input_ids) == max_seq_length assert len(input_mask) == max_seq_length assert len(segment_ids) == max_seq_length label_id = label_map[example.label] if ex_index < 5: logger.info("*** Example ***") logger.info("guid: %s" % (example.guid)) logger.info("tokens: %s" % " ".join( [tokenization_pytorch.printable_text(x) for x in tokens])) logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids])) logger.info("input_mask: %s" % " ".join([str(x) for x in input_mask])) logger.info( "segment_ids: %s" % " ".join([str(x) for x in segment_ids])) logger.info("label: %s (id = %d)" % (example.label, label_id)) features.append( InputFeatures( input_ids=input_ids, input_mask=input_mask, segment_ids=segment_ids, label_id=label_id)) 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 create_model(bert_config, is_training, input_ids, input_mask, segment_ids, labels, num_labels, use_one_hot_embeddings): raise NotImplementedError() def model_fn_builder(bert_config, num_labels, init_checkpoint, learning_rate, num_train_steps, num_warmup_steps, use_one_hot_embeddings): raise NotImplementedError() ### ATTENTION - I removed the `use_tpu` argument def input_fn_builder(features, seq_length, is_training, drop_remainder): """Creates an `input_fn` closure to be passed to TPUEstimator.""" ### ATTENTION - To rewrite ### all_input_ids = [] all_input_mask = [] all_segment_ids = [] all_label_ids = [] for feature in features: all_input_ids.append(feature.input_ids) all_input_mask.append(feature.input_mask) all_segment_ids.append(feature.segment_ids) all_label_ids.append(feature.label_id) 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({ "input_ids": torch.Tensor(all_input_ids, size=[num_examples, seq_length], dtype=torch.int32, requires_grad=False), "input_mask": torch.Tensor(all_input_mask, size=[num_examples, seq_length], dtype=torch.int32, requires_grad=False), "segment_ids": torch.Tensor(all_segment_ids, size=[num_examples, seq_length], dtype=torch.int32, requires_grad=False), "label_ids": torch.Tensor(all_label_ids, size=[num_examples], dtype=torch.int32, requires_grad=False) }) if is_training: d = d.repeat() d = d.shuffle(buffer_size=100) d = d.batch(batch_size=batch_size, drop_remainder=drop_remainder) return d return input_fn