ADD file via upload

examples/nlp/bert/processBertData.py

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+from datasets import load_dataset
+import random
+import hetu
+import os
+import numpy as np
+
+''' Usage example:
+    In dir Hetu/examples/nlp/bert/: python processBertData.py
+'''
+
+# https://the-eye.eu/public/AI/pile_preliminary_components/books1.tar.gz
+
+
+class TrainingInstance(object):
+  """A single training instance (sentence pair)."""
+
+  def __init__(self, tokens, segment_ids, masked_lm_positions, masked_lm_labels,
+               is_random_next):
+    self.tokens = tokens
+    self.segment_ids = segment_ids
+    self.is_random_next = is_random_next
+    self.masked_lm_positions = masked_lm_positions
+    self.masked_lm_labels = masked_lm_labels
+
+  def __str__(self):
+    s = ""
+    s += "tokens: %s\n" % (" ".join(
+        [str(x) for x in self.tokens]))
+    s += "segment_ids: %s\n" % (" ".join([str(x) for x in self.segment_ids]))
+    s += "is_random_next: %s\n" % self.is_random_next
+    s += "masked_lm_positions: %s\n" % (" ".join(
+        [str(x) for x in self.masked_lm_positions]))
+    s += "masked_lm_labels: %s\n" % (" ".join(
+        [str(x) for x in self.masked_lm_labels]))
+    s += "\n"
+    return s
+
+  def __repr__(self):
+    return self.__str__()
+
+
+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):
+        if token == "[CLS]" or token == "[SEP]":
+            continue
+        cand_indexes.append(i)
+    rng.shuffle(cand_indexes)
+    output_tokens = list(tokens)
+    num_to_predict = min(max_predictions_per_seq,
+                         max(1, int(round(len(tokens) * masked_lm_prob))))
+    masked_lms = []
+    for index in cand_indexes:
+        if len(masked_lms) >= num_to_predict:
+            break
+        masked_token = None
+        # replace with [MASK] at 80%.
+        if rng.random() < 0.8:
+            masked_token = "[MASK]"
+        else:
+            # keep original at 10%.
+            if rng.random() < 0.5:
+                masked_token = tokens[index]
+            #  replace with random word at 10%.
+            else:
+                masked_token = vocab_words[rng.randint(0, len(vocab_words) - 1)]
+        output_tokens[index] = masked_token
+        masked_lms.append([index, tokens[index]])
+
+    masked_lms.sort(key = lambda x: x[0])
+    masked_lm_positions = []
+    masked_lm_labels = []
+
+    for p in masked_lms:
+        masked_lm_positions.append(p[0])
+        masked_lm_labels.append(p[1])
+
+    return (output_tokens, masked_lm_positions, masked_lm_labels)
+
+
+def create_data_from_document(all_document,  doc_id, max_seq_length, short_seq_prob, masked_lm_prob, max_predictions_per_seq, vocab_words, rng):
+    """ Create Training example for input document """
+    document = all_document[doc_id]
+    max_num_tokens = max_seq_length - 3 # [CLS], [SEP], [SEP]
+    target_seq_length = max_num_tokens
+    # generate short sequence at the probility of short_seq_prob
+    # In order to minimize the mismatch between pre-training and fine-tuning.
+    if rng.random() < short_seq_prob:
+        target_seq_length = rng.randint(2, max_num_tokens)
+    instances = []
+    current_chunk = []
+    current_length = 0
+    i = 0
+    while i < len(document):
+        segment = document[i]
+        current_chunk.append(segment)
+        current_length += len(segment)
+        if i == len(document) - 1 or current_length >= target_seq_length:
+            if current_chunk:
+                # create sentence A
+                a_end = 1
+                if len(current_chunk) >= 2:
+                    a_end = rng.randint(1, len(current_chunk) - 1)
+                tokens_a = []
+                for j in range(a_end):
+                    tokens_a.extend([current_chunk[j]])
+                tokens_b = []
+                # Random next
+                is_random_next = False
+                if len(current_chunk) == 1 or rng.random() < 0.5:
+                    is_random_next = True
+                    target_b_length = target_seq_length - len(tokens_a)
+                    for _ in range(10):
+                        random_document_index = rng.randint(0, len(all_document) - 1)
+                        if random_document_index != doc_id:
+                            break
+                    #If picked random document is the same as the current document
+                    if random_document_index == doc_id:
+                        is_random_next = False
+                    random_document = all_document[random_document_index]
+                    random_start = rng.randint(0, len(random_document) - 1)
+                    for j in range(random_start, len(random_document)):
+                        tokens_b.extend([random_document[j]])
+                        if len(tokens_b) >= target_b_length:
+                            break
+                    # We didn't actually use these segments so we "put them back" so
+                    # they don't go to waste.
+                    num_unused_segments = len(current_chunk) - a_end
+                    i -= num_unused_segments
+                # Actual next
+                else:
+                    is_random_next = False
+                    for j in range(a_end, len(current_chunk)):
+                        tokens_b.extend([current_chunk[j]])
+                truncate_seq_pair(tokens_a, tokens_b, max_num_tokens, rng)
+                assert len(tokens_a) >= 1
+                assert len(tokens_b) >= 1
+
+                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)
+
+                for token in tokens_b:
+                    tokens.append(token)
+                    segment_ids.append(1)
+                tokens.append("[SEP]")
+                segment_ids.append(1)
+
+                (tokens, masked_lm_positions, masked_lm_labels) = create_masked_lm_predictions(
+                    tokens, masked_lm_prob, max_predictions_per_seq, vocab_words, rng)
+                
+                instance = TrainingInstance(
+                    tokens=tokens,
+                    segment_ids=segment_ids,
+                    is_random_next=is_random_next,
+                    masked_lm_positions=masked_lm_positions,
+                    masked_lm_labels=masked_lm_labels)
+                instances.append(instance)
+            current_chunk = []
+            current_length = 0
+        i += 1
+
+    return instances
+    
+def convert_instances_to_data(instances, tokenizer, max_seq_length):
+    
+    num_instances = len(instances)
+    input_ids_list = np.zeros([num_instances, max_seq_length], dtype="int32")
+    input_mask_list = np.zeros([num_instances, max_seq_length], dtype="int32")
+    segment_ids_list = np.zeros([num_instances, max_seq_length], dtype="int32")
+    masked_lm_labels = np.full([num_instances, max_seq_length],-1, dtype="int32")
+    next_sentence_labels_list = np.zeros(num_instances, dtype="int32")
+
+    for (idx, instance) in enumerate(instances):
+        input_ids = tokenizer.convert_tokens_to_ids(instance.tokens)
+        input_mask = [1] * len(input_ids)
+        segment_ids = list(instance.segment_ids)
+        assert len(input_ids) <= max_seq_length
+
+        padding_zero_list = [0]*int(max_seq_length - len(input_ids))
+        input_ids += padding_zero_list
+        input_mask += padding_zero_list
+        segment_ids += padding_zero_list
+
+        assert len(input_ids) == max_seq_length
+        assert len(input_mask) == max_seq_length
+        assert len(segment_ids) == max_seq_length
+
+        masked_lm_positions = list(instance.masked_lm_positions)
+        masked_lm_ids = tokenizer.convert_tokens_to_ids(instance.masked_lm_labels)
+
+        input_ids_list[idx][:] = input_ids
+        input_mask_list[idx][:] = input_mask
+        segment_ids_list[idx][:] = segment_ids
+        masked_lm_labels[idx][masked_lm_positions] = masked_lm_ids
+        next_sentence_labels_list[idx] = 1 if instance.is_random_next else 0
+
+    return input_ids_list, input_mask_list, segment_ids_list, masked_lm_labels, next_sentence_labels_list
+
+def create_pretrain_data(dataset, tokenizer, max_seq_length, short_seq_prob, masked_lm_prob, max_predictions_per_seq, rng):
+    documents,  all_data = [], [[],[],[],[],[]]
+    vocab_words = list(tokenizer.vocab.keys())
+
+    save_path='./preprocessed_data/bookcorpus/'
+    if not os.path.exists(save_path):
+        os.makedirs(save_path)
+
+    for i in range(dataset['train'].shape[0]):
+        tokens = tokenizer.tokenize(dataset['train'][i]['text'])
+        documents.append(tokens)
+        instance = create_data_from_document(documents, i,\
+                            max_seq_length, short_seq_prob, masked_lm_prob, 
+                            max_predictions_per_seq, vocab_words, rng)
+        data = convert_instances_to_data(instance, tokenizer, max_seq_length)
+        print(i, len(tokens), len(instance))
+        for j in range(5):
+            all_data[j].append(data[j]) 
+
+        save_gap=200
+        if (i+1)%save_gap==0 and i:
+            input_ids_list, input_mask_list, segment_ids_list, masked_lm_labels, next_sentence_labels_list = [np.concatenate(all_data[j],axis=0) for j in range(5)]
+            print('Saving data from %d to %d: doc_num = %d, input_ids_shape ='%(i+1-save_gap,i, i+1), input_ids_list.shape)
+            save_data(input_ids_list, input_mask_list, segment_ids_list, masked_lm_labels, next_sentence_labels_list, name='_%d_%d'%(i+1-save_gap,i))
+            all_data = [[],[],[],[],[]]
+        if i == dataset['train'].shape[0]-1:
+            input_ids_list, input_mask_list, segment_ids_list, masked_lm_labels, next_sentence_labels_list = [np.concatenate(all_data[j],axis=0) for j in range(5)]
+            print('Saving data from %d to %d: doc_num = %d, input_ids_shape ='%(save_gap*int(i/save_gap),i, i+1), input_ids_list.shape)
+            save_data(input_ids_list, input_mask_list, segment_ids_list, masked_lm_labels, next_sentence_labels_list, name='_%d_%d'%(save_gap*int(i/save_gap),i))
+
+def save_data(input_ids_list, input_mask_list, segment_ids_list, masked_lm_labels, next_sentence_labels_list,name=''):
+    save_path='./preprocessed_data/bookcorpus/'
+    np.save(save_path+'input_ids'+name,np.array(input_ids_list))
+    np.save(save_path+'token_type_ids'+name,np.array(segment_ids_list))
+    np.save(save_path+'attention_mask'+name,np.array(input_mask_list))
+    np.save(save_path+'masked_lm_labels'+name,np.array(masked_lm_labels))
+    np.save(save_path+'next_sentence_label'+name,np.array(next_sentence_labels_list))
+
+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:
+            break
+
+        trunc_tokens = tokens_a if len(tokens_a) > len(tokens_b) else tokens_b
+        assert len(trunc_tokens) >= 1
+
+        #add more randomness and avoid biases.
+        if rng.random() < 0.5:
+            del trunc_tokens[0]
+        else:
+            trunc_tokens.pop()
+
+def show_dataset_detail(dataset):
+    print(dataset.shape)
+    print(dataset.column_names)
+    print(dataset['train'].features)
+    print(dataset['train'][0]['text'])
+
+if __name__ == "__main__":
+    max_seq_length = 512
+    do_lower_case = True
+    short_seq_prob = 0.1
+    masked_lm_prob = 0.15
+    max_predictions_per_seq = 20
+    
+    vocab_path = "./datasets/bert-base-uncased-vocab.txt"
+    dataset = load_dataset('../bookcorpus', cache_dir = "./cached_data")
+    
+    print("total number of documents {} ".format(dataset['train'].shape[0]))
+    random_seed = 123
+    rng = random.Random(random_seed)
+    tokenizer = hetu.BertTokenizer(vocab_file=vocab_path, do_lower_case = do_lower_case)
+
+    print("vocab_size =",len(tokenizer.vocab))
+    print("max_seq_len =", max_seq_length)
+    
+    create_pretrain_data(dataset, tokenizer, max_seq_length, short_seq_prob, masked_lm_prob, max_predictions_per_seq, rng)
+
+
+
+
+