Works for BERT

transformers/data/processors/squad.py

@@ -3,6 +3,7 @@ import collections
 import logging
 import os
 import json
+import numpy as np
 
 from ...tokenization_bert import BasicTokenizer, whitespace_tokenize
 from .utils import DataProcessor, InputExample, InputFeatures
@@ -13,19 +14,7 @@ if is_tf_available():
 
 logger = logging.getLogger(__name__)
 
-
-def squad_convert_examples_to_features(examples, tokenizer, max_seq_length,
-                                       doc_stride, max_query_length, is_training,
-                                       cls_token_at_end=False,
-                                       cls_token='[CLS]', sep_token='[SEP]', pad_token=0,
-                                       sequence_a_segment_id=0, sequence_b_segment_id=1,
-                                       cls_token_segment_id=0, pad_token_segment_id=0,
-                                       mask_padding_with_zero=True,
-                                       sequence_a_is_doc=False):
-    """Loads a data file into a list of `InputBatch`s."""
-
-    # Defining helper methods
-    def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer,
+def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer,
                         orig_answer_text):
     """Returns tokenized answer spans that better match the annotated answer."""
     tok_answer_text = " ".join(tokenizer.tokenize(orig_answer_text))
@@ -37,7 +26,8 @@ def squad_convert_examples_to_features(examples, tokenizer, max_seq_length,
                 return (new_start, new_end)
 
     return (input_start, input_end)
-    def _check_is_max_context(doc_spans, cur_span_index, position):
+
+def _check_is_max_context(doc_spans, cur_span_index, position):
     """Check if this is the 'max context' doc span for the token."""
     best_score = None
     best_span_index = None
@@ -56,25 +46,221 @@ def squad_convert_examples_to_features(examples, tokenizer, max_seq_length,
 
     return cur_span_index == best_span_index
 
+
+def _new_check_is_max_context(doc_spans, cur_span_index, position):
+    """Check if this is the 'max context' doc span for the token."""
+    # if len(doc_spans) == 1:
+        # return True
+    best_score = None
+    best_span_index = None
+    for (span_index, doc_span) in enumerate(doc_spans):
+        end = doc_span["start"] + doc_span["length"] - 1
+        if position < doc_span["start"]:
+            continue
+        if position > end:
+            continue
+        num_left_context = position - doc_span["start"]
+        num_right_context = end - position
+        score = min(num_left_context, num_right_context) + 0.01 * doc_span["length"]
+        if best_score is None or score > best_score:
+            best_score = score
+            best_span_index = span_index
+
+    return cur_span_index == best_span_index
+
+def _is_whitespace(c):
+    if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F:
+        return True
+    return False
+
+def squad_convert_examples_to_features(examples, tokenizer, max_seq_length,
+                                       doc_stride, max_query_length, is_training,
+                                       cls_token_at_end=True,
+                                       cls_token='[CLS]', sep_token='[SEP]', pad_token=0,
+                                       sequence_a_segment_id=0, sequence_b_segment_id=1,
+                                       cls_token_segment_id=0, pad_token_segment_id=0,
+                                       mask_padding_with_zero=True,
+                                       sequence_a_is_doc=False):
+    """Loads a data file into a list of `InputBatch`s."""
+
+    # Defining helper methods    
     unique_id = 1000000000
 
     features = []
+    new_features = []
     for (example_index, example) in enumerate(tqdm(examples)):
-        query_tokens = tokenizer.tokenize(example.question_text)
 
-        if len(query_tokens) > max_query_length:
-            query_tokens = query_tokens[0:max_query_length]
+        doc_tokens = []
+        char_to_word_offset = []
+        prev_is_whitespace = True
+
+        # Split on whitespace so that different tokens may be attributed to their original position.
+        for c in example.context_text:
+            if _is_whitespace(c):
+                prev_is_whitespace = True
+            else:
+                if prev_is_whitespace:
+                    doc_tokens.append(c)
+                else:
+                    doc_tokens[-1] += c
+                prev_is_whitespace = False
+            char_to_word_offset.append(len(doc_tokens) - 1)
+
+        if is_training:
+            # Get start and end position
+            answer_length = len(example.answer_text)
+            start_position = char_to_word_offset[example.start_position]
+            end_position = char_to_word_offset[example.start_position + answer_length - 1]
+
+            # If the answer cannot be found in the text, then skip this example.
+            actual_text = " ".join(doc_tokens[start_position:(end_position + 1)])
+            cleaned_answer_text = " ".join(whitespace_tokenize(example.answer_text))
+            if actual_text.find(cleaned_answer_text) == -1:
+                logger.warning("Could not find answer: '%s' vs. '%s'", actual_text, cleaned_answer_text)
+                continue
 
         tok_to_orig_index = []
         orig_to_tok_index = []
         all_doc_tokens = []
-        for (i, token) in enumerate(example.doc_tokens):
+        for (i, token) in enumerate(doc_tokens):
             orig_to_tok_index.append(len(all_doc_tokens))
             sub_tokens = tokenizer.tokenize(token)
             for sub_token in sub_tokens:
                 tok_to_orig_index.append(i)
                 all_doc_tokens.append(sub_token)
 
+        spans = []
+        
+        truncated_query = tokenizer.encode(example.question_text, add_special_tokens=False, max_length=max_query_length)
+        sequence_added_tokens = tokenizer.max_len - tokenizer.max_len_single_sentence 
+        sequence_pair_added_tokens = tokenizer.max_len - tokenizer.max_len_sentences_pair 
+
+        encoded_dict = tokenizer.encode_plus(
+            truncated_query, 
+            all_doc_tokens, 
+            max_length=max_seq_length, 
+            padding_strategy='right',
+            stride=max_seq_length - doc_stride - len(truncated_query) - sequence_pair_added_tokens,
+            return_overflowing_tokens=True, 
+            truncation_strategy='only_second'
+        )
+
+        ids = encoded_dict['input_ids']
+        print("Ids computes; position of the first padding", ids.index(tokenizer.pad_token_id) if tokenizer.pad_token_id in ids else None)
+        non_padded_ids = ids[:ids.index(tokenizer.pad_token_id)] if tokenizer.pad_token_id in ids else ids
+        paragraph_len = min(len(all_doc_tokens) - len(spans) * doc_stride, max_seq_length - len(truncated_query) - sequence_pair_added_tokens)
+        tokens = tokenizer.convert_ids_to_tokens(non_padded_ids)
+
+        token_to_orig_map = {}
+        for i in range(paragraph_len):
+            token_to_orig_map[len(truncated_query) + sequence_added_tokens + i] = tok_to_orig_index[0 + i]
+
+        encoded_dict["paragraph_len"] = paragraph_len
+        encoded_dict["tokens"] = tokens
+        encoded_dict["token_to_orig_map"] = token_to_orig_map
+        encoded_dict["truncated_query_with_special_tokens_length"] = len(truncated_query) + sequence_added_tokens
+        encoded_dict["token_is_max_context"] = {}
+        encoded_dict["start"] = 0
+        encoded_dict["length"] = paragraph_len
+
+        spans.append(encoded_dict)
+        print("YESSIR", len(spans) * doc_stride < len(all_doc_tokens), "overflowing_tokens" in encoded_dict)
+        while len(spans) * doc_stride < len(all_doc_tokens) and "overflowing_tokens" in encoded_dict:
+            
+            overflowing_tokens = encoded_dict['overflowing_tokens']
+
+            print("OVERFLOW", len(overflowing_tokens))
+
+            encoded_dict = tokenizer.encode_plus(
+                truncated_query, 
+                overflowing_tokens, 
+                max_length=max_seq_length, 
+                return_overflowing_tokens=True, 
+                padding_strategy='right',
+                stride=max_seq_length - doc_stride - len(truncated_query) - sequence_pair_added_tokens,
+                truncation_strategy='only_second'
+            )
+
+            ids = encoded_dict['input_ids']
+            print("Ids computes; position of the first padding", ids.index(tokenizer.pad_token_id) if tokenizer.pad_token_id in ids else None)
+
+            # Length of the document without the query
+            paragraph_len = min(len(all_doc_tokens) - len(spans) * doc_stride, max_seq_length - len(truncated_query) - sequence_pair_added_tokens)
+
+            non_padded_ids = encoded_dict['input_ids'][:encoded_dict['input_ids'].index(tokenizer.pad_token_id)]
+            tokens = tokenizer.convert_ids_to_tokens(non_padded_ids)
+
+            token_to_orig_map = {}
+            for i in range(paragraph_len):
+                token_to_orig_map[len(truncated_query) + sequence_added_tokens + i] = tok_to_orig_index[len(spans) * doc_stride + i]
+
+            encoded_dict["paragraph_len"] = paragraph_len
+            encoded_dict["tokens"] = tokens
+            encoded_dict["token_to_orig_map"] = token_to_orig_map
+            encoded_dict["truncated_query_with_special_tokens_length"] = len(truncated_query) + sequence_added_tokens
+            encoded_dict["token_is_max_context"] = {}
+            encoded_dict["start"] = len(spans) * doc_stride
+            encoded_dict["length"] = paragraph_len
+
+            # split_token_index = doc_span.start + i
+            # token_to_orig_map[len(tokens)] = tok_to_orig_index[split_token_index]
+
+            # is_max_context = _check_is_max_context(doc_spans, doc_span_index,
+            #                                         split_token_index)
+            # token_is_max_context[len(tokens)] = is_max_context
+            # tokens.append(all_doc_tokens[split_token_index])
+
+            spans.append(encoded_dict)
+
+        for doc_span_index in range(len(spans)):
+            for j in range(spans[doc_span_index]["paragraph_len"]):
+                is_max_context = _new_check_is_max_context(spans, doc_span_index, doc_span_index * doc_stride + j)
+                index = spans[doc_span_index]["truncated_query_with_special_tokens_length"] + j
+                spans[doc_span_index]["token_is_max_context"][index] = is_max_context
+
+        print("new span", len(spans))
+        for span in spans:
+            # Identify the position of the CLS token
+            cls_index = span['input_ids'].index(tokenizer.cls_token_id)
+
+            # p_mask: mask with 1 for token than cannot be in the answer (0 for token which can be in an answer)
+            # Original TF implem also keep the classification token (set to 0) (not sure why...)
+            p_mask = np.array(span['token_type_ids'])
+
+            # Convert all SEP indices to '0' before inversion
+            p_mask[np.where(np.array(span["input_ids"]) == tokenizer.sep_token_id)[0]] = 0
+
+            # Limit positive values to one
+            p_mask = 1 - np.minimum(p_mask, 1)
+
+            # Set the CLS index to '0'
+            p_mask[cls_index] = 0
+
+            print("new features length", len(new_features))
+
+            new_features.append(NewSquadFeatures(
+                span['input_ids'],
+                span['attention_mask'],
+                span['token_type_ids'],
+                cls_index,
+                p_mask.tolist(),
+
+                example_index=example_index,
+                unique_id=unique_id,
+                paragraph_len=span['paragraph_len'],
+                token_is_max_context=span["token_is_max_context"],
+                tokens=span["tokens"],
+                token_to_orig_map=span["token_to_orig_map"]
+            ))
+
+            unique_id += 1
+
+        # tokenize ...
+        query_tokens = tokenizer.tokenize(example.question_text)
+
+        if len(query_tokens) > max_query_length:
+            query_tokens = query_tokens[0:max_query_length]
+
         tok_start_position = None
         tok_end_position = None
         if is_training and example.is_impossible:
@@ -82,7 +268,7 @@ def squad_convert_examples_to_features(examples, tokenizer, max_seq_length,
             tok_end_position = -1
         if is_training and not example.is_impossible:
             tok_start_position = orig_to_tok_index[example.start_position]
-            if example.end_position < len(example.doc_tokens) - 1:
+            if example.end_position < len(doc_tokens) - 1:
                 tok_end_position = orig_to_tok_index[example.end_position + 1] - 1
             else:
                 tok_end_position = len(all_doc_tokens) - 1
@@ -101,14 +287,19 @@ def squad_convert_examples_to_features(examples, tokenizer, max_seq_length,
         doc_spans = []
         start_offset = 0
         while start_offset < len(all_doc_tokens):
+            print("OLD DOC CREATION BEGIN", start_offset, len(all_doc_tokens))
             length = len(all_doc_tokens) - start_offset
             if length > max_tokens_for_doc:
                 length = max_tokens_for_doc
             doc_spans.append(_DocSpan(start=start_offset, length=length))
             if start_offset + length == len(all_doc_tokens):
+                print("Done with this doc span, breaking out.", start_offset, length)
                 break
+            print("CHOOSING OFFSET", length, doc_stride)
             start_offset += min(length, doc_stride)
+            print("OLD DOC CREATION END", start_offset)
 
+        print("old span", len(doc_spans))
         for (doc_span_index, doc_span) in enumerate(doc_spans):
             tokens = []
             token_to_orig_map = {}
@@ -183,18 +374,20 @@ def squad_convert_examples_to_features(examples, tokenizer, max_seq_length,
             # tokens are attended to.
             input_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)
 
+
+            
             # Zero-pad up to the sequence length.
             while len(input_ids) < max_seq_length:
                 input_ids.append(pad_token)
                 input_mask.append(0 if mask_padding_with_zero else 1)
                 segment_ids.append(pad_token_segment_id)
                 p_mask.append(1)
-
+            print("[OLD] Ids computed; position of the first padding", input_ids.index(tokenizer.pad_token_id) if tokenizer.pad_token_id in input_ids else None)
             assert len(input_ids) == max_seq_length
             assert len(input_mask) == max_seq_length
             assert len(segment_ids) == max_seq_length
 
-            span_is_impossible = example.is_impossible
+            span_is_impossible = example.is_impossible if hasattr(example, "is_impossible") else False
             start_position = None
             end_position = None
             if is_training and not span_is_impossible:
@@ -222,31 +415,32 @@ def squad_convert_examples_to_features(examples, tokenizer, max_seq_length,
                 start_position = cls_index
                 end_position = cls_index
 
-            if example_index < 20:
-                logger.info("*** Example ***")
-                logger.info("unique_id: %s" % (unique_id))
-                logger.info("example_index: %s" % (example_index))
-                logger.info("doc_span_index: %s" % (doc_span_index))
-                logger.info("tokens: %s" % " ".join(tokens))
-                logger.info("token_to_orig_map: %s" % " ".join([
-                    "%d:%d" % (x, y) for (x, y) in token_to_orig_map.items()]))
-                logger.info("token_is_max_context: %s" % " ".join([
-                    "%d:%s" % (x, y) for (x, y) in token_is_max_context.items()
-                ]))
-                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]))
-                if is_training and span_is_impossible:
-                    logger.info("impossible example")
-                if is_training and not span_is_impossible:
-                    answer_text = " ".join(tokens[start_position:(end_position + 1)])
-                    logger.info("start_position: %d" % (start_position))
-                    logger.info("end_position: %d" % (end_position))
-                    logger.info(
-                        "answer: %s" % (answer_text))
-
+            # if example_index < 20:
+            #     logger.info("*** Example ***")
+            #     logger.info("unique_id: %s" % (unique_id))
+            #     logger.info("example_index: %s" % (example_index))
+            #     logger.info("doc_span_index: %s" % (doc_span_index))
+            #     logger.info("tokens: %s" % str(tokens))
+            #     logger.info("token_to_orig_map: %s" % " ".join([
+            #         "%d:%d" % (x, y) for (x, y) in token_to_orig_map.items()]))
+            #     logger.info("token_is_max_context: %s" % " ".join([
+            #         "%d:%s" % (x, y) for (x, y) in token_is_max_context.items()
+            #     ]))
+            #     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]))
+            #     if is_training and span_is_impossible:
+            #         logger.info("impossible example")
+            #     if is_training and not span_is_impossible:
+            #         answer_text = " ".join(tokens[start_position:(end_position + 1)])
+            #         logger.info("start_position: %d" % (start_position))
+            #         logger.info("end_position: %d" % (end_position))
+            #         logger.info(
+            #             "answer: %s" % (answer_text))
+
+            print("features length", len(features))
             features.append(
                 SquadFeatures(
                     unique_id=unique_id,
@@ -266,7 +460,48 @@ def squad_convert_examples_to_features(examples, tokenizer, max_seq_length,
                     is_impossible=span_is_impossible))
             unique_id += 1
 
-    return features
+        assert len(features) == len(new_features)
+
+    assert len(features) == len(new_features)
+    for i in range(len(features)):
+        print(i)
+        feature, new_feature = features[i], new_features[i]
+        
+        input_ids = feature.input_ids
+        input_mask = feature.input_mask
+        segment_ids = feature.segment_ids
+        cls_index = feature.cls_index
+        p_mask = feature.p_mask
+        example_index = feature.example_index
+        paragraph_len = feature.paragraph_len
+        token_is_max_context = feature.token_is_max_context
+        tokens = feature.tokens
+        token_to_orig_map = feature.token_to_orig_map
+              
+        new_input_ids = new_feature.input_ids
+        new_input_mask = new_feature.attention_mask
+        new_segment_ids = new_feature.token_type_ids
+        new_cls_index = new_feature.cls_index
+        new_p_mask = new_feature.p_mask
+        new_example_index = new_feature.example_index
+        new_paragraph_len = new_feature.paragraph_len
+        new_token_is_max_context = new_feature.token_is_max_context
+        new_tokens = new_feature.tokens
+        new_token_to_orig_map = new_feature.token_to_orig_map
+
+        assert input_ids == new_input_ids
+        assert input_mask == new_input_mask
+        assert segment_ids == new_segment_ids
+        assert cls_index == new_cls_index
+        assert p_mask == new_p_mask
+        assert example_index == new_example_index
+        assert paragraph_len == new_paragraph_len
+        assert token_is_max_context == new_token_is_max_context
+        assert tokens == new_tokens
+        assert token_to_orig_map == new_token_to_orig_map
+
+
+    return new_features
 
 
 def read_squad_examples(input_file, is_training, version_2_with_negative):
@@ -347,6 +582,124 @@ def read_squad_examples(input_file, is_training, version_2_with_negative):
     return examples
 
 
+class SquadV1Processor(DataProcessor):
+    """Processor for the SQuAD data set."""
+
+    def get_example_from_tensor_dict(self, tensor_dict):
+        """See base class."""
+        return NewSquadExample(
+            tensor_dict['id'].numpy(),
+            tensor_dict['question'].numpy().decode('utf-8'),
+            tensor_dict['context'].numpy().decode('utf-8'),
+            tensor_dict['answers']['text'].numpy().decode('utf-8'),
+            tensor_dict['answers']['answers_start'].numpy().decode('utf-8'),
+            tensor_dict['title'].numpy().decode('utf-8')
+        )
+
+    def get_train_examples(self, data_dir):
+        """See base class."""
+        with open(os.path.join(data_dir, "train-v1.1.json"), "r", encoding='utf-8') as reader:
+            input_data = json.load(reader)["data"]
+        return self._create_examples(input_data, "train")
+
+    def get_dev_examples(self, data_dir):
+        """See base class."""
+        with open(os.path.join(data_dir, "dev-v1.1.json"), "r", encoding='utf-8') as reader:
+            input_data = json.load(reader)["data"]
+        return self._create_examples(input_data, "dev")
+
+    def get_labels(self):
+        """See base class."""
+        return ["0", "1"]
+
+    def _create_examples(self, input_data, set_type):
+        """Creates examples for the training and dev sets."""
+        
+        is_training = set_type == "train"
+        examples = []
+        for entry in input_data:
+            title = entry['title']
+            for paragraph in entry["paragraphs"]:
+                context_text = paragraph["context"]
+                for qa in paragraph["qas"]:
+                    qas_id = qa["id"]
+                    question_text = qa["question"]
+                    start_position = None
+                    answer_text = None
+                    if is_training:
+                        if (len(qa["answers"]) != 1):
+                            raise ValueError(
+                                "For training, each question should have exactly 1 answer.")
+                        answer = qa["answers"][0]
+                        answer_text = answer['text']
+                        start_position = answer['answer_start']
+
+                    example = NewSquadExample(
+                        qas_id=qas_id,
+                        question_text=question_text,
+                        context_text=context_text,
+                        answer_text=answer_text,
+                        start_position=start_position,
+                        title=title
+                    )
+                    examples.append(example)
+        return examples
+        
+
+
+class NewSquadExample(object):
+    """
+    A single training/test example for the Squad dataset, as loaded from disk.
+    """
+
+    def __init__(self,
+                 qas_id,
+                 question_text,
+                 context_text,
+                 answer_text,
+                 start_position,
+                 title):
+        self.qas_id = qas_id
+        self.question_text = question_text
+        self.context_text = context_text
+        self.answer_text = answer_text
+        self.start_position = start_position
+        self.title = title
+
+
+class NewSquadFeatures(object):
+    """
+    Single squad example features to be fed to a model.
+    Those features are model-specific.
+    """
+
+    def __init__(self,
+                 input_ids,
+                 attention_mask,
+                 token_type_ids,
+                 cls_index,
+                 p_mask,
+                 
+                 example_index,
+                 unique_id,
+                 paragraph_len,
+                 token_is_max_context,
+                 tokens,
+                 token_to_orig_map
+        ):
+        self.input_ids = input_ids 
+        self.attention_mask = attention_mask
+        self.token_type_ids = token_type_ids
+        self.cls_index = cls_index
+        self.p_mask = p_mask
+
+        self.example_index = example_index
+        self.unique_id = unique_id
+        self.paragraph_len = paragraph_len
+        self.token_is_max_context = token_is_max_context
+        self.tokens = tokens
+        self.token_to_orig_map = token_to_orig_map
+
 class SquadExample(object):
     """
     A single training/test example for the Squad dataset.
@@ -423,18 +776,22 @@ class SquadFeatures(object):
         self.is_impossible = is_impossible
 
     def __eq__(self, other):
-        return self.cls_index == other.cls_index and \
-                self.doc_span_index == other.doc_span_index and \
-                self.end_position == other.end_position and \
-                self.example_index == other.example_index and \
+        print(self.example_index == other.example_index)
+        print(self.input_ids == other.input_ids)
+        print(self.input_mask == other.attention_mask)
+        print(self.p_mask == other.p_mask)
+        print(self.paragraph_len == other.paragraph_len)
+        print(self.segment_ids == other.token_type_ids)
+        print(self.token_is_max_context == other.token_is_max_context)
+        print(self.token_to_orig_map == other.token_to_orig_map)
+        print(self.tokens == other.tokens)
+
+        return self.example_index == other.example_index and \
                 self.input_ids == other.input_ids and \
-                self.input_mask == other.input_mask and \
-                self.is_impossible == other.is_impossible and \
+                self.input_mask == other.attention_mask and \
                 self.p_mask == other.p_mask and \
                 self.paragraph_len == other.paragraph_len and \
-                self.segment_ids == other.segment_ids and \
-                self.start_position == other.start_position and \
+                self.segment_ids == other.token_type_ids and \
                 self.token_is_max_context == other.token_is_max_context and \
                 self.token_to_orig_map == other.token_to_orig_map and \
-                self.tokens == other.tokens and \
-                self.unique_id == other.unique_id
\ No newline at end of file
+                self.tokens == other.tokens
\ No newline at end of file