updating squad for compatibility with XLNet

examples/run_squad.py

@@ -41,7 +41,9 @@ from pytorch_transformers import (WEIGHTS_NAME, BertConfig,
 
 from pytorch_transformers import AdamW, WarmupLinearSchedule
 
-from utils_squad import read_squad_examples, convert_examples_to_features, RawResult, write_predictions
+from utils_squad import (read_squad_examples, convert_examples_to_features,
+                         RawResult, write_predictions,
+                         RawResultExtended, write_predictions_extended)
 
 # The follwing import is the official SQuAD evaluation script (2.0).
 # You can remove it from the dependencies if you are using this script outside of the library
@@ -66,6 +68,8 @@ def set_seed(args):
     if args.n_gpu > 0:
         torch.cuda.manual_seed_all(args.seed)
 
+def to_list(tensor):
+    return tensor.detach().cpu().tolist()
 
 def train(args, train_dataset, model, tokenizer):
     """ Train the model """
@@ -118,10 +122,13 @@ def train(args, train_dataset, model, tokenizer):
             model.train()
             batch = tuple(t.to(args.device) for t in batch)
             inputs = {'input_ids':       batch[0],
-                      'token_type_ids':  batch[1] if args.model_type in ['bert', 'xlnet'] else None,  # XLM don't use segment_ids
+                      'token_type_ids':  None if args.model_type == 'xlm' else batch[1],  # XLM don't use segment_ids
                       'attention_mask':  batch[2],
                       'start_positions': batch[3],
                       'end_positions':   batch[4]}
+            if args.model_type in ['xlnet', 'xlm']:
+                inputs.update({'cls_index': batch[5],
+                               'p_mask':    batch[6]})
             ouputs = model(**inputs)
             loss = ouputs[0]  # model outputs are always tuple in pytorch-transformers (see doc)
 
@@ -197,30 +204,49 @@ def evaluate(args, model, tokenizer, prefix=""):
     for batch in tqdm(eval_dataloader, desc="Evaluating"):
         model.eval()
         batch = tuple(t.to(args.device) for t in batch)
-        example_indices = batch[3]
         with torch.no_grad():
             inputs = {'input_ids':      batch[0],
-                        'token_type_ids': batch[1] if args.model_type in ['bert', 'xlnet'] else None,  # XLM don't use segment_ids
+                      'token_type_ids': None if args.model_type == 'xlm' else batch[1],  # XLM don't use segment_ids
                       'attention_mask': batch[2]}
+            example_indices = batch[3]
+            if args.model_type in ['xlnet', 'xlm']:
+                inputs.update({'cls_index': batch[4],
+                               'p_mask':    batch[5]})
             outputs = model(**inputs)
             batch_start_logits, batch_end_logits = outputs[:2]
 
         for i, example_index in enumerate(example_indices):
-            start_logits = batch_start_logits[i].detach().cpu().tolist()
-            end_logits = batch_end_logits[i].detach().cpu().tolist()
             eval_feature = features[example_index.item()]
             unique_id = int(eval_feature.unique_id)
-            all_results.append(RawResult(unique_id=unique_id,
+            if args.model_type in ['xlnet', 'xlm']:
-                                         start_logits=start_logits,
+                # XLNet uses a more complex post-processing procedure
-                                         end_logits=end_logits))
+                result = RawResultExtended(unique_id            = unique_id,
+                                           start_top_log_probs  = to_list(outputs[0][i]),
+                                           start_top_index      = to_list(outputs[1][i]),
+                                           end_top_log_probs    = to_list(outputs[2][i]),
+                                           end_top_index        = to_list(outputs[3][i]),
+                                           cls_logits           = to_list(outputs[4][i]))
+            else:
+                result = RawResult(unique_id    = unique_id,
+                                   start_logits = to_list(outputs[0][i]),
+                                   end_logits   = to_list(outputs[1][i]))
+            all_results.append(result)
 
     # Compute predictions
     output_prediction_file = os.path.join(args.output_dir, "predictions_{}.json".format(prefix))
     output_nbest_file = os.path.join(args.output_dir, "nbest_predictions_{}.json".format(prefix))
     output_null_log_odds_file = os.path.join(args.output_dir, "null_odds_{}.json".format(prefix))
-    write_predictions(examples, features, all_results, args.n_best_size, args.max_answer_length,
+
-                      args.do_lower_case, output_prediction_file, output_nbest_file,
+    if args.model_type in ['xlnet', 'xlm']:
-                      output_null_log_odds_file, args.verbose_logging,
+        # XLNet uses a more complex post-processing procedure
+        write_predictions_extended(examples, features, all_results, args.n_best_size,
+                        args.max_answer_length, output_prediction_file,
+                        output_nbest_file, output_null_log_odds_file, args.predict_file,
+                        args.start_n_top, args.end_n_top, args.version_2_with_negative)
+    else:
+        write_predictions(examples, features, all_results, args.n_best_size,
+                        args.max_answer_length, args.do_lower_case, output_prediction_file,
+                        output_nbest_file, output_null_log_odds_file, args.verbose_logging,
                         args.version_2_with_negative, args.null_score_diff_threshold)
 
     # Evaluate with the official SQuAD script
@@ -260,13 +286,18 @@ def load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=Fal
     all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
     all_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long)
     all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long)
+    all_cls_index = torch.tensor([f.cls_index for f in features], dtype=torch.long)
+    all_p_mask = torch.tensor([f.p_mask for f in features], dtype=torch.float)
     if evaluate:
         all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long)
-        dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_example_index)
+        dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids,
+                                all_example_index, all_cls_index, all_p_mask)
     else:
         all_start_positions = torch.tensor([f.start_position for f in features], dtype=torch.long)
         all_end_positions = torch.tensor([f.end_position for f in features], dtype=torch.long)
-        dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_start_positions, all_end_positions)
+        dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids,
+                                all_start_positions, all_end_positions,
+                                all_cls_index, all_p_mask)
 
     if output_examples:
         return dataset, examples, features

examples/utils_squad.py

@@ -26,6 +26,9 @@ from io import open
 
 from pytorch_transformers.tokenization_bert import BasicTokenizer, whitespace_tokenize
 
+# Required by XLNet evaluation method to compute optimal threshold (see write_predictions_extended() method)
+from utils_squad_evaluate import find_all_best_thresh_v2, make_qid_to_has_ans, get_raw_scores
+
 logger = logging.getLogger(__name__)
 
 
@@ -82,6 +85,8 @@ class InputFeatures(object):
                  input_ids,
                  input_mask,
                  segment_ids,
+                 cls_index,
+                 p_mask,
                  start_position=None,
                  end_position=None,
                  is_impossible=None):
@@ -94,6 +99,8 @@ class InputFeatures(object):
         self.input_ids = input_ids
         self.input_mask = input_mask
         self.segment_ids = segment_ids
+        self.cls_index = cls_index
+        self.p_mask = p_mask
         self.start_position = start_position
         self.end_position = end_position
         self.is_impossible = is_impossible
@@ -178,13 +185,25 @@ def read_squad_examples(input_file, is_training, version_2_with_negative):
 
 
 def convert_examples_to_features(examples, tokenizer, max_seq_length,
-                                 doc_stride, max_query_length, is_training):
+                                 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):
     """Loads a data file into a list of `InputBatch`s."""
 
     unique_id = 1000000000
+    # cnt_pos, cnt_neg = 0, 0
+    # max_N, max_M = 1024, 1024
+    # f = np.zeros((max_N, max_M), dtype=np.float32)
 
     features = []
     for (example_index, example) in enumerate(examples):
+
+        # if example_index % 100 == 0:
+        #     logger.info('Converting %s/%s pos %s neg %s', example_index, len(examples), cnt_pos, cnt_neg)
+
         query_tokens = tokenizer.tokenize(example.question_text)
 
         if len(query_tokens) > max_query_length:
@@ -239,14 +258,30 @@ def convert_examples_to_features(examples, tokenizer, max_seq_length,
             token_to_orig_map = {}
             token_is_max_context = {}
             segment_ids = []
-            tokens.append("[CLS]")
+
-            segment_ids.append(0)
+            # 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 = []
+
+            # CLS token at the beginning
+            if not cls_token_at_end:
+                tokens.append(cls_token)
+                segment_ids.append(cls_token_segment_id)
+                p_mask.append(0)
+                cls_index = 0
+
+            # Query
             for token in query_tokens:
                 tokens.append(token)
-                segment_ids.append(0)
+                segment_ids.append(sequence_a_segment_id)
-            tokens.append("[SEP]")
+                p_mask.append(1)
-            segment_ids.append(0)
 
+            # SEP token
+            tokens.append(sep_token)
+            segment_ids.append(sequence_a_segment_id)
+            p_mask.append(1)
+
+            # Paragraph
             for i in range(doc_span.length):
                 split_token_index = doc_span.start + i
                 token_to_orig_map[len(tokens)] = tok_to_orig_index[split_token_index]
@@ -255,29 +290,42 @@ def convert_examples_to_features(examples, tokenizer, max_seq_length,
                                                        split_token_index)
                 token_is_max_context[len(tokens)] = is_max_context
                 tokens.append(all_doc_tokens[split_token_index])
-                segment_ids.append(1)
+                segment_ids.append(sequence_b_segment_id)
-            tokens.append("[SEP]")
+                p_mask.append(0)
-            segment_ids.append(1)
+
+            # SEP token
+            tokens.append(sep_token)
+            segment_ids.append(sequence_b_segment_id)
+            p_mask.append(1)
+
+            # CLS token at the end
+            if cls_token_at_end:
+                tokens.append(cls_token)
+                segment_ids.append(cls_token_segment_id)
+                p_mask.append(0)
+                cls_index = len(tokens) - 1  # Index of classification token
 
             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)
+            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(0)
+                input_ids.append(pad_token)
-                input_mask.append(0)
+                input_mask.append(0 if mask_padding_with_zero else 1)
-                segment_ids.append(0)
+                segment_ids.append(pad_token_segment_id)
+                p_mask.append(1)
 
             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
             start_position = None
             end_position = None
-            if is_training and not example.is_impossible:
+            if is_training and not span_is_impossible:
                 # For training, if our document chunk does not contain an annotation
                 # we throw it out, since there is nothing to predict.
                 doc_start = doc_span.start
@@ -289,13 +337,16 @@ def convert_examples_to_features(examples, tokenizer, max_seq_length,
                 if out_of_span:
                     start_position = 0
                     end_position = 0
+                    span_is_impossible = True
                 else:
                     doc_offset = len(query_tokens) + 2
                     start_position = tok_start_position - doc_start + doc_offset
                     end_position = tok_end_position - doc_start + doc_offset
-            if is_training and example.is_impossible:
+
-                start_position = 0
+            if is_training and span_is_impossible:
-                end_position = 0
+                start_position = cls_index
+                end_position = cls_index
+
             if example_index < 20:
                 logger.info("*** Example ***")
                 logger.info("unique_id: %s" % (unique_id))
@@ -312,9 +363,9 @@ def convert_examples_to_features(examples, tokenizer, max_seq_length,
                     "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 example.is_impossible:
+                if is_training and span_is_impossible:
                     logger.info("impossible example")
-                if is_training and not example.is_impossible:
+                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))
@@ -332,9 +383,11 @@ def convert_examples_to_features(examples, tokenizer, max_seq_length,
                     input_ids=input_ids,
                     input_mask=input_mask,
                     segment_ids=segment_ids,
+                    cls_index=cls_index,
+                    p_mask=p_mask,
                     start_position=start_position,
                     end_position=end_position,
-                    is_impossible=example.is_impossible))
+                    is_impossible=span_is_impossible))
             unique_id += 1
 
     return features
@@ -417,7 +470,6 @@ def _check_is_max_context(doc_spans, cur_span_index, position):
 RawResult = collections.namedtuple("RawResult",
                                    ["unique_id", "start_logits", "end_logits"])
 
-
 def write_predictions(all_examples, all_features, all_results, n_best_size,
                       max_answer_length, do_lower_case, output_prediction_file,
                       output_nbest_file, output_null_log_odds_file, verbose_logging,
@@ -612,6 +664,182 @@ def write_predictions(all_examples, all_features, all_results, n_best_size,
     return all_predictions
 
 
+# For XLNet (and XLM which uses the same head)
+RawResultExtended = collections.namedtuple("RawResultExtended",
+    ["unique_id", "start_top_log_probs", "start_top_index",
+     "end_top_log_probs", "end_top_index", "cls_logits"])
+
+
+def write_predictions_extended(all_examples, all_features, all_results, n_best_size,
+                                max_answer_length, output_prediction_file,
+                                output_nbest_file,
+                                output_null_log_odds_file, orig_data,
+                                start_n_top, end_n_top, version_2_with_negative):
+    """ XLNet write prediction logic (more complex than Bert's).
+        Write final predictions to the json file and log-odds of null if needed.
+
+        Requires utils_squad_evaluate.py
+    """
+    _PrelimPrediction = collections.namedtuple(  # pylint: disable=invalid-name
+        "PrelimPrediction",
+        ["feature_index", "start_index", "end_index",
+        "start_log_prob", "end_log_prob"])
+
+    _NbestPrediction = collections.namedtuple(  # pylint: disable=invalid-name
+        "NbestPrediction", ["text", "start_log_prob", "end_log_prob"])
+
+    logger.info("Writing predictions to: %s", output_prediction_file)
+    # logger.info("Writing nbest to: %s" % (output_nbest_file))
+
+    example_index_to_features = collections.defaultdict(list)
+    for feature in all_features:
+        example_index_to_features[feature.example_index].append(feature)
+
+    unique_id_to_result = {}
+    for result in all_results:
+        unique_id_to_result[result.unique_id] = result
+
+    all_predictions = collections.OrderedDict()
+    all_nbest_json = collections.OrderedDict()
+    scores_diff_json = collections.OrderedDict()
+
+    for (example_index, example) in enumerate(all_examples):
+        features = example_index_to_features[example_index]
+
+        prelim_predictions = []
+        # keep track of the minimum score of null start+end of position 0
+        score_null = 1000000  # large and positive
+
+        for (feature_index, feature) in enumerate(features):
+            result = unique_id_to_result[feature.unique_id]
+
+            cur_null_score = result.cls_logits
+
+            # if we could have irrelevant answers, get the min score of irrelevant
+            score_null = min(score_null, cur_null_score)
+
+            for i in range(start_n_top):
+                for j in range(end_n_top):
+                    start_log_prob = result.start_top_log_probs[i]
+                    start_index = result.start_top_index[i]
+
+                    j_index = i * end_n_top + j
+
+                    end_log_prob = result.end_top_log_probs[j_index]
+                    end_index = result.end_top_index[j_index]
+
+                    # We could hypothetically create invalid predictions, e.g., predict
+                    # that the start of the span is in the question. We throw out all
+                    # invalid predictions.
+                    if start_index >= feature.paragraph_len - 1:
+                        continue
+                    if end_index >= feature.paragraph_len - 1:
+                        continue
+
+                    if not feature.token_is_max_context.get(start_index, False):
+                        continue
+                    if end_index < start_index:
+                        continue
+                    length = end_index - start_index + 1
+                    if length > max_answer_length:
+                        continue
+
+                    prelim_predictions.append(
+                        _PrelimPrediction(
+                            feature_index=feature_index,
+                            start_index=start_index,
+                            end_index=end_index,
+                            start_log_prob=start_log_prob,
+                            end_log_prob=end_log_prob))
+
+        prelim_predictions = sorted(
+            prelim_predictions,
+            key=lambda x: (x.start_log_prob + x.end_log_prob),
+            reverse=True)
+
+        seen_predictions = {}
+        nbest = []
+        for pred in prelim_predictions:
+            if len(nbest) >= n_best_size:
+                break
+            feature = features[pred.feature_index]
+
+            tok_start_to_orig_index = feature.tok_start_to_orig_index
+            tok_end_to_orig_index = feature.tok_end_to_orig_index
+            start_orig_pos = tok_start_to_orig_index[pred.start_index]
+            end_orig_pos = tok_end_to_orig_index[pred.end_index]
+
+            paragraph_text = example.paragraph_text
+            final_text = paragraph_text[start_orig_pos: end_orig_pos + 1].strip()
+
+            if final_text in seen_predictions:
+                continue
+
+            seen_predictions[final_text] = True
+
+            nbest.append(
+                _NbestPrediction(
+                    text=final_text,
+                    start_log_prob=pred.start_log_prob,
+                    end_log_prob=pred.end_log_prob))
+
+        # In very rare edge cases we could have no valid predictions. So we
+        # just create a nonce prediction in this case to avoid failure.
+        if not nbest:
+            nbest.append(
+                _NbestPrediction(text="", start_log_prob=-1e6,
+                end_log_prob=-1e6))
+
+        total_scores = []
+        best_non_null_entry = None
+        for entry in nbest:
+            total_scores.append(entry.start_log_prob + entry.end_log_prob)
+            if not best_non_null_entry:
+                best_non_null_entry = entry
+
+        probs = _compute_softmax(total_scores)
+
+        nbest_json = []
+        for (i, entry) in enumerate(nbest):
+            output = collections.OrderedDict()
+            output["text"] = entry.text
+            output["probability"] = probs[i]
+            output["start_log_prob"] = entry.start_log_prob
+            output["end_log_prob"] = entry.end_log_prob
+            nbest_json.append(output)
+
+        assert len(nbest_json) >= 1
+        assert best_non_null_entry is not None
+
+        score_diff = score_null
+        scores_diff_json[example.qas_id] = score_diff
+        # note(zhiliny): always predict best_non_null_entry
+        # and the evaluation script will search for the best threshold
+        all_predictions[example.qas_id] = best_non_null_entry.text
+
+        all_nbest_json[example.qas_id] = nbest_json
+
+    with open(output_prediction_file, "w") as writer:
+        writer.write(json.dumps(all_predictions, indent=4) + "\n")
+
+    with open(output_nbest_file, "w") as writer:
+        writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
+
+    if version_2_with_negative:
+        with open(output_null_log_odds_file, "w") as writer:
+            writer.write(json.dumps(scores_diff_json, indent=4) + "\n")
+
+    qid_to_has_ans = make_qid_to_has_ans(orig_data)
+    has_ans_qids = [k for k, v in qid_to_has_ans.items() if v]
+    no_ans_qids = [k for k, v in qid_to_has_ans.items() if not v]
+    exact_raw, f1_raw = get_raw_scores(orig_data, all_predictions)
+    out_eval = {}
+
+    find_all_best_thresh_v2(out_eval, all_predictions, exact_raw, f1_raw, scores_diff_json, qid_to_has_ans)
+
+    return out_eval
+
+
 def get_final_text(pred_text, orig_text, do_lower_case, verbose_logging=False):
     """Project the tokenized prediction back to the original text."""
 

examples/utils_squad_evaluate.py

@@ -1,4 +1,5 @@
 """ Official evaluation script for SQuAD version 2.0.
+    Modified by XLNet authors to update `find_best_threshold` scripts for SQuAD V2.0
 
 In addition to basic functionality, we also compute additional statistics and
 plot precision-recall curves if an additional na_prob.json file is provided.
@@ -232,6 +233,36 @@ def find_best_thresh(preds, scores, na_probs, qid_to_has_ans):
       best_thresh = na_probs[qid]
   return 100.0 * best_score / len(scores), best_thresh
 
+def find_best_thresh_v2(preds, scores, na_probs, qid_to_has_ans):
+  num_no_ans = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k])
+  cur_score = num_no_ans
+  best_score = cur_score
+  best_thresh = 0.0
+  qid_list = sorted(na_probs, key=lambda k: na_probs[k])
+  for i, qid in enumerate(qid_list):
+    if qid not in scores: continue
+    if qid_to_has_ans[qid]:
+      diff = scores[qid]
+    else:
+      if preds[qid]:
+        diff = -1
+      else:
+        diff = 0
+    cur_score += diff
+    if cur_score > best_score:
+      best_score = cur_score
+      best_thresh = na_probs[qid]
+
+  has_ans_score, has_ans_cnt = 0, 0
+  for qid in qid_list:
+    if not qid_to_has_ans[qid]: continue
+    has_ans_cnt += 1
+
+    if qid not in scores: continue
+    has_ans_score += scores[qid]
+
+  return 100.0 * best_score / len(scores), best_thresh, 1.0 * has_ans_score / has_ans_cnt
+
 def find_all_best_thresh(main_eval, preds, exact_raw, f1_raw, na_probs, qid_to_has_ans):
   best_exact, exact_thresh = find_best_thresh(preds, exact_raw, na_probs, qid_to_has_ans)
   best_f1, f1_thresh = find_best_thresh(preds, f1_raw, na_probs, qid_to_has_ans)
@@ -240,6 +271,16 @@ def find_all_best_thresh(main_eval, preds, exact_raw, f1_raw, na_probs, qid_to_h
   main_eval['best_f1'] = best_f1
   main_eval['best_f1_thresh'] = f1_thresh
 
+def find_all_best_thresh_v2(main_eval, preds, exact_raw, f1_raw, na_probs, qid_to_has_ans):
+  best_exact, exact_thresh, has_ans_exact = find_best_thresh_v2(preds, exact_raw, na_probs, qid_to_has_ans)
+  best_f1, f1_thresh, has_ans_f1 = find_best_thresh_v2(preds, f1_raw, na_probs, qid_to_has_ans)
+  main_eval['best_exact'] = best_exact
+  main_eval['best_exact_thresh'] = exact_thresh
+  main_eval['best_f1'] = best_f1
+  main_eval['best_f1_thresh'] = f1_thresh
+  main_eval['has_ans_exact'] = has_ans_exact
+  main_eval['has_ans_f1'] = has_ans_f1
+
 def main(OPTS):
   with open(OPTS.data_file) as f:
     dataset_json = json.load(f)

pytorch_transformers/modeling_utils.py

@@ -493,8 +493,9 @@ class PoolerStartLogits(nn.Module):
 
     def forward(self, hidden_states, p_mask=None):
         """ Args:
-            `p_mask`: [optional] invalid position mask such as query and special symbols (PAD, SEP, CLS)
+            **p_mask**: (`optional`) ``torch.FloatTensor`` of shape `(batch_size, seq_len)`
-                shape [batch_size, seq_len]. 1.0 means token should be masked.
+                invalid position mask such as query and special symbols (PAD, SEP, CLS)
+                1.0 means token should be masked.
         """
         x = self.dense(hidden_states).squeeze(-1)
 
@@ -516,11 +517,16 @@ class PoolerEndLogits(nn.Module):
 
     def forward(self, hidden_states, start_states=None, start_positions=None, p_mask=None):
         """ Args:
-            One of start_states, start_positions should be not None. If both are set, start_positions overrides start_states.
+            One of ``start_states``, ``start_positions`` should be not None.
-            `start_states`: hidden states of the first tokens for the labeled span: torch.LongTensor of shape identical to hidden_states.
+            If both are set, ``start_positions`` overrides ``start_states``.
-            `start_positions`: position of the first token for the labeled span: torch.LongTensor of shape [batch_size].
+
-            `p_mask`: [optional] invalid position mask such as query and special symbols (PAD, SEP, CLS)
+            **start_states**: ``torch.LongTensor`` of shape identical to hidden_states
-                shape [batch_size, seq_len]. 1.0 means token should be masked.
+                hidden states of the first tokens for the labeled span.
+            **start_positions**: ``torch.LongTensor`` of shape ``(batch_size,)``
+                position of the first token for the labeled span: 
+            **p_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, seq_len)``
+                Mask of invalid position such as query and special symbols (PAD, SEP, CLS)
+                1.0 means token should be masked.
         """
         slen, hsz = hidden_states.shape[-2:]
         assert start_states is not None or start_positions is not None, "One of start_states, start_positions should be not None"
@@ -549,13 +555,21 @@ class PoolerAnswerClass(nn.Module):
         self.dense_1 = nn.Linear(config.hidden_size, 1, bias=False)
 
     def forward(self, hidden_states, start_states=None, start_positions=None, cls_index=None):
-        """ Args:
+        """
-            One of start_states, start_positions should be not None. If both are set, start_positions overrides start_states.
+        Args:
-            `start_states`: hidden states of the first tokens for the labeled span: torch.LongTensor of shape identical to hidden_states.
+            One of ``start_states``, ``start_positions`` should be not None.
-            `start_positions`: position of the first token for the labeled span: torch.LongTensor of shape [batch_size].
+            If both are set, ``start_positions`` overrides ``start_states``.
-            `cls_index`: position of the CLS token: torch.LongTensor of shape [batch_size]. If None, take the last token.
 
-            # note(zhiliny): no dependency on end_feature so that we can obtain one single `cls_logits` for each sample
+            **start_states**: ``torch.LongTensor`` of shape identical to ``hidden_states``.
+                hidden states of the first tokens for the labeled span.
+            **start_positions**: ``torch.LongTensor`` of shape ``(batch_size,)``
+                position of the first token for the labeled span.
+            **cls_index**: torch.LongTensor of shape ``(batch_size,)``
+                position of the CLS token. If None, take the last token.
+
+            note(Original repo):
+                no dependency on end_feature so that we can obtain one single `cls_logits`
+                for each sample
         """
         slen, hsz = hidden_states.shape[-2:]
         assert start_states is not None or start_positions is not None, "One of start_states, start_positions should be not None"
@@ -577,7 +591,35 @@ class PoolerAnswerClass(nn.Module):
 
 
 class SQuADHead(nn.Module):
-    """ A SQuAD head inspired by XLNet.
+    r""" A SQuAD head inspired by XLNet.
+
+    Parameters:
+        config (:class:`~pytorch_transformers.XLNetConfig`): Model configuration class with all the parameters of the model.
+
+    Inputs:
+        **hidden_states**: ``torch.FloatTensor`` of shape ``(batch_size, seq_len, hidden_size)``
+            hidden states of sequence tokens
+        **start_positions**: ``torch.LongTensor`` of shape ``(batch_size,)``
+            position of the first token for the labeled span.
+        **end_positions**: ``torch.LongTensor`` of shape ``(batch_size,)``
+            position of the last token for the labeled span.
+        **cls_index**: torch.LongTensor of shape ``(batch_size,)``
+            position of the CLS token. If None, take the last token.
+        **is_impossible**: ``torch.LongTensor`` of shape ``(batch_size,)``
+            Whether the question has a possible answer in the paragraph or not.
+        **p_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, seq_len)``
+            Mask of invalid position such as query and special symbols (PAD, SEP, CLS)
+            1.0 means token should be masked.
+
+    Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
+        **loss**: (`optional`, returned if both ``start_positions`` and ``end_positions`` are provided) ``torch.FloatTensor`` of shape ``(1,)``:
+            Classification loss as the sum of start token, end token (and is_impossible if provided) classification losses.
+        **last_hidden_state**: `(`optional`, returned if ``start_positions`` or ``end_positions`` is not provided) `torch.FloatTensor`` of shape ``(batch_size, sequence_length, hidden_size)``
+            Sequence of hidden-states at the last layer of the model.
+        **mems**:
+            list of ``torch.FloatTensor`` (one for each layer):
+            that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
+            (see `mems` input above). Can be used to speed up sequential decoding and attend to longer context.
     """
     def __init__(self, config):
         super(SQuADHead, self).__init__()
@@ -590,8 +632,6 @@ class SQuADHead(nn.Module):
 
     def forward(self, hidden_states, start_positions=None, end_positions=None,
                 cls_index=None, is_impossible=None, p_mask=None):
-        """ hidden_states: float Tensor in shape [bsz, seq_len, hidden_size], the hidden-states of the last layer.
-        """
         outputs = ()
 
         start_logits = self.start_logits(hidden_states, p_mask=p_mask)
@@ -618,9 +658,8 @@ class SQuADHead(nn.Module):
 
                 # note(zhiliny): by default multiply the loss by 0.5 so that the scale is comparable to start_loss and end_loss
                 total_loss += cls_loss * 0.5
-                outputs = (total_loss, start_logits, end_logits, cls_logits) + outputs
+
-            else:
+            outputs = (total_loss,) + outputs
-                outputs = (total_loss, start_logits, end_logits) + outputs
 
         else:
             # during inference, compute the end logits based on beam search
@@ -647,7 +686,7 @@ class SQuADHead(nn.Module):
             outputs = (start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits) + outputs
 
         # return start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits
-        # or (if labels are provided) total_loss, start_logits, end_logits, (cls_logits)
+        # or (if labels are provided) (total_loss,)
         return outputs
 
 

pytorch_transformers/modeling_xlnet.py

@@ -1162,8 +1162,9 @@ class XLNetForQuestionAnswering(XLNetPreTrainedModel):
             Labels whether a question has an answer or no answer (SQuAD 2.0)
         **cls_index**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
             Labels for position (index) of the classification token to use as input for computing plausibility of the answer.
-        **p_mask**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
+        **p_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length)``:
-            Optional mask of tokens which can't be in answers (e.g. [CLS], [PAD], ...) 
+            Optional mask of tokens which can't be in answers (e.g. [CLS], [PAD], ...).
+            1.0 means token should be masked. 0.0 mean token is not masked.
 
     Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
         **loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``: