!9258 add squad for bert

From: @yoonlee666
Reviewed-by: @c_34,@guoqi1024
Signed-off-by: @c_34

model_zoo/official/nlp/bert/run_classifier.py

@@ -144,12 +144,14 @@ def run_classifier():
     parser.add_argument("--do_eval", type=str, default="false", choices=["true", "false"],
                         help="Enable eval, default is false")
     parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
-    parser.add_argument("--epoch_num", type=int, default="1", help="Epoch number, default is 1.")
-    parser.add_argument("--num_class", type=int, default="2", help="The number of class, default is 2.")
+    parser.add_argument("--epoch_num", type=int, default=3, help="Epoch number, default is 3.")
+    parser.add_argument("--num_class", type=int, default=2, help="The number of class, default is 2.")
     parser.add_argument("--train_data_shuffle", type=str, default="true", choices=["true", "false"],
                         help="Enable train data shuffle, default is true")
     parser.add_argument("--eval_data_shuffle", type=str, default="false", choices=["true", "false"],
                         help="Enable eval data shuffle, default is false")
+    parser.add_argument("--train_batch_size", type=int, default=32, help="Train batch size, default is 32")
+    parser.add_argument("--eval_batch_size", type=int, default=1, help="Eval batch size, default is 1")
     parser.add_argument("--save_finetune_checkpoint_path", type=str, default="", help="Save checkpoint path")
     parser.add_argument("--load_pretrain_checkpoint_path", type=str, default="", help="Load checkpoint file path")
     parser.add_argument("--load_finetune_checkpoint_path", type=str, default="", help="Load checkpoint file path")
@@ -188,7 +190,7 @@ def run_classifier():
                           assessment_method=assessment_method)
 
     if args_opt.do_train.lower() == "true":
-        ds = create_classification_dataset(batch_size=optimizer_cfg.batch_size, repeat_count=1,
+        ds = create_classification_dataset(batch_size=args_opt.train_batch_size, repeat_count=1,
                                            assessment_method=assessment_method,
                                            data_file_path=args_opt.train_data_file_path,
                                            schema_file_path=args_opt.schema_file_path,
@@ -204,7 +206,7 @@ def run_classifier():
                                                            ds.get_dataset_size(), epoch_num, "classifier")
 
     if args_opt.do_eval.lower() == "true":
-        ds = create_classification_dataset(batch_size=optimizer_cfg.batch_size, repeat_count=1,
+        ds = create_classification_dataset(batch_size=args_opt.eval_batch_size, repeat_count=1,
                                            assessment_method=assessment_method,
                                            data_file_path=args_opt.eval_data_file_path,
                                            schema_file_path=args_opt.schema_file_path,

model_zoo/official/nlp/bert/run_ner.py

@@ -97,14 +97,12 @@ def eval_result_print(assessment_method="accuracy", callback=None):
     else:
         raise ValueError("Assessment method not supported, support: [accuracy, f1, mcc, spearman_correlation]")
 
-def do_eval(dataset=None, network=None, use_crf="", num_class=2, assessment_method="accuracy", data_file="",
-            load_checkpoint_path="", vocab_file="", label_file="", tag_to_index=None):
+def do_eval(dataset=None, network=None, use_crf="", num_class=41, assessment_method="accuracy", data_file="",
+            load_checkpoint_path="", vocab_file="", label_file="", tag_to_index=None, batch_size=1):
     """ do eval """
     if load_checkpoint_path == "":
         raise ValueError("Finetune model missed, evaluation task must load finetune model!")
-    if assessment_method == "clue_benchmark":
-        optimizer_cfg.batch_size = 1
-    net_for_pretraining = network(bert_net_cfg, optimizer_cfg.batch_size, False, num_class,
+    net_for_pretraining = network(bert_net_cfg, batch_size, False, num_class,
                                   use_crf=(use_crf.lower() == "true"), tag_to_index=tag_to_index)
     net_for_pretraining.set_train(False)
     param_dict = load_checkpoint(load_checkpoint_path)
@@ -142,7 +140,7 @@ def do_eval(dataset=None, network=None, use_crf="", num_class=2, assessment_meth
 
 def parse_args():
     """set and check parameters."""
-    parser = argparse.ArgumentParser(description="run classifier")
+    parser = argparse.ArgumentParser(description="run ner")
     parser.add_argument("--device_target", type=str, default="Ascend", choices=["Ascend", "GPU"],
                         help="Device type, default is Ascend")
     parser.add_argument("--assessment_method", type=str, default="F1", choices=["F1", "clue_benchmark"],
@@ -154,12 +152,14 @@ def parse_args():
     parser.add_argument("--use_crf", type=str, default="false", choices=["true", "false"],
                         help="Use crf, default is false")
     parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
-    parser.add_argument("--epoch_num", type=int, default="1", help="Epoch number, default is 1.")
-    parser.add_argument("--num_class", type=int, default="41", help="The number of class, default is 41.")
+    parser.add_argument("--epoch_num", type=int, default=5, help="Epoch number, default is 5.")
+    parser.add_argument("--num_class", type=int, default=41, help="The number of class, default is 41.")
     parser.add_argument("--train_data_shuffle", type=str, default="true", choices=["true", "false"],
                         help="Enable train data shuffle, default is true")
     parser.add_argument("--eval_data_shuffle", type=str, default="false", choices=["true", "false"],
                         help="Enable eval data shuffle, default is false")
+    parser.add_argument("--train_batch_size", type=int, default=32, help="Train batch size, default is 32")
+    parser.add_argument("--eval_batch_size", type=int, default=1, help="Eval batch size, default is 1")
     parser.add_argument("--vocab_file_path", type=str, default="", help="Vocab file path, used in clue benchmark")
     parser.add_argument("--label_file_path", type=str, default="", help="label file path, used in clue benchmark")
     parser.add_argument("--save_finetune_checkpoint_path", type=str, default="", help="Save checkpoint path")
@@ -184,6 +184,8 @@ def parse_args():
         raise ValueError("'label_file_path' must be set to use crf")
     if args_opt.assessment_method.lower() == "clue_benchmark" and args_opt.label_file_path == "":
         raise ValueError("'label_file_path' must be set to do clue benchmark")
+    if args_opt.assessment_method.lower() == "clue_benchmark":
+        args_opt.eval_batch_size = 1
     return args_opt
 
 
@@ -217,11 +219,11 @@ def run_ner():
         number_labels = len(tag_to_index)
     else:
         number_labels = args_opt.num_class
-    netwithloss = BertNER(bert_net_cfg, optimizer_cfg.batch_size, True, num_labels=number_labels,
-                          use_crf=(args_opt.use_crf.lower() == "true"),
-                          tag_to_index=tag_to_index, dropout_prob=0.1)
     if args_opt.do_train.lower() == "true":
-        ds = create_ner_dataset(batch_size=optimizer_cfg.batch_size, repeat_count=1,
+        netwithloss = BertNER(bert_net_cfg, args_opt.train_batch_size, True, num_labels=number_labels,
+                              use_crf=(args_opt.use_crf.lower() == "true"),
+                              tag_to_index=tag_to_index, dropout_prob=0.1)
+        ds = create_ner_dataset(batch_size=args_opt.train_batch_size, repeat_count=1,
                                 assessment_method=assessment_method, data_file_path=args_opt.train_data_file_path,
                                 schema_file_path=args_opt.schema_file_path,
                                 do_shuffle=(args_opt.train_data_shuffle.lower() == "true"))
@@ -236,12 +238,13 @@ def run_ner():
                                                            ds.get_dataset_size(), epoch_num, "ner")
 
     if args_opt.do_eval.lower() == "true":
-        ds = create_ner_dataset(batch_size=optimizer_cfg.batch_size, repeat_count=1,
+        ds = create_ner_dataset(batch_size=args_opt.eval_batch_size, repeat_count=1,
                                 assessment_method=assessment_method, data_file_path=args_opt.eval_data_file_path,
                                 schema_file_path=args_opt.schema_file_path,
                                 do_shuffle=(args_opt.eval_data_shuffle.lower() == "true"))
-        do_eval(ds, BertNER, args_opt.use_crf, number_labels, assessment_method, args_opt.eval_data_file_path,
-                load_finetune_checkpoint_path, args_opt.vocab_file_path, args_opt.label_file_path, tag_to_index)
+        do_eval(ds, BertNER, args_opt.use_crf, number_labels, assessment_method,
+                args_opt.eval_data_file_path, load_finetune_checkpoint_path, args_opt.vocab_file_path,
+                args_opt.label_file_path, tag_to_index, args_opt.eval_batch_size)
 
 if __name__ == "__main__":
     run_ner()

model_zoo/official/nlp/bert/run_squad.py

@@ -22,9 +22,6 @@ import collections
 from src.bert_for_finetune import BertSquadCell, BertSquad
 from src.finetune_eval_config import optimizer_cfg, bert_net_cfg
 from src.dataset import create_squad_dataset
-from src import tokenization
-from src.create_squad_data import read_squad_examples, convert_examples_to_features
-from src.run_squad import write_predictions
 from src.utils import make_directory, LossCallBack, LoadNewestCkpt, BertLearningRate
 import mindspore.common.dtype as mstype
 from mindspore import context
@@ -85,22 +82,10 @@ def do_train(dataset=None, network=None, load_checkpoint_path="", save_checkpoin
     model.train(epoch_num, dataset, callbacks=callbacks)
 
 
-def do_eval(dataset=None, vocab_file="", eval_json="", load_checkpoint_path="", seq_length=384):
+def do_eval(dataset=None, load_checkpoint_path="", eval_batch_size=1):
     """ do eval """
     if load_checkpoint_path == "":
         raise ValueError("Finetune model missed, evaluation task must load finetune model!")
-    tokenizer = tokenization.FullTokenizer(vocab_file=vocab_file, do_lower_case=True)
-    eval_examples = read_squad_examples(eval_json, False)
-    eval_features = convert_examples_to_features(
-        examples=eval_examples,
-        tokenizer=tokenizer,
-        max_seq_length=seq_length,
-        doc_stride=128,
-        max_query_length=64,
-        is_training=False,
-        output_fn=None,
-        verbose_logging=False)
-
     net = BertSquad(bert_net_cfg, False, 2)
     net.set_train(False)
     param_dict = load_checkpoint(load_checkpoint_path)
@@ -123,7 +108,7 @@ def do_eval(dataset=None, vocab_file="", eval_json="", load_checkpoint_path="",
         start = logits[1].asnumpy()
         end = logits[2].asnumpy()
 
-        for i in range(optimizer_cfg.batch_size):
+        for i in range(eval_batch_size):
             unique_id = int(ids[i])
             start_logits = [float(x) for x in start[i].flat]
             end_logits = [float(x) for x in end[i].flat]
@@ -131,11 +116,11 @@ def do_eval(dataset=None, vocab_file="", eval_json="", load_checkpoint_path="",
                 unique_id=unique_id,
                 start_logits=start_logits,
                 end_logits=end_logits))
-    write_predictions(eval_examples, eval_features, output, 20, 30, True, "./predictions.json", None, None)
+    return output
 
 def run_squad():
     """run squad task"""
-    parser = argparse.ArgumentParser(description="run classifier")
+    parser = argparse.ArgumentParser(description="run squad")
     parser.add_argument("--device_target", type=str, default="Ascend", choices=["Ascend", "GPU"],
                         help="Device type, default is Ascend")
     parser.add_argument("--do_train", type=str, default="false", choices=["true", "false"],
@@ -143,12 +128,14 @@ def run_squad():
     parser.add_argument("--do_eval", type=str, default="false", choices=["true", "false"],
                         help="Eable eval, default is false")
     parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
-    parser.add_argument("--epoch_num", type=int, default="1", help="Epoch number, default is 1.")
-    parser.add_argument("--num_class", type=int, default="2", help="The number of class, default is 2.")
+    parser.add_argument("--epoch_num", type=int, default=3, help="Epoch number, default is 1.")
+    parser.add_argument("--num_class", type=int, default=2, help="The number of class, default is 2.")
     parser.add_argument("--train_data_shuffle", type=str, default="true", choices=["true", "false"],
                         help="Enable train data shuffle, default is true")
     parser.add_argument("--eval_data_shuffle", type=str, default="false", choices=["true", "false"],
                         help="Enable eval data shuffle, default is false")
+    parser.add_argument("--train_batch_size", type=int, default=32, help="Train batch size, default is 32")
+    parser.add_argument("--eval_batch_size", type=int, default=1, help="Eval batch size, default is 1")
     parser.add_argument("--vocab_file_path", type=str, default="", help="Vocab file path")
     parser.add_argument("--eval_json_path", type=str, default="", help="Evaluation json file path, can be eval.json")
     parser.add_argument("--save_finetune_checkpoint_path", type=str, default="", help="Save checkpoint path")
@@ -156,8 +143,6 @@ def run_squad():
     parser.add_argument("--load_finetune_checkpoint_path", type=str, default="", help="Load checkpoint file path")
     parser.add_argument("--train_data_file_path", type=str, default="",
                         help="Data path, it is better to use absolute path")
-    parser.add_argument("--eval_data_file_path", type=str, default="",
-                        help="Data path, it is better to use absolute path")
     parser.add_argument("--schema_file_path", type=str, default="",
                         help="Schema path, it is better to use absolute path")
     args_opt = parser.parse_args()
@@ -171,8 +156,6 @@ def run_squad():
     if args_opt.do_train.lower() == "true" and args_opt.train_data_file_path == "":
         raise ValueError("'train_data_file_path' must be set when do finetune task")
     if args_opt.do_eval.lower() == "true":
-        if args_opt.eval_data_file_path == "":
-            raise ValueError("'eval_data_file_path' must be set when do evaluation task")
         if args_opt.vocab_file_path == "":
             raise ValueError("'vocab_file_path' must be set when do evaluation task")
         if args_opt.eval_json_path == "":
@@ -193,7 +176,7 @@ def run_squad():
     netwithloss = BertSquad(bert_net_cfg, True, 2, dropout_prob=0.1)
 
     if args_opt.do_train.lower() == "true":
-        ds = create_squad_dataset(batch_size=optimizer_cfg.batch_size, repeat_count=1,
+        ds = create_squad_dataset(batch_size=args_opt.train_batch_size, repeat_count=1,
                                   data_file_path=args_opt.train_data_file_path,
                                   schema_file_path=args_opt.schema_file_path,
                                   do_shuffle=(args_opt.train_data_shuffle.lower() == "true"))
@@ -207,12 +190,29 @@ def run_squad():
                                                            ds.get_dataset_size(), epoch_num, "squad")
 
     if args_opt.do_eval.lower() == "true":
-        ds = create_squad_dataset(batch_size=optimizer_cfg.batch_size, repeat_count=1,
-                                  data_file_path=args_opt.eval_data_file_path,
+        from src import tokenization
+        from src.create_squad_data import read_squad_examples, convert_examples_to_features
+        from src.squad_get_predictions import write_predictions
+        from src.squad_postprocess import SQuad_postprocess
+        tokenizer = tokenization.FullTokenizer(vocab_file=args_opt.vocab_file_path, do_lower_case=True)
+        eval_examples = read_squad_examples(args_opt.eval_json_path, False)
+        eval_features = convert_examples_to_features(
+            examples=eval_examples,
+            tokenizer=tokenizer,
+            max_seq_length=bert_net_cfg.seq_length,
+            doc_stride=128,
+            max_query_length=64,
+            is_training=False,
+            output_fn=None,
+            vocab_file=args_opt.vocab_file_path)
+        ds = create_squad_dataset(batch_size=args_opt.eval_batch_size, repeat_count=1,
+                                  data_file_path=eval_features,
                                   schema_file_path=args_opt.schema_file_path, is_training=False,
                                   do_shuffle=(args_opt.eval_data_shuffle.lower() == "true"))
-        do_eval(ds, args_opt.vocab_file_path, args_opt.eval_json_path,
-                load_finetune_checkpoint_path, bert_net_cfg.seq_length)
+        outputs = do_eval(ds, load_finetune_checkpoint_path, args_opt.eval_batch_size)
+        all_predictions = write_predictions(eval_examples, eval_features, outputs, 20, 30, True)
+        SQuad_postprocess(args_opt.eval_json_path, all_predictions, output_metrics="output.json")
+
 
 if __name__ == "__main__":
     run_squad()

model_zoo/official/nlp/bert/scripts/run_classifier.sh

@@ -32,13 +32,15 @@ python ${PROJECT_DIR}/../run_classifier.py  \
     --do_eval="false" \
     --assessment_method="Accuracy" \
     --device_id=0 \
-    --epoch_num=1 \
+    --epoch_num=3 \
     --num_class=2 \
     --train_data_shuffle="true" \
     --eval_data_shuffle="false" \
+    --train_batch_size=32 \
+    --eval_batch_size=1 \
     --save_finetune_checkpoint_path="" \
     --load_pretrain_checkpoint_path="" \
     --load_finetune_checkpoint_path="" \
     --train_data_file_path="" \
     --eval_data_file_path="" \
-    --schema_file_path="" > classfifier_log.txt 2>&1 &
+    --schema_file_path="" > classifier_log.txt 2>&1 &

model_zoo/official/nlp/bert/scripts/run_ner.sh

@@ -33,10 +33,12 @@ python ${PROJECT_DIR}/../run_ner.py  \
     --assessment_method="F1" \
     --use_crf="false" \
     --device_id=0 \
-    --epoch_num=1 \
-    --num_class=2 \
+    --epoch_num=5 \
+    --num_class=41 \
     --train_data_shuffle="true" \
     --eval_data_shuffle="false" \
+    --train_batch_size=32 \
+    --eval_batch_size=1 \
     --vocab_file_path="" \
     --label_file_path="" \
     --save_finetune_checkpoint_path="" \

model_zoo/official/nlp/bert/scripts/run_squad.sh

@@ -31,15 +31,16 @@ python ${PROJECT_DIR}/../run_squad.py  \
     --do_train="true" \
     --do_eval="false" \
     --device_id=0 \
-    --epoch_num=1 \
+    --epoch_num=3 \
     --num_class=2 \
     --train_data_shuffle="true" \
     --eval_data_shuffle="false" \
+    --train_batch_size=32 \
+    --eval_batch_size=1 \
     --vocab_file_path="" \
-    --eval_json_path="" \
     --save_finetune_checkpoint_path="" \
     --load_pretrain_checkpoint_path="" \
     --load_finetune_checkpoint_path="" \
     --train_data_file_path="" \
-    --eval_data_file_path="" \
+    --eval_json_path="" \
     --schema_file_path="" > squad_log.txt 2>&1 &

model_zoo/official/nlp/bert/src/bert_for_finetune.py

@@ -325,6 +325,8 @@ class BertSquad(nn.Cell):
             total_loss = (start_loss + end_loss) / 2.0
         else:
             start_logits = self.squeeze(logits[:, :, 0:1])
+            start_logits = start_logits + 100 * input_mask
             end_logits = self.squeeze(logits[:, :, 1:2])
+            end_logits = end_logits + 100 * input_mask
             total_loss = (unique_id, start_logits, end_logits)
         return total_loss

model_zoo/official/nlp/bert/src/create_squad_data.py

@@ -0,0 +1,309 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+
+"""create squad data"""
+
+import collections
+import json
+from src import tokenization
+
+
+class SquadExample():
+    """extract column contents from raw data"""
+    def __init__(self,
+                 qas_id,
+                 question_text,
+                 doc_tokens,
+                 orig_answer_text=None,
+                 start_position=None,
+                 end_position=None,
+                 is_impossible=False):
+        self.qas_id = qas_id
+        self.question_text = question_text
+        self.doc_tokens = doc_tokens
+        self.orig_answer_text = orig_answer_text
+        self.start_position = start_position
+        self.end_position = end_position
+        self.is_impossible = is_impossible
+
+
+class InputFeatures():
+    """A single set of features of data."""
+    def __init__(self,
+                 unique_id,
+                 example_index,
+                 doc_span_index,
+                 tokens,
+                 token_to_orig_map,
+                 token_is_max_context,
+                 input_ids,
+                 input_mask,
+                 segment_ids,
+                 start_position=None,
+                 end_position=None,
+                 is_impossible=None):
+        self.unique_id = unique_id
+        self.example_index = example_index
+        self.doc_span_index = doc_span_index
+        self.tokens = tokens
+        self.token_to_orig_map = token_to_orig_map
+        self.token_is_max_context = token_is_max_context
+        self.input_ids = input_ids
+        self.input_mask = input_mask
+        self.segment_ids = segment_ids
+        self.start_position = start_position
+        self.end_position = end_position
+        self.is_impossible = is_impossible
+
+
+def read_squad_examples(input_file, is_training, version_2_with_negative=False):
+    """Return list of SquadExample from input_data or input_file (SQuAD json file)"""
+    with open(input_file, "r") as reader:
+        input_data = json.load(reader)["data"]
+
+    def is_whitespace(c):
+        if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F:
+            return True
+        return False
+
+    def token_offset(text):
+        doc_tokens = []
+        char_to_word_offset = []
+        prev_is_whitespace = True
+        for c in paragraph_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)
+        return (doc_tokens, char_to_word_offset)
+
+
+    def process_one_example(qa, is_training, version_2_with_negative, doc_tokens, char_to_word_offset):
+        qas_id = qa["id"]
+        question_text = qa["question"]
+        start_position = -1
+        end_position = -1
+        orig_answer_text = ""
+        is_impossible = False
+        if is_training:
+            if version_2_with_negative:
+                is_impossible = qa["is_impossible"]
+            if (len(qa["answers"]) != 1) and (not is_impossible):
+                raise ValueError("For training, each question should have exactly 1 answer.")
+            if not is_impossible:
+                answer = qa["answers"][0]
+                orig_answer_text = answer["text"]
+                answer_offset = answer["answer_start"]
+                answer_length = len(orig_answer_text)
+                start_position = char_to_word_offset[answer_offset]
+                end_position = char_to_word_offset[answer_offset + answer_length - 1]
+                actual_text = " ".join(doc_tokens[start_position:(end_position + 1)])
+                cleaned_answer_text = " ".join(tokenization.whitespace_tokenize(orig_answer_text))
+                if actual_text.find(cleaned_answer_text) == -1:
+                    return None
+        example = SquadExample(
+            qas_id=qas_id,
+            question_text=question_text,
+            doc_tokens=doc_tokens,
+            orig_answer_text=orig_answer_text,
+            start_position=start_position,
+            end_position=end_position,
+            is_impossible=is_impossible)
+        return example
+
+
+    examples = []
+    for entry in input_data:
+        for paragraph in entry["paragraphs"]:
+            paragraph_text = paragraph["context"]
+            doc_tokens, char_to_word_offset = token_offset(paragraph_text)
+            for qa in paragraph["qas"]:
+                one_example = process_one_example(qa, is_training, version_2_with_negative,
+                                                  doc_tokens, char_to_word_offset)
+                if one_example is not None:
+                    examples.append(one_example)
+    return examples
+
+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
+    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 _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))
+
+    for new_start in range(input_start, input_end + 1):
+        for new_end in range(input_end, new_start - 1, -1):
+            text_span = " ".join(doc_tokens[new_start:(new_end + 1)])
+            if text_span == tok_answer_text:
+                return (new_start, new_end)
+
+    return (input_start, input_end)
+
+
+def convert_examples_to_features(examples, tokenizer, max_seq_length, doc_stride,
+                                 max_query_length, is_training, output_fn, vocab_file):
+    """Loads a data file into a list of `InputBatch`s."""
+    unique_id = 1000000000
+    output = []
+    for (example_index, example) in enumerate(examples):
+        query_tokens = tokenizer.tokenize(example.question_text)
+
+        if len(query_tokens) > max_query_length:
+            query_tokens = query_tokens[0:max_query_length]
+
+        tok_to_orig_index = []
+        orig_to_tok_index = []
+        all_doc_tokens = []
+        for (i, token) in enumerate(example.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)
+
+        tok_start_position = None
+        tok_end_position = None
+        if is_training and example.is_impossible:
+            tok_start_position = -1
+            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:
+                tok_end_position = orig_to_tok_index[example.end_position + 1] - 1
+            else:
+                tok_end_position = len(all_doc_tokens) - 1
+            (tok_start_position, tok_end_position) = _improve_answer_span(
+                all_doc_tokens, tok_start_position, tok_end_position, tokenizer,
+                example.orig_answer_text)
+
+        # The -3 accounts for [CLS], [SEP] and [SEP]
+        max_tokens_for_doc = max_seq_length - len(query_tokens) - 3
+
+        # We can have documents that are longer than the maximum sequence length.
+        # To deal with this we do a sliding window approach, where we take chunks
+        # of the up to our max length with a stride of `doc_stride`.
+        _DocSpan = collections.namedtuple("DocSpan", ["start", "length"])
+        doc_spans = []
+        start_offset = 0
+        while 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):
+                break
+            start_offset += min(length, doc_stride)
+
+        for (doc_span_index, doc_span) in enumerate(doc_spans):
+            tokens = []
+            token_to_orig_map = {}
+            token_is_max_context = {}
+            segment_ids = []
+            tokens.append("[CLS]")
+            segment_ids.append(0)
+            for token in query_tokens:
+                tokens.append(token)
+                segment_ids.append(0)
+            tokens.append("[SEP]")
+            segment_ids.append(0)
+
+            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]
+
+                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])
+                segment_ids.append(1)
+            tokens.append("[SEP]")
+            segment_ids.append(1)
+
+            input_ids = tokenization.convert_tokens_to_ids(vocab_file, 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
+
+            start_position = None
+            end_position = None
+            if is_training and not example.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
+                doc_end = doc_span.start + doc_span.length - 1
+                out_of_span = False
+                if not (tok_start_position >= doc_start and tok_end_position <= doc_end):
+                    out_of_span = True
+                if out_of_span:
+                    start_position = 0
+                    end_position = 0
+                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
+                end_position = 0
+
+            feature = InputFeatures(
+                unique_id=unique_id,
+                example_index=example_index,
+                doc_span_index=doc_span_index,
+                tokens=tokens,
+                token_to_orig_map=token_to_orig_map,
+                token_is_max_context=token_is_max_context,
+                input_ids=input_ids,
+                input_mask=input_mask,
+                segment_ids=segment_ids,
+                start_position=start_position,
+                end_position=end_position,
+                is_impossible=example.is_impossible)
+
+            # Run callback
+            output.append(feature)
+            unique_id += 1
+    return output

model_zoo/official/nlp/bert/src/dataset.py

@@ -92,6 +92,11 @@ def create_classification_dataset(batch_size=1, repeat_count=1, assessment_metho
     return ds
 
 
+def generator_squad(data_features):
+    for feature in data_features:
+        yield (feature.input_ids, feature.input_mask, feature.segment_ids, feature.unique_id)
+
+
 def create_squad_dataset(batch_size=1, repeat_count=1, data_file_path=None, schema_file_path=None,
                          is_training=True, do_shuffle=True):
     """create finetune or evaluation dataset"""
@@ -104,11 +109,12 @@ def create_squad_dataset(batch_size=1, repeat_count=1, data_file_path=None, sche
         ds = ds.map(operations=type_cast_op, input_columns="start_positions")
         ds = ds.map(operations=type_cast_op, input_columns="end_positions")
     else:
-        ds = de.TFRecordDataset([data_file_path], schema_file_path if schema_file_path != "" else None,
-                                columns_list=["input_ids", "input_mask", "segment_ids", "unique_ids"])
+        ds = de.GeneratorDataset(generator_squad(data_file_path), shuffle=do_shuffle,
+                                 column_names=["input_ids", "input_mask", "segment_ids", "unique_ids"])
     ds = ds.map(operations=type_cast_op, input_columns="segment_ids")
     ds = ds.map(operations=type_cast_op, input_columns="input_mask")
     ds = ds.map(operations=type_cast_op, input_columns="input_ids")
+    ds = ds.map(operations=type_cast_op, input_columns="unique_ids")
     ds = ds.repeat(repeat_count)
     # apply batch operations
     ds = ds.batch(batch_size, drop_remainder=True)

model_zoo/official/nlp/bert/src/finetune_eval_config.py

@@ -22,7 +22,6 @@ import mindspore.common.dtype as mstype
 from .bert_model import BertConfig
 
 optimizer_cfg = edict({
-    'batch_size': 16,
     'optimizer': 'Lamb',
     'AdamWeightDecay': edict({
         'learning_rate': 2e-5,

model_zoo/official/nlp/bert/src/squad_get_predictions.py

@@ -0,0 +1,258 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+
+"""get predictions for squad"""
+
+import math
+import collections
+import six
+from src import tokenization
+
+
+def get_prelim_predictions(features, unique_id_to_result, n_best_size, max_answer_length):
+    """get prelim predictions"""
+    _PrelimPrediction = collections.namedtuple(
+        "PrelimPrediction",
+        ["feature_index", "start_index", "end_index", "start_logit", "end_logit"])
+    prelim_predictions = []
+    # keep track of the minimum score of null start+end of position 0
+    for (feature_index, feature) in enumerate(features):
+        if feature.unique_id not in unique_id_to_result:
+            continue
+        result = unique_id_to_result[feature.unique_id]
+        start_indexes = _get_best_indexes(result.start_logits, n_best_size)
+        end_indexes = _get_best_indexes(result.end_logits, n_best_size)
+        # if we could have irrelevant answers, get the min score of irrelevant
+        for start_index in start_indexes:
+            for end_index in end_indexes:
+                # 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 >= len(feature.tokens):
+                    continue
+                if end_index >= len(feature.tokens):
+                    continue
+                if start_index not in feature.token_to_orig_map:
+                    continue
+                if end_index not in feature.token_to_orig_map:
+                    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_logit=result.start_logits[start_index],
+                        end_logit=result.end_logits[end_index]))
+
+    prelim_predictions = sorted(
+        prelim_predictions,
+        key=lambda x: (x.start_logit + x.end_logit),
+        reverse=True)
+    return prelim_predictions
+
+
+def get_nbest(prelim_predictions, features, example, n_best_size, do_lower_case):
+    """get nbest predictions"""
+    _NbestPrediction = collections.namedtuple(
+        "NbestPrediction", ["text", "start_logit", "end_logit"])
+
+    seen_predictions = {}
+    nbest = []
+    for pred in prelim_predictions:
+        if len(nbest) >= n_best_size:
+            break
+        feature = features[pred.feature_index]
+        if pred.start_index > 0:  # this is a non-null prediction
+            tok_tokens = feature.tokens[pred.start_index:(pred.end_index + 1)]
+            orig_doc_start = feature.token_to_orig_map[pred.start_index]
+            orig_doc_end = feature.token_to_orig_map[pred.end_index]
+            orig_tokens = example.doc_tokens[orig_doc_start:(orig_doc_end + 1)]
+            tok_text = " ".join(tok_tokens)
+
+            # De-tokenize WordPieces that have been split off.
+            tok_text = tok_text.replace(" ##", "")
+            tok_text = tok_text.replace("##", "")
+
+            # Clean whitespace
+            tok_text = tok_text.strip()
+            tok_text = " ".join(tok_text.split())
+            orig_text = " ".join(orig_tokens)
+            final_text = get_final_text(tok_text, orig_text, do_lower_case)
+            if final_text in seen_predictions:
+                continue
+
+            seen_predictions[final_text] = True
+        else:
+            final_text = ""
+            seen_predictions[final_text] = True
+
+        nbest.append(
+            _NbestPrediction(
+                text=final_text,
+                start_logit=pred.start_logit,
+                end_logit=pred.end_logit))
+
+    # 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="empty", start_logit=0.0, end_logit=0.0))
+
+    assert len(nbest) >= 1
+    return nbest
+
+
+def get_predictions(all_examples, all_features, all_results, n_best_size, max_answer_length, do_lower_case):
+    """Get final predictions"""
+    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()
+
+    for (example_index, example) in enumerate(all_examples):
+        features = example_index_to_features[example_index]
+        prelim_predictions = get_prelim_predictions(features, unique_id_to_result, n_best_size, max_answer_length)
+        nbest = get_nbest(prelim_predictions, features, example, n_best_size, do_lower_case)
+
+        total_scores = []
+        best_non_null_entry = None
+        for entry in nbest:
+            total_scores.append(entry.start_logit + entry.end_logit)
+            if not best_non_null_entry:
+                if entry.text:
+                    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_logit"] = entry.start_logit
+            output["end_logit"] = entry.end_logit
+            nbest_json.append(output)
+
+        assert len(nbest_json) >= 1
+
+        all_predictions[example.qas_id] = nbest_json[0]["text"]
+    return all_predictions
+
+
+def write_predictions(all_examples, all_features, all_results, n_best_size,
+                      max_answer_length, do_lower_case):
+    """Write final predictions to the json file and log-odds of null if needed."""
+
+    all_predictions = get_predictions(all_examples, all_features, all_results,
+                                      n_best_size, max_answer_length, do_lower_case)
+    return all_predictions
+
+
+def get_final_text(pred_text, orig_text, do_lower_case):
+    """Project the tokenized prediction back to the original text."""
+    def _strip_spaces(text):
+        ns_chars = []
+        ns_to_s_map = collections.OrderedDict()
+        for (i, c) in enumerate(text):
+            if c == " ":
+                continue
+            ns_to_s_map[len(ns_chars)] = i
+            ns_chars.append(c)
+        ns_text = "".join(ns_chars)
+        return (ns_text, ns_to_s_map)
+
+    tokenizer = tokenization.BasicTokenizer(do_lower_case=do_lower_case)
+    tok_text = " ".join(tokenizer.tokenize(orig_text))
+
+    start_position = tok_text.find(pred_text)
+    if start_position == -1:
+        return orig_text
+    end_position = start_position + len(pred_text) - 1
+
+    (orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text)
+    (tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text)
+
+    if len(orig_ns_text) != len(tok_ns_text):
+        return orig_text
+
+    tok_s_to_ns_map = {}
+    for (i, tok_index) in six.iteritems(tok_ns_to_s_map):
+        tok_s_to_ns_map[tok_index] = i
+
+    orig_start_position = None
+    if start_position in tok_s_to_ns_map:
+        ns_start_position = tok_s_to_ns_map[start_position]
+        if ns_start_position in orig_ns_to_s_map:
+            orig_start_position = orig_ns_to_s_map[ns_start_position]
+
+    if orig_start_position is None:
+        return orig_text
+
+    orig_end_position = None
+    if end_position in tok_s_to_ns_map:
+        ns_end_position = tok_s_to_ns_map[end_position]
+        if ns_end_position in orig_ns_to_s_map:
+            orig_end_position = orig_ns_to_s_map[ns_end_position]
+
+    if orig_end_position is None:
+        return orig_text
+
+    output_text = orig_text[orig_start_position:(orig_end_position + 1)]
+    return output_text
+
+
+def _get_best_indexes(logits, n_best_size):
+    """Get the n-best logits from a list."""
+    index_and_score = sorted(enumerate(logits), key=lambda x: x[1], reverse=True)
+
+    best_indexes = []
+    for (i, score) in enumerate(index_and_score):
+        if i >= n_best_size:
+            break
+        best_indexes.append(score[0])
+    return best_indexes
+
+
+def _compute_softmax(scores):
+    """Compute softmax probability over raw logits."""
+    if not scores:
+        return []
+
+    max_score = None
+    for score in scores:
+        if max_score is None or score > max_score:
+            max_score = score
+
+    exp_scores = []
+    total_sum = 0.0
+    for score in scores:
+        x = math.exp(score - max_score)
+        exp_scores.append(x)
+        total_sum += x
+
+    probs = []
+    for score in exp_scores:
+        probs.append(score / total_sum)
+    return probs

model_zoo/official/nlp/bert/src/squad_postprocess.py

@@ -0,0 +1,97 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+
+"""evaluation script for SQuAD v1.1"""
+
+from collections import Counter
+import string
+import re
+import json
+import sys
+
+def normalize_answer(s):
+    """Lower text and remove punctuation, articles and extra whitespace."""
+    def remove_articles(text):
+        return re.sub(r'\b(a|an|the)\b', ' ', text)
+
+    def white_space_fix(text):
+        return ' '.join(text.split())
+
+    def remove_punc(text):
+        exclude = set(string.punctuation)
+        return ''.join(ch for ch in text if ch not in exclude)
+
+    def lower(text):
+        return text.lower()
+
+    return white_space_fix(remove_articles(remove_punc(lower(s))))
+
+def f1_score(prediction, ground_truth):
+    """calculate f1 score"""
+    prediction_tokens = normalize_answer(prediction).split()
+    ground_truth_tokens = normalize_answer(ground_truth).split()
+    common = Counter(prediction_tokens) & Counter(ground_truth_tokens)
+    num_same = sum(common.values())
+    if num_same == 0:
+        return 0
+    precision = 1.0 * num_same / len(prediction_tokens)
+    recall = 1.0 * num_same / len(ground_truth_tokens)
+    f1 = (2 * precision * recall) / (precision + recall)
+    return f1
+
+def exact_match_score(prediction, ground_truth):
+    return normalize_answer(prediction) == normalize_answer(ground_truth)
+
+def metric_max_over_ground_truths(metric_fn, prediction, ground_truths):
+    scores_for_ground_truths = []
+    for ground_truth in ground_truths:
+        score = metric_fn(prediction, ground_truth)
+        scores_for_ground_truths.append(score)
+    return max(scores_for_ground_truths)
+
+def evaluate(dataset, predictions):
+    """do evaluation"""
+    f1 = exact_match = total = 0
+    for article in dataset:
+        for paragraph in article['paragraphs']:
+            for qa in paragraph['qas']:
+                total += 1
+                if qa['id'] not in predictions:
+                    message = 'Unanswered question ' + qa['id'] + \
+                              ' will receive score 0.'
+                    print(message, file=sys.stderr)
+                    continue
+                ground_truths = list(map(lambda x: x['text'], qa['answers']))
+                if not ground_truths:
+                    continue
+                prediction = predictions[qa['id']]
+                exact_match += metric_max_over_ground_truths(
+                    exact_match_score, prediction, ground_truths)
+                f1 += metric_max_over_ground_truths(
+                    f1_score, prediction, ground_truths)
+
+    exact_match = 100.0 * exact_match / total
+    f1 = 100.0 * f1 / total
+    return {'exact_match': exact_match, 'f1': f1}
+
+
+def SQuad_postprocess(dataset_file, all_predictions, output_metrics="output.json"):
+    with open(dataset_file) as ds:
+        dataset_json = json.load(ds)
+        dataset = dataset_json['data']
+    re_json = evaluate(dataset, all_predictions)
+    print(json.dumps(re_json))
+    with open(output_metrics, 'w') as wr:
+        wr.write(json.dumps(re_json))