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Update pipelines.py

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Modified QA pipeline to consider all features for each example before generating topk answers. 
Current pipeline only takes one SquadExample, one SquadFeature, one start logit list, one end logit list to retrieve the answer, this is not correct as one SquadExample can produce multiple SquadFeatures.
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Rishabh Manoj 2020-01-08 21:12:34 +05:30 committed by GitHub
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commit f26a353057
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1 changed files with 53 additions and 43 deletions
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96
src/transformers/pipelines.py
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@ -705,55 +705,65 @@ class QuestionAnsweringPipeline(Pipeline):
# Convert inputs to features
examples = self._args_parser(*texts, **kwargs)
features = squad_convert_examples_to_features(
examples, self.tokenizer, kwargs["max_seq_len"], kwargs["doc_stride"], kwargs["max_question_len"], False
)
fw_args = self.inputs_for_model([f.__dict__ for f in features])
features_list = [ squad_convert_examples_to_features(
[example],
self.tokenizer,
kwargs["max_seq_len"],
kwargs["doc_stride"],
kwargs["max_question_len"],
False
) for example in examples ]
all_answers = []
for features, example in zip(features_list, examples):
fw_args = self.inputs_for_model([f.__dict__ for f in features])
# Manage tensor allocation on correct device
with self.device_placement():
if self.framework == "tf":
fw_args = {k: tf.constant(v) for (k, v) in fw_args.items()}
start, end = self.model(fw_args)
start, end = start.numpy(), end.numpy()
else:
with torch.no_grad():
# Retrieve the score for the context tokens only (removing question tokens)
fw_args = {k: torch.tensor(v, device=self.device) for (k, v) in fw_args.items()}
start, end = self.model(**fw_args)
start, end = start.cpu().numpy(), end.cpu().numpy()
# Manage tensor allocation on correct device
with self.device_placement():
if self.framework == "tf":
fw_args = {k: tf.constant(v) for (k, v) in fw_args.items()}
start, end = self.model(fw_args)
start, end = start.numpy(), end.numpy()
else:
with torch.no_grad():
# Retrieve the score for the context tokens only (removing question tokens)
fw_args = {k: torch.tensor(v, device=self.device) for (k, v) in fw_args.items()}
start, end = self.model(**fw_args)
start, end = start.cpu().numpy(), end.cpu().numpy()
answers = []
for (example, feature, start_, end_) in zip(examples, features, start, end):
# Normalize logits and spans to retrieve the answer
start_ = np.exp(start_) / np.sum(np.exp(start_))
end_ = np.exp(end_) / np.sum(np.exp(end_))
answers = []
for (feature, start_, end_) in zip(features, start, end):
# Normalize logits and spans to retrieve the answer
start_ = np.exp(start_) / np.sum(np.exp(start_))
end_ = np.exp(end_) / np.sum(np.exp(end_))
# Mask padding and question
start_, end_ = start_ * np.abs(np.array(feature.p_mask) - 1), end_ * np.abs(np.array(feature.p_mask) - 1)
# Mask padding and question
start_, end_ = start_ * np.abs(np.array(feature.p_mask) - 1), end_ * np.abs(np.array(feature.p_mask) - 1)
# TODO : What happens if not possible
# Mask CLS
start_[0] = end_[0] = 0
# TODO : What happens if not possible
# Mask CLS
start_[0] = end_[0] = 0
starts, ends, scores = self.decode(start_, end_, kwargs["topk"], kwargs["max_answer_len"])
char_to_word = np.array(example.char_to_word_offset)
starts, ends, scores = self.decode(start_, end_, kwargs["topk"], kwargs["max_answer_len"])
char_to_word = np.array(example.char_to_word_offset)
# Convert the answer (tokens) back to the original text
answers += [
{
"score": score.item(),
"start": np.where(char_to_word == feature.token_to_orig_map[s])[0][0].item(),
"end": np.where(char_to_word == feature.token_to_orig_map[e])[0][-1].item(),
"answer": " ".join(
example.doc_tokens[feature.token_to_orig_map[s] : feature.token_to_orig_map[e] + 1]
),
}
for s, e, score in zip(starts, ends, scores)
]
if len(answers) == 1:
return answers[0]
return answers
# Convert the answer (tokens) back to the original text
answers += [
{
"score": score.item(),
"start": np.where(char_to_word == feature.token_to_orig_map[s])[0][0].item(),
"end": np.where(char_to_word == feature.token_to_orig_map[e])[0][-1].item(),
"answer": " ".join(
example.doc_tokens[feature.token_to_orig_map[s] : feature.token_to_orig_map[e] + 1]
),
}
for s, e, score in zip(starts, ends, scores)
]
answers = sorted(answers, key = lambda x:x['score'], reverse=True)[:kwargs["topk"]]
all_answers+=answers
if len(all_answers) == 1:
return all_answers[0]
return all_answers
def decode(self, start: np.ndarray, end: np.ndarray, topk: int, max_answer_len: int) -> Tuple:
"""