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re-add scoring filtering

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Patrick von Platen 2020-03-10 16:53:09 +01:00
parent 9b8ee8cea0
commit 7351a8dbaf
2 changed files with 8 additions and 10 deletions
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15
src/transformers/modeling_utils.py
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@ -1084,10 +1084,9 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
next_token_logits = next_token_logits / temperature
scores = F.log_softmax(next_token_logits, dim=-1) # (batch_size * num_beams, vocab_size)
# if (
# self.config.is_encoder_decoder and do_sample is False
# ): # TODO(PVP) to be refactored later - do we need this boolean flag here? Also Only add for beam_search or also for no_beam_search? The prepare scores fn is ugly here
# scores = self.prepare_scores_for_generation(scores, cur_len, max_length)
if self.config.is_encoder_decoder and do_sample is False:
# TODO(PVP) to be refactored later - do we need this boolean flag here? Also Only add for beam_search or also for no_beam_search? The prepare scores fn is ugly here
scores = self.prepare_scores_for_generation(scores, cur_len, max_length)
# set eos token prob to zero if min_length is not reached
if eos_token_ids is not None and cur_len < min_length:
@ -1279,10 +1278,10 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
assert (len(hypo) == max_length for hypo in best)
decoded = torch.stack(best).type(torch.long).to(next(self.parameters()).device)
# if self.config.is_encoder_decoder:
# do not return first <EOS> token
# return decoded[:, 1:]
return decoded
if self.config.is_encoder_decoder:
# do not return first <EOS> token
return decoded[:, 1:]
# return decoded
# force one of token_ids to be generated by setting prob of all other tokens to 0.
def _force_token_ids_generation(self, scores, token_ids):

3
tests/test_modeling_bart.py
View file

@ -471,8 +471,7 @@ class BartModelIntegrationTest(unittest.TestCase):
attention_mask=dct["attention_mask"].to(torch_device),
num_beams=4,
length_penalty=2.0,
# max_length=max_length + 2,
max_length=max_length + 1,
max_length=max_length + 2,
min_length=min_length + 1,
no_repeat_ngram_size=3,
do_sample=False,