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Extend config with task specific configs. (#3433)

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* add new default configs

* change prefix default to None
This commit is contained in:
Patrick von Platen
2020-03-25 21:32:04 +01:00
committed by GitHub
parent 83272a3853
commit ffa17fe322
3 changed files with 27 additions and 13 deletions
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13
src/transformers/modeling_tf_utils.py
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@@ -610,7 +610,9 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin):
num_return_sequences = (
num_return_sequences if num_return_sequences is not None else self.config.num_return_sequences
)
decoder_start_token_id = decoder_start_token_id if decoder_start_token_id is not None else bos_token_id
decoder_start_token_id = (
decoder_start_token_id if decoder_start_token_id is not None else self.config.decoder_start_token_id
)
if input_ids is not None:
batch_size = shape_list(input_ids)[0] # overriden by the input batch_size
@@ -635,9 +637,6 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin):
assert (eos_token_id is None) or (
isinstance(eos_token_id, int) and (eos_token_id >= 0)
), "`eos_token_id` should be a positive integer."
assert (
decoder_start_token_id is not None or self.config.is_encoder_decoder is False
), "`decoder_start_token_id` has to be defined if model is encoder-decoder model"
assert length_penalty > 0, "`length_penalty` should be strictely positive."
assert (
isinstance(num_return_sequences, int) and num_return_sequences > 0
@@ -708,8 +707,12 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin):
) # shape: (batch_size * num_return_sequences * num_beams, cur_len)
if self.config.is_encoder_decoder:
if decoder_start_token_id is None:
decoder_start_token_id = bos_token_id
assert bos_token_id is not None, "Encoder Decoder Models need to have a bos_token_id"
assert (
decoder_start_token_id is not None
), "decoder_start_token_id or bos_token_id has to be defined for encoder-decoder generation"
assert hasattr(self, "get_encoder"), "{} should have a 'get_encoder' function defined".format(self)
assert callable(self.get_encoder), "{} should be a method".format(self.get_encoder)