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[T5, TF 2.2] change tf t5 argument naming (#3547)

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* change tf t5 argument naming for TF 2.2

* correct bug in testing
This commit is contained in:
Patrick von Platen
2020-04-01 22:04:20 +02:00
committed by GitHub
parent 06dd597552
commit a4ee4da18a
3 changed files with 36 additions and 25 deletions
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33
src/transformers/modeling_tf_t5.py
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@@ -592,8 +592,8 @@ class TFT5PreTrainedModel(TFPreTrainedModel):
input_ids = tf.constant(DUMMY_INPUTS)
input_mask = tf.constant(DUMMY_MASK)
dummy_inputs = {
"inputs": input_ids,
"decoder_input_ids": input_ids,
"input_ids": input_ids,
"decoder_attention_mask": input_mask,
}
return dummy_inputs
@@ -637,11 +637,9 @@ T5_START_DOCSTRING = r""" The T5 model was proposed in
T5_INPUTS_DOCSTRING = r"""
Args:
decoder_input_ids are usually used as a `dict` (see T5 description above for more information) containing all the following.
decoder_input_ids (:obj:`tf.Tensor` of shape :obj:`(batch_size, target_sequence_length)`, `optional`, defaults to :obj:`None`):
Provide for sequence to sequence training. T5 uses the pad_token_id as the starting token for decoder_input_ids generation.
inputs are usually used as a `dict` (see T5 description above for more information) containing all the following.
input_ids (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`):
inputs (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary.
T5 is a model with relative position embeddings so you should be able to pad the inputs on
the right or the left.
@@ -650,6 +648,8 @@ T5_INPUTS_DOCSTRING = r"""
`T5 Training <./t5.html#training>`_ .
See :func:`transformers.PreTrainedTokenizer.encode` and
:func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
decoder_input_ids (:obj:`tf.Tensor` of shape :obj:`(batch_size, target_sequence_length)`, `optional`, defaults to :obj:`None`):
Provide for sequence to sequence training. T5 uses the pad_token_id as the starting token for decoder_input_ids generation.
attention_mask (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
Mask to avoid performing attention on padding token indices.
Mask values selected in ``[0, 1]``:
@@ -706,7 +706,7 @@ class TFT5Model(TFT5PreTrainedModel):
return self.shared
@add_start_docstrings_to_callable(T5_INPUTS_DOCSTRING)
def call(self, decoder_input_ids, **kwargs):
def call(self, inputs, **kwargs):
r"""
Return:
:obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.T5Config`) and inputs.
@@ -736,13 +736,13 @@ class TFT5Model(TFT5PreTrainedModel):
"""
if isinstance(decoder_input_ids, dict):
kwargs.update(decoder_input_ids)
if isinstance(inputs, dict):
kwargs.update(inputs)
else:
kwargs["decoder_input_ids"] = decoder_input_ids
kwargs["inputs"] = inputs
# retrieve arguments
input_ids = kwargs.get("input_ids", None)
input_ids = kwargs.get("inputs", None)
decoder_input_ids = kwargs.get("decoder_input_ids", None)
attention_mask = kwargs.get("attention_mask", None)
encoder_outputs = kwargs.get("encoder_outputs", None)
@@ -803,7 +803,7 @@ class TFT5ForConditionalGeneration(TFT5PreTrainedModel):
return self.encoder
@add_start_docstrings_to_callable(T5_INPUTS_DOCSTRING)
def call(self, decoder_input_ids, **kwargs):
def call(self, inputs, **kwargs):
r"""
Return:
:obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.T5Config`) and inputs.
@@ -839,13 +839,13 @@ class TFT5ForConditionalGeneration(TFT5PreTrainedModel):
"""
if isinstance(decoder_input_ids, dict):
kwargs.update(decoder_input_ids)
if isinstance(inputs, dict):
kwargs.update(inputs)
else:
kwargs["decoder_input_ids"] = decoder_input_ids
kwargs["inputs"] = inputs
# retrieve arguments
input_ids = kwargs.get("input_ids", None)
input_ids = kwargs.get("inputs", None)
decoder_input_ids = kwargs.get("decoder_input_ids", None)
attention_mask = kwargs.get("attention_mask", None)
encoder_outputs = kwargs.get("encoder_outputs", None)
@@ -890,7 +890,8 @@ class TFT5ForConditionalGeneration(TFT5PreTrainedModel):
encoder_outputs = (past,)
return {
"inputs": input_ids,
"inputs": None, # inputs don't have to be defined, but still need to be passed to make Keras.layer.__call__ happy
"decoder_input_ids": input_ids, # input_ids are the decoder_input_ids
"encoder_outputs": encoder_outputs,
"attention_mask": attention_mask,
}