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[GPT2, CTRL] Allow input of input_ids and past of variable length (#4581)

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* revert convenience  method

* clean docs a bit
This commit is contained in:
Patrick von Platen
2020-05-26 19:43:58 +02:00
committed by GitHub
parent 5ddd8d6531
commit 003c477129
4 changed files with 25 additions and 57 deletions
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14
src/transformers/modeling_tf_ctrl.py
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@@ -411,9 +411,12 @@ CTRL_START_DOCSTRING = r"""
CTRL_INPUTS_DOCSTRING = r"""
Args:
input_ids (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`):
input_ids (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, input_ids_length)`):
:obj:`input_ids_length` = ``sequence_length`` if ``past`` is ``None`` else ``past[0].shape[-2]`` (``sequence_length`` of input past key value states).
Indices of input sequence tokens in the vocabulary.
If `past` is used, optionally only the last `input_ids` have to be input (see `past`).
If `past` is used, only input_ids that do not have their past calculated should be passed as input_ids (see `past`).
Indices can be obtained using :class:`transformers.CTRLTokenizer`.
See :func:`transformers.PreTrainedTokenizer.encode` and
@@ -423,9 +426,8 @@ CTRL_INPUTS_DOCSTRING = r"""
past (:obj:`List[tf.Tensor]` of length :obj:`config.n_layers`):
Contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
(see `past` output below). Can be used to speed up sequential decoding.
If `past` is used, the user can optionally input only the last `input_ids`
(those that don't have their past given to this model) of shape :obj:`(batch_size, 1)`
instead of all `input_ids` of shape :obj:`(batch_size, sequence_length)`.
The token ids which have their past given to this model
should not be passed as input ids as they have already been computed.
attention_mask (:obj:`tf.Tensor` or :obj:`Numpy array` 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]``:
@@ -436,7 +438,6 @@ CTRL_INPUTS_DOCSTRING = r"""
Segment token indices to indicate first and second portions of the inputs.
Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1``
corresponds to a `sentence B` token
If `past` is used, optionally only the last `token_type_ids` have to be input (see `past`).
`What are token type IDs? <../glossary.html#token-type-ids>`_
position_ids (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
@@ -452,7 +453,6 @@ CTRL_INPUTS_DOCSTRING = r"""
Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
If `past` is used, optionally only the last `input_embeds` have to be input (see `past`).
use_cache (:obj:`bool`):
If `use_cache` is True, `past` key value states are returned and
can be used to speed up decoding (see `past`). Defaults to `True`.