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[Generate] Facilitate PyTorch generate using ModelOutputs (#6735)

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* fix generate for GPT2 Double Head

* fix gpt2 double head model

* fix  bart / t5

* also add for no beam search

* fix no beam search

* fix encoder decoder

* simplify t5

* simplify t5

* fix t5 tests

* fix BART

* fix transfo-xl

* fix conflict

* integrating sylvains and sams comments

* fix tf past_decoder_key_values

* fix enc dec test
This commit is contained in:
Patrick von Platen
2020-09-01 12:38:25 +02:00
committed by GitHub
parent 397f819615
commit afc4ece462
20 changed files with 393 additions and 259 deletions
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26
src/transformers/modeling_tf_outputs.py
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@@ -113,13 +113,13 @@ class TFSeq2SeqModelOutput(ModelOutput):
last_hidden_state (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the decoder of the model.
If ``decoder_past_key_values`` is used only the last hidden-state of the sequences of shape :obj:`(batch_size, 1, hidden_size)` is output.
decoder_past_key_values (:obj:`List[tf.Tensor]`, `optional`, returned when ``use_cache=True`` is passed or when ``config.use_cache=True``):
If ``past_key_values`` is used only the last hidden-state of the sequences of shape :obj:`(batch_size, 1, hidden_size)` is output.
past_key_values (:obj:`List[tf.Tensor]`, `optional`, returned when ``use_cache=True`` is passed or when ``config.use_cache=True``):
List of :obj:`tf.Tensor` of length :obj:`config.n_layers`, with each tensor of shape
:obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`).
Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see ``decoder_past_key_values`` input) to speed up sequential decoding.
used (see ``past_key_values`` input) to speed up sequential decoding.
decoder_hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
@@ -147,7 +147,7 @@ class TFSeq2SeqModelOutput(ModelOutput):
"""
last_hidden_state: tf.Tensor = None
decoder_past_key_values: Optional[List[tf.Tensor]] = None
past_key_values: Optional[List[tf.Tensor]] = None
decoder_hidden_states: Optional[Tuple[tf.Tensor]] = None
decoder_attentions: Optional[Tuple[tf.Tensor]] = None
encoder_last_hidden_state: Optional[tf.Tensor] = None
@@ -259,12 +259,12 @@ class TFSeq2SeqLMOutput(ModelOutput):
Languaged modeling loss.
logits (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
decoder_past_key_values (:obj:`List[tf.Tensor]`, `optional`, returned when ``use_cache=True`` is passed or when ``config.use_cache=True``):
past_key_values (:obj:`List[tf.Tensor]`, `optional`, returned when ``use_cache=True`` is passed or when ``config.use_cache=True``):
List of :obj:`tf.Tensor` of length :obj:`config.n_layers`, with each tensor of shape
:obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`).
Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see ``decoder_past_key_values`` input) to speed up sequential decoding.
used (see ``past_key_values`` input) to speed up sequential decoding.
decoder_hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
@@ -293,7 +293,7 @@ class TFSeq2SeqLMOutput(ModelOutput):
loss: Optional[tf.Tensor] = None
logits: tf.Tensor = None
decoder_past_key_values: Optional[List[tf.Tensor]] = None
past_key_values: Optional[List[tf.Tensor]] = None
decoder_hidden_states: Optional[Tuple[tf.Tensor]] = None
decoder_attentions: Optional[Tuple[tf.Tensor]] = None
encoder_last_hidden_state: Optional[tf.Tensor] = None
@@ -366,12 +366,12 @@ class TFSeq2SeqSequenceClassifierOutput(ModelOutput):
Classification (or regression if config.num_labels==1) loss.
logits (:obj:`tf.Tensor` of shape :obj:`(batch_size, config.num_labels)`):
Classification (or regression if config.num_labels==1) scores (before SoftMax).
decoder_past_key_values (:obj:`List[tf.Tensor]`, `optional`, returned when ``use_cache=True`` is passed or when ``config.use_cache=True``):
past_key_values (:obj:`List[tf.Tensor]`, `optional`, returned when ``use_cache=True`` is passed or when ``config.use_cache=True``):
List of :obj:`tf.Tensor` of length :obj:`config.n_layers`, with each tensor of shape
:obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`).
Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see ``decoder_past_key_values`` input) to speed up sequential decoding.
used (see ``past_key_values`` input) to speed up sequential decoding.
decoder_hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
@@ -400,7 +400,7 @@ class TFSeq2SeqSequenceClassifierOutput(ModelOutput):
loss: Optional[tf.Tensor] = None
logits: tf.Tensor = None
decoder_past_key_values: Optional[List[tf.Tensor]] = None
past_key_values: Optional[List[tf.Tensor]] = None
decoder_hidden_states: Optional[Tuple[tf.Tensor]] = None
decoder_attentions: Optional[Tuple[tf.Tensor]] = None
encoder_last_hidden_state: Optional[tf.Tensor] = None
@@ -512,12 +512,12 @@ class TFSeq2SeqQuestionAnsweringModelOutput(ModelOutput):
Span-start scores (before SoftMax).
end_logits (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length,)`):
Span-end scores (before SoftMax).
decoder_past_key_values (:obj:`List[tf.Tensor]`, `optional`, returned when ``use_cache=True`` is passed or when ``config.use_cache=True``):
past_key_values (:obj:`List[tf.Tensor]`, `optional`, returned when ``use_cache=True`` is passed or when ``config.use_cache=True``):
List of :obj:`tf.Tensor` of length :obj:`config.n_layers`, with each tensor of shape
:obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`).
Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see ``decoder_past_key_values`` input) to speed up sequential decoding.
used (see ``past_key_values`` input) to speed up sequential decoding.
decoder_hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
@@ -547,7 +547,7 @@ class TFSeq2SeqQuestionAnsweringModelOutput(ModelOutput):
loss: Optional[tf.Tensor] = None
start_logits: tf.Tensor = None
end_logits: tf.Tensor = None
decoder_past_key_values: Optional[List[tf.Tensor]] = None
past_key_values: Optional[List[tf.Tensor]] = None
decoder_hidden_states: Optional[Tuple[tf.Tensor]] = None
decoder_attentions: Optional[Tuple[tf.Tensor]] = None
encoder_last_hidden_state: Optional[tf.Tensor] = None