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Unverified Commit f4e1f022 authored by Joseph Liu's avatar Joseph Liu Committed by GitHub
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Output hidden states (#4978) 



* Configure all models to use output_hidden_states as argument passed to foward()

* Pass all tests

* Remove cast_bool_to_primitive in TF Flaubert model

* correct tf xlnet

* add pytorch test

* add tf test

* Fix broken tests

* Configure all models to use output_hidden_states as argument passed to foward()

* Pass all tests

* Remove cast_bool_to_primitive in TF Flaubert model

* correct tf xlnet

* add pytorch test

* add tf test

* Fix broken tests

* Refactor output_hidden_states for mobilebert

* Reset and remerge to master
Co-authored-by: default avatarJoseph Liu <joseph.liu@coinflex.com>
Co-authored-by: default avatarpatrickvonplaten <patrick.v.platen@gmail.com>
parent 866a8cca
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Showing with 276 additions and 122 deletions
src/transformers/modeling_tf_flaubert.py
View file @ f4e1f022
...@@ -137,6 +137,7 @@ class TFFlaubertMainLayer(TFXLMMainLayer): ...@@ -137,6 +137,7 @@ class TFFlaubertMainLayer(TFXLMMainLayer):
inputs_embeds=None, inputs_embeds=None,
training=False, training=False,
output_attentions=False, output_attentions=False,
output_hidden_states=False,
): ):
# removed: src_enc=None, src_len=None # removed: src_enc=None, src_len=None
if isinstance(inputs, (tuple, list)): if isinstance(inputs, (tuple, list)):
...@@ -251,15 +252,14 @@ class TFFlaubertMainLayer(TFXLMMainLayer): ...@@ -251,15 +252,14 @@ class TFFlaubertMainLayer(TFXLMMainLayer):
if training and (dropout_probability < self.layerdrop): if training and (dropout_probability < self.layerdrop):
continue continue
if self.output_hidden_states: if output_hidden_states:
hidden_states = hidden_states + (tensor,) hidden_states = hidden_states + (tensor,)
# self attention # self attention
if not self.pre_norm: if not self.pre_norm:
attn_outputs = self.attentions[i]([tensor, attn_mask, None, cache, head_mask[i]], training=training) attn_outputs = self.attentions[i]([tensor, attn_mask, None, cache, head_mask[i]], training=training)
attn = attn_outputs[0] attn = attn_outputs[0]
if output_attentions: attentions = attentions + (attn_outputs[1],)
attentions = attentions + (attn_outputs[1],)
attn = self.dropout(attn, training=training) attn = self.dropout(attn, training=training)
tensor = tensor + attn tensor = tensor + attn
tensor = self.layer_norm1[i](tensor) tensor = self.layer_norm1[i](tensor)
...@@ -292,7 +292,7 @@ class TFFlaubertMainLayer(TFXLMMainLayer): ...@@ -292,7 +292,7 @@ class TFFlaubertMainLayer(TFXLMMainLayer):
tensor = tensor * mask[..., tf.newaxis] tensor = tensor * mask[..., tf.newaxis]
# Add last hidden state # Add last hidden state
if self.output_hidden_states: if output_hidden_states:
hidden_states = hidden_states + (tensor,) hidden_states = hidden_states + (tensor,)
# update cache length # update cache length
...@@ -303,7 +303,7 @@ class TFFlaubertMainLayer(TFXLMMainLayer): ...@@ -303,7 +303,7 @@ class TFFlaubertMainLayer(TFXLMMainLayer):
# tensor = tensor.transpose(0, 1) # tensor = tensor.transpose(0, 1)
outputs = (tensor,) outputs = (tensor,)
if self.output_hidden_states: if output_hidden_states:
outputs = outputs + (hidden_states,) outputs = outputs + (hidden_states,)
if output_attentions: if output_attentions:
outputs = outputs + (attentions,) outputs = outputs + (attentions,)
......
src/transformers/modeling_tf_gpt2.py
View file @ f4e1f022
...@@ -257,6 +257,7 @@ class TFGPT2MainLayer(tf.keras.layers.Layer): ...@@ -257,6 +257,7 @@ class TFGPT2MainLayer(tf.keras.layers.Layer):
use_cache=True, use_cache=True,
training=False, training=False,
output_attentions=None, output_attentions=None,
output_hidden_states=None,
): ):
if isinstance(inputs, (tuple, list)): if isinstance(inputs, (tuple, list)):
input_ids = inputs[0] input_ids = inputs[0]
...@@ -268,7 +269,8 @@ class TFGPT2MainLayer(tf.keras.layers.Layer): ...@@ -268,7 +269,8 @@ class TFGPT2MainLayer(tf.keras.layers.Layer):
inputs_embeds = inputs[6] if len(inputs) > 6 else inputs_embeds inputs_embeds = inputs[6] if len(inputs) > 6 else inputs_embeds
use_cache = inputs[7] if len(inputs) > 7 else use_cache use_cache = inputs[7] if len(inputs) > 7 else use_cache
output_attentions = inputs[8] if len(inputs) > 7 else output_attentions output_attentions = inputs[8] if len(inputs) > 7 else output_attentions
assert len(inputs) <= 9, "Too many inputs." output_hidden_states = inputs[9] if len(inputs) > 8 else output_hidden_states
assert len(inputs) <= 10, "Too many inputs."
elif isinstance(inputs, (dict, BatchEncoding)): elif isinstance(inputs, (dict, BatchEncoding)):
input_ids = inputs.get("input_ids") input_ids = inputs.get("input_ids")
past = inputs.get("past", past) past = inputs.get("past", past)
...@@ -279,11 +281,13 @@ class TFGPT2MainLayer(tf.keras.layers.Layer): ...@@ -279,11 +281,13 @@ class TFGPT2MainLayer(tf.keras.layers.Layer):
inputs_embeds = inputs.get("inputs_embeds", inputs_embeds) inputs_embeds = inputs.get("inputs_embeds", inputs_embeds)
use_cache = inputs.get("use_cache", use_cache) use_cache = inputs.get("use_cache", use_cache)
output_attentions = inputs.get("output_attentions", output_attentions) output_attentions = inputs.get("output_attentions", output_attentions)
assert len(inputs) <= 9, "Too many inputs." output_hidden_states = inputs.get("output_hidden_states", output_hidden_states)
assert len(inputs) <= 10, "Too many inputs."
else: else:
input_ids = inputs input_ids = inputs
output_attentions = output_attentions if output_attentions is not None else self.output_attentions output_attentions = output_attentions if output_attentions is not None else self.output_attentions
output_hidden_states = output_hidden_states if output_hidden_states is not None else self.output_hidden_states
if input_ids is not None and inputs_embeds is not None: if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
...@@ -352,7 +356,7 @@ class TFGPT2MainLayer(tf.keras.layers.Layer): ...@@ -352,7 +356,7 @@ class TFGPT2MainLayer(tf.keras.layers.Layer):
all_attentions = [] all_attentions = []
all_hidden_states = () all_hidden_states = ()
for i, (block, layer_past) in enumerate(zip(self.h, past)): for i, (block, layer_past) in enumerate(zip(self.h, past)):
if self.output_hidden_states: if cast_bool_to_primitive(output_hidden_states) is True:
all_hidden_states = all_hidden_states + (tf.reshape(hidden_states, output_shape),) all_hidden_states = all_hidden_states + (tf.reshape(hidden_states, output_shape),)
outputs = block( outputs = block(
...@@ -370,14 +374,14 @@ class TFGPT2MainLayer(tf.keras.layers.Layer): ...@@ -370,14 +374,14 @@ class TFGPT2MainLayer(tf.keras.layers.Layer):
hidden_states = tf.reshape(hidden_states, output_shape) hidden_states = tf.reshape(hidden_states, output_shape)
# Add last hidden state # Add last hidden state
if self.output_hidden_states: if cast_bool_to_primitive(output_hidden_states) is True:
all_hidden_states = all_hidden_states + (hidden_states,) all_hidden_states = all_hidden_states + (hidden_states,)
outputs = (hidden_states,) outputs = (hidden_states,)
if use_cache is True: if use_cache is True:
outputs = outputs + (presents,) outputs = outputs + (presents,)
if self.output_hidden_states: if cast_bool_to_primitive(output_hidden_states) is True:
outputs = outputs + (all_hidden_states,) outputs = outputs + (all_hidden_states,)
if cast_bool_to_primitive(output_attentions) is True: if cast_bool_to_primitive(output_attentions) is True:
# let the number of heads free (-1) so we can extract attention even after head pruning # let the number of heads free (-1) so we can extract attention even after head pruning
...@@ -493,7 +497,7 @@ class TFGPT2Model(TFGPT2PreTrainedModel): ...@@ -493,7 +497,7 @@ class TFGPT2Model(TFGPT2PreTrainedModel):
Contains pre-computed hidden-states (key and values in the attention blocks). Contains pre-computed hidden-states (key and values in the attention blocks).
Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model
should not be passed as input ids as they have already been computed. should not be passed as input ids as they have already been computed.
hidden_states (:obj:`tuple(tf.Tensor)` `optional`, returned when ``config.output_hidden_states=True``): 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) 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)`. of shape :obj:`(batch_size, sequence_length, hidden_size)`.
...@@ -552,7 +556,7 @@ class TFGPT2LMHeadModel(TFGPT2PreTrainedModel): ...@@ -552,7 +556,7 @@ class TFGPT2LMHeadModel(TFGPT2PreTrainedModel):
Contains pre-computed hidden-states (key and values in the attention blocks). Contains pre-computed hidden-states (key and values in the attention blocks).
Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model
should not be passed as input ids as they have already been computed. should not be passed as input ids as they have already been computed.
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_hidden_states=True``): 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) 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)`. of shape :obj:`(batch_size, sequence_length, hidden_size)`.
...@@ -620,6 +624,7 @@ class TFGPT2DoubleHeadsModel(TFGPT2PreTrainedModel): ...@@ -620,6 +624,7 @@ class TFGPT2DoubleHeadsModel(TFGPT2PreTrainedModel):
mc_token_ids=None, mc_token_ids=None,
use_cache=True, use_cache=True,
output_attentions=None, output_attentions=None,
output_hidden_states=None,
training=False, training=False,
): ):
r""" r"""
...@@ -637,7 +642,7 @@ class TFGPT2DoubleHeadsModel(TFGPT2PreTrainedModel): ...@@ -637,7 +642,7 @@ class TFGPT2DoubleHeadsModel(TFGPT2PreTrainedModel):
Contains pre-computed hidden-states (key and values in the attention blocks). Contains pre-computed hidden-states (key and values in the attention blocks).
Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model
should not be passed as `input_ids` as they have already been computed. should not be passed as `input_ids` as they have already been computed.
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_hidden_states=True``): 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) 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)`. of shape :obj:`(batch_size, sequence_length, hidden_size)`.
...@@ -726,6 +731,7 @@ class TFGPT2DoubleHeadsModel(TFGPT2PreTrainedModel): ...@@ -726,6 +731,7 @@ class TFGPT2DoubleHeadsModel(TFGPT2PreTrainedModel):
inputs_embeds, inputs_embeds,
use_cache, use_cache,
output_attentions, output_attentions,
output_hidden_states,
] ]
transformer_outputs = self.transformer(flat_inputs, training=training) transformer_outputs = self.transformer(flat_inputs, training=training)
......
src/transformers/modeling_tf_mobilebert.py
View file @ f4e1f022
...@@ -508,16 +508,15 @@ class TFMobileBertLayer(tf.keras.layers.Layer): ...@@ -508,16 +508,15 @@ class TFMobileBertLayer(tf.keras.layers.Layer):
class TFMobileBertEncoder(tf.keras.layers.Layer): class TFMobileBertEncoder(tf.keras.layers.Layer):
def __init__(self, config, **kwargs): def __init__(self, config, **kwargs):
super().__init__(**kwargs) super().__init__(**kwargs)
self.output_hidden_states = config.output_hidden_states
self.layer = [TFMobileBertLayer(config, name="layer_._{}".format(i)) for i in range(config.num_hidden_layers)] self.layer = [TFMobileBertLayer(config, name="layer_._{}".format(i)) for i in range(config.num_hidden_layers)]
def call(self, inputs, training=False): def call(self, inputs, training=False):
hidden_states, attention_mask, head_mask, output_attentions = inputs hidden_states, attention_mask, head_mask, output_attentions, output_hidden_states = inputs
all_hidden_states = () all_hidden_states = ()
all_attentions = () all_attentions = ()
for i, layer_module in enumerate(self.layer): for i, layer_module in enumerate(self.layer):
if self.output_hidden_states: if cast_bool_to_primitive(output_hidden_states) is True:
all_hidden_states = all_hidden_states + (hidden_states,) all_hidden_states = all_hidden_states + (hidden_states,)
layer_outputs = layer_module( layer_outputs = layer_module(
...@@ -529,11 +528,11 @@ class TFMobileBertEncoder(tf.keras.layers.Layer): ...@@ -529,11 +528,11 @@ class TFMobileBertEncoder(tf.keras.layers.Layer):
all_attentions = all_attentions + (layer_outputs[1],) all_attentions = all_attentions + (layer_outputs[1],)
# Add last layer # Add last layer
if self.output_hidden_states: if cast_bool_to_primitive(output_hidden_states) is True:
all_hidden_states = all_hidden_states + (hidden_states,) all_hidden_states = all_hidden_states + (hidden_states,)
outputs = (hidden_states,) outputs = (hidden_states,)
if self.output_hidden_states: if cast_bool_to_primitive(output_hidden_states) is True:
outputs = outputs + (all_hidden_states,) outputs = outputs + (all_hidden_states,)
if cast_bool_to_primitive(output_attentions) is True: if cast_bool_to_primitive(output_attentions) is True:
outputs = outputs + (all_attentions,) outputs = outputs + (all_attentions,)
...@@ -643,6 +642,7 @@ class TFMobileBertMainLayer(tf.keras.layers.Layer): ...@@ -643,6 +642,7 @@ class TFMobileBertMainLayer(tf.keras.layers.Layer):
super().__init__(**kwargs) super().__init__(**kwargs)
self.num_hidden_layers = config.num_hidden_layers self.num_hidden_layers = config.num_hidden_layers
self.output_attentions = config.output_attentions self.output_attentions = config.output_attentions
self.output_hidden_states = config.output_hidden_states
self.embeddings = TFMobileBertEmbeddings(config, name="embeddings") self.embeddings = TFMobileBertEmbeddings(config, name="embeddings")
self.encoder = TFMobileBertEncoder(config, name="encoder") self.encoder = TFMobileBertEncoder(config, name="encoder")
...@@ -670,6 +670,7 @@ class TFMobileBertMainLayer(tf.keras.layers.Layer): ...@@ -670,6 +670,7 @@ class TFMobileBertMainLayer(tf.keras.layers.Layer):
head_mask=None, head_mask=None,
inputs_embeds=None, inputs_embeds=None,
output_attentions=None, output_attentions=None,
output_hidden_states=None,
training=False, training=False,
): ):
if isinstance(inputs, (tuple, list)): if isinstance(inputs, (tuple, list)):
...@@ -680,7 +681,8 @@ class TFMobileBertMainLayer(tf.keras.layers.Layer): ...@@ -680,7 +681,8 @@ class TFMobileBertMainLayer(tf.keras.layers.Layer):
head_mask = inputs[4] if len(inputs) > 4 else head_mask head_mask = inputs[4] if len(inputs) > 4 else head_mask
inputs_embeds = inputs[5] if len(inputs) > 5 else inputs_embeds inputs_embeds = inputs[5] if len(inputs) > 5 else inputs_embeds
output_attentions = inputs[6] if len(inputs) > 6 else output_attentions output_attentions = inputs[6] if len(inputs) > 6 else output_attentions
assert len(inputs) <= 7, "Too many inputs." output_hidden_states = inputs[7] if len(inputs) > 7 else output_hidden_states
assert len(inputs) <= 8, "Too many inputs."
elif isinstance(inputs, (dict, BatchEncoding)): elif isinstance(inputs, (dict, BatchEncoding)):
input_ids = inputs.get("input_ids") input_ids = inputs.get("input_ids")
attention_mask = inputs.get("attention_mask", attention_mask) attention_mask = inputs.get("attention_mask", attention_mask)
...@@ -689,11 +691,13 @@ class TFMobileBertMainLayer(tf.keras.layers.Layer): ...@@ -689,11 +691,13 @@ class TFMobileBertMainLayer(tf.keras.layers.Layer):
head_mask = inputs.get("head_mask", head_mask) head_mask = inputs.get("head_mask", head_mask)
inputs_embeds = inputs.get("inputs_embeds", inputs_embeds) inputs_embeds = inputs.get("inputs_embeds", inputs_embeds)
output_attentions = inputs.get("output_attentions", output_attentions) output_attentions = inputs.get("output_attentions", output_attentions)
assert len(inputs) <= 7, "Too many inputs." output_hidden_states = inputs.get("output_hidden_states", output_hidden_states)
assert len(inputs) <= 8, "Too many inputs."
else: else:
input_ids = inputs input_ids = inputs
output_attentions = output_attentions if output_attentions is not None else self.output_attentions output_attentions = output_attentions if output_attentions is not None else self.output_attentions
output_hidden_states = output_hidden_states if output_hidden_states is not None else self.output_hidden_states
if input_ids is not None and inputs_embeds is not None: if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
...@@ -738,7 +742,8 @@ class TFMobileBertMainLayer(tf.keras.layers.Layer): ...@@ -738,7 +742,8 @@ class TFMobileBertMainLayer(tf.keras.layers.Layer):
embedding_output = self.embeddings([input_ids, position_ids, token_type_ids, inputs_embeds], training=training) embedding_output = self.embeddings([input_ids, position_ids, token_type_ids, inputs_embeds], training=training)
encoder_outputs = self.encoder( encoder_outputs = self.encoder(
[embedding_output, extended_attention_mask, head_mask, output_attentions], training=training [embedding_output, extended_attention_mask, head_mask, output_attentions, output_hidden_states],
training=training,
) )
sequence_output = encoder_outputs[0] sequence_output = encoder_outputs[0]
...@@ -1079,6 +1084,7 @@ class TFMobileBertForSequenceClassification(TFMobileBertPreTrainedModel, TFSeque ...@@ -1079,6 +1084,7 @@ class TFMobileBertForSequenceClassification(TFMobileBertPreTrainedModel, TFSeque
inputs_embeds=None, inputs_embeds=None,
labels=None, labels=None,
output_attentions=None, output_attentions=None,
output_hidden_states=None,
training=False, training=False,
): ):
r""" r"""
...@@ -1092,7 +1098,7 @@ class TFMobileBertForSequenceClassification(TFMobileBertPreTrainedModel, TFSeque ...@@ -1092,7 +1098,7 @@ class TFMobileBertForSequenceClassification(TFMobileBertPreTrainedModel, TFSeque
:obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.MobileBertConfig`) and inputs: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.MobileBertConfig`) and inputs:
logits (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, config.num_labels)`): logits (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, config.num_labels)`):
Classification (or regression if config.num_labels==1) scores (before SoftMax). Classification (or regression if config.num_labels==1) scores (before SoftMax).
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`): hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True``):
tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer) 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)`. of shape :obj:`(batch_size, sequence_length, hidden_size)`.
...@@ -1125,6 +1131,7 @@ class TFMobileBertForSequenceClassification(TFMobileBertPreTrainedModel, TFSeque ...@@ -1125,6 +1131,7 @@ class TFMobileBertForSequenceClassification(TFMobileBertPreTrainedModel, TFSeque
head_mask=head_mask, head_mask=head_mask,
inputs_embeds=inputs_embeds, inputs_embeds=inputs_embeds,
output_attentions=output_attentions, output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
training=training, training=training,
) )
...@@ -1172,6 +1179,7 @@ class TFMobileBertForQuestionAnswering(TFMobileBertPreTrainedModel, TFQuestionAn ...@@ -1172,6 +1179,7 @@ class TFMobileBertForQuestionAnswering(TFMobileBertPreTrainedModel, TFQuestionAn
p_mask=None, p_mask=None,
is_impossible=None, is_impossible=None,
output_attentions=None, output_attentions=None,
output_hidden_states=None,
training=False, training=False,
): ):
r""" r"""
...@@ -1190,7 +1198,7 @@ class TFMobileBertForQuestionAnswering(TFMobileBertPreTrainedModel, TFQuestionAn ...@@ -1190,7 +1198,7 @@ class TFMobileBertForQuestionAnswering(TFMobileBertPreTrainedModel, TFQuestionAn
Span-start scores (before SoftMax). Span-start scores (before SoftMax).
end_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length,)`): end_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length,)`):
Span-end scores (before SoftMax). Span-end scores (before SoftMax).
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`): hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True``):
tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer) 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)`. of shape :obj:`(batch_size, sequence_length, hidden_size)`.
...@@ -1225,6 +1233,7 @@ class TFMobileBertForQuestionAnswering(TFMobileBertPreTrainedModel, TFQuestionAn ...@@ -1225,6 +1233,7 @@ class TFMobileBertForQuestionAnswering(TFMobileBertPreTrainedModel, TFQuestionAn
head_mask=head_mask, head_mask=head_mask,
inputs_embeds=inputs_embeds, inputs_embeds=inputs_embeds,
output_attentions=output_attentions, output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
training=training, training=training,
) )
...@@ -1281,6 +1290,7 @@ class TFMobileBertForMultipleChoice(TFMobileBertPreTrainedModel, TFMultipleChoic ...@@ -1281,6 +1290,7 @@ class TFMobileBertForMultipleChoice(TFMobileBertPreTrainedModel, TFMultipleChoic
inputs_embeds=None, inputs_embeds=None,
labels=None, labels=None,
output_attentions=None, output_attentions=None,
output_hidden_states=None,
training=False, training=False,
): ):
r""" r"""
...@@ -1295,7 +1305,7 @@ class TFMobileBertForMultipleChoice(TFMobileBertPreTrainedModel, TFMultipleChoic ...@@ -1295,7 +1305,7 @@ class TFMobileBertForMultipleChoice(TFMobileBertPreTrainedModel, TFMultipleChoic
`num_choices` is the size of the second dimension of the input tensors. (see `input_ids` above). `num_choices` is the size of the second dimension of the input tensors. (see `input_ids` above).
Classification scores (before SoftMax). Classification scores (before SoftMax).
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`): hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True``):
tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer) 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)`. of shape :obj:`(batch_size, sequence_length, hidden_size)`.
...@@ -1330,7 +1340,8 @@ class TFMobileBertForMultipleChoice(TFMobileBertPreTrainedModel, TFMultipleChoic ...@@ -1330,7 +1340,8 @@ class TFMobileBertForMultipleChoice(TFMobileBertPreTrainedModel, TFMultipleChoic
head_mask = inputs[4] if len(inputs) > 4 else head_mask head_mask = inputs[4] if len(inputs) > 4 else head_mask
inputs_embeds = inputs[5] if len(inputs) > 5 else inputs_embeds inputs_embeds = inputs[5] if len(inputs) > 5 else inputs_embeds
output_attentions = inputs[6] if len(inputs) > 6 else output_attentions output_attentions = inputs[6] if len(inputs) > 6 else output_attentions
assert len(inputs) <= 7, "Too many inputs." output_hidden_states = inputs[7] if len(inputs) > 7 else output_hidden_states
assert len(inputs) <= 8, "Too many inputs."
elif isinstance(inputs, (dict, BatchEncoding)): elif isinstance(inputs, (dict, BatchEncoding)):
input_ids = inputs.get("input_ids") input_ids = inputs.get("input_ids")
attention_mask = inputs.get("attention_mask", attention_mask) attention_mask = inputs.get("attention_mask", attention_mask)
...@@ -1339,7 +1350,8 @@ class TFMobileBertForMultipleChoice(TFMobileBertPreTrainedModel, TFMultipleChoic ...@@ -1339,7 +1350,8 @@ class TFMobileBertForMultipleChoice(TFMobileBertPreTrainedModel, TFMultipleChoic
head_mask = inputs.get("head_mask", head_mask) head_mask = inputs.get("head_mask", head_mask)
inputs_embeds = inputs.get("inputs_embeds", inputs_embeds) inputs_embeds = inputs.get("inputs_embeds", inputs_embeds)
output_attentions = inputs.get("output_attentions", output_attentions) output_attentions = inputs.get("output_attentions", output_attentions)
assert len(inputs) <= 7, "Too many inputs." output_hidden_states = inputs.get("output_hidden_states", output_hidden_states)
assert len(inputs) <= 8, "Too many inputs."
else: else:
input_ids = inputs input_ids = inputs
...@@ -1368,6 +1380,7 @@ class TFMobileBertForMultipleChoice(TFMobileBertPreTrainedModel, TFMultipleChoic ...@@ -1368,6 +1380,7 @@ class TFMobileBertForMultipleChoice(TFMobileBertPreTrainedModel, TFMultipleChoic
head_mask, head_mask,
flat_inputs_embeds, flat_inputs_embeds,
output_attentions, output_attentions,
output_hidden_states,
] ]
outputs = self.mobilebert(flat_inputs, training=training) outputs = self.mobilebert(flat_inputs, training=training)
...@@ -1414,6 +1427,7 @@ class TFMobileBertForTokenClassification(TFMobileBertPreTrainedModel, TFTokenCla ...@@ -1414,6 +1427,7 @@ class TFMobileBertForTokenClassification(TFMobileBertPreTrainedModel, TFTokenCla
inputs_embeds=None, inputs_embeds=None,
labels=None, labels=None,
output_attentions=None, output_attentions=None,
output_hidden_states=None,
training=False, training=False,
): ):
r""" r"""
...@@ -1425,7 +1439,7 @@ class TFMobileBertForTokenClassification(TFMobileBertPreTrainedModel, TFTokenCla ...@@ -1425,7 +1439,7 @@ class TFMobileBertForTokenClassification(TFMobileBertPreTrainedModel, TFTokenCla
:obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.MobileBertConfig`) and inputs: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.MobileBertConfig`) and inputs:
scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.num_labels)`): scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.num_labels)`):
Classification scores (before SoftMax). Classification scores (before SoftMax).
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`): hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` or ``config.output_hidden_states=True``):
tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer) 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)`. of shape :obj:`(batch_size, sequence_length, hidden_size)`.
...@@ -1457,6 +1471,7 @@ class TFMobileBertForTokenClassification(TFMobileBertPreTrainedModel, TFTokenCla ...@@ -1457,6 +1471,7 @@ class TFMobileBertForTokenClassification(TFMobileBertPreTrainedModel, TFTokenCla
head_mask=head_mask, head_mask=head_mask,
inputs_embeds=inputs_embeds, inputs_embeds=inputs_embeds,
output_attentions=output_attentions, output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
training=training, training=training,
) )
......
src/transformers/modeling_tf_openai.py
View file @ f4e1f022
...@@ -246,6 +246,7 @@ class TFOpenAIGPTMainLayer(tf.keras.layers.Layer): ...@@ -246,6 +246,7 @@ class TFOpenAIGPTMainLayer(tf.keras.layers.Layer):
head_mask=None, head_mask=None,
inputs_embeds=None, inputs_embeds=None,
output_attentions=None, output_attentions=None,
output_hidden_states=None,
training=False, training=False,
): ):
if isinstance(inputs, (tuple, list)): if isinstance(inputs, (tuple, list)):
...@@ -256,7 +257,8 @@ class TFOpenAIGPTMainLayer(tf.keras.layers.Layer): ...@@ -256,7 +257,8 @@ class TFOpenAIGPTMainLayer(tf.keras.layers.Layer):
head_mask = inputs[4] if len(inputs) > 4 else head_mask head_mask = inputs[4] if len(inputs) > 4 else head_mask
inputs_embeds = inputs[5] if len(inputs) > 5 else inputs_embeds inputs_embeds = inputs[5] if len(inputs) > 5 else inputs_embeds
output_attentions = inputs[6] if len(inputs) > 6 else output_attentions output_attentions = inputs[6] if len(inputs) > 6 else output_attentions
assert len(inputs) <= 7, "Too many inputs." output_hidden_states = inputs[7] if len(inputs) > 7 else output_hidden_states
assert len(inputs) <= 8, "Too many inputs."
elif isinstance(inputs, (dict, BatchEncoding)): elif isinstance(inputs, (dict, BatchEncoding)):
input_ids = inputs.get("input_ids") input_ids = inputs.get("input_ids")
attention_mask = inputs.get("attention_mask", attention_mask) attention_mask = inputs.get("attention_mask", attention_mask)
...@@ -265,11 +267,13 @@ class TFOpenAIGPTMainLayer(tf.keras.layers.Layer): ...@@ -265,11 +267,13 @@ class TFOpenAIGPTMainLayer(tf.keras.layers.Layer):
head_mask = inputs.get("head_mask", head_mask) head_mask = inputs.get("head_mask", head_mask)
inputs_embeds = inputs.get("inputs_embeds", inputs_embeds) inputs_embeds = inputs.get("inputs_embeds", inputs_embeds)
output_attentions = inputs.get("output_attentions", output_attentions) output_attentions = inputs.get("output_attentions", output_attentions)
assert len(inputs) <= 7, "Too many inputs." output_hidden_states = inputs.get("output_hidden_states", output_hidden_states)
assert len(inputs) <= 8, "Too many inputs."
else: else:
input_ids = inputs input_ids = inputs
output_attentions = output_attentions if output_attentions is not None else self.output_attentions output_attentions = output_attentions if output_attentions is not None else self.output_attentions
output_hidden_states = output_hidden_states if output_hidden_states is not None else self.output_hidden_states
if input_ids is not None and inputs_embeds is not None: if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
...@@ -332,7 +336,7 @@ class TFOpenAIGPTMainLayer(tf.keras.layers.Layer): ...@@ -332,7 +336,7 @@ class TFOpenAIGPTMainLayer(tf.keras.layers.Layer):
all_attentions = [] all_attentions = []
all_hidden_states = () all_hidden_states = ()
for i, block in enumerate(self.h): for i, block in enumerate(self.h):
if self.output_hidden_states: if cast_bool_to_primitive(output_hidden_states) is True:
all_hidden_states = all_hidden_states + (tf.reshape(hidden_states, output_shape),) all_hidden_states = all_hidden_states + (tf.reshape(hidden_states, output_shape),)
outputs = block([hidden_states, attention_mask, head_mask[i], output_attentions], training=training) outputs = block([hidden_states, attention_mask, head_mask[i], output_attentions], training=training)
...@@ -342,11 +346,11 @@ class TFOpenAIGPTMainLayer(tf.keras.layers.Layer): ...@@ -342,11 +346,11 @@ class TFOpenAIGPTMainLayer(tf.keras.layers.Layer):
hidden_states = tf.reshape(hidden_states, output_shape) hidden_states = tf.reshape(hidden_states, output_shape)
# Add last hidden state # Add last hidden state
if self.output_hidden_states: if cast_bool_to_primitive(output_hidden_states) is True:
all_hidden_states = all_hidden_states + (hidden_states,) all_hidden_states = all_hidden_states + (hidden_states,)
outputs = (hidden_states,) outputs = (hidden_states,)
if self.output_hidden_states: if cast_bool_to_primitive(output_hidden_states) is True:
outputs = outputs + (all_hidden_states,) outputs = outputs + (all_hidden_states,)
if cast_bool_to_primitive(output_attentions) is True: if cast_bool_to_primitive(output_attentions) is True:
# let the number of heads free (-1) so we can extract attention even after head pruning # let the number of heads free (-1) so we can extract attention even after head pruning
...@@ -451,7 +455,7 @@ class TFOpenAIGPTModel(TFOpenAIGPTPreTrainedModel): ...@@ -451,7 +455,7 @@ class TFOpenAIGPTModel(TFOpenAIGPTPreTrainedModel):
:obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.OpenAIGPTConfig`) and inputs: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.OpenAIGPTConfig`) and inputs:
last_hidden_state (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`): last_hidden_state (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the last layer of the model. Sequence of hidden-states at the last layer of the model.
hidden_states (:obj:`tuple(tf.Tensor)` `optional`, returned when ``config.output_hidden_states=True``): 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) 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)`. of shape :obj:`(batch_size, sequence_length, hidden_size)`.
...@@ -499,7 +503,7 @@ class TFOpenAIGPTLMHeadModel(TFOpenAIGPTPreTrainedModel): ...@@ -499,7 +503,7 @@ class TFOpenAIGPTLMHeadModel(TFOpenAIGPTPreTrainedModel):
:obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.OpenAIGPTConfig`) and inputs: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.OpenAIGPTConfig`) and inputs:
prediction_scores (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`): prediction_scores (: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). Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_hidden_states=True``): 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) 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)`. of shape :obj:`(batch_size, sequence_length, hidden_size)`.
...@@ -564,6 +568,7 @@ class TFOpenAIGPTDoubleHeadsModel(TFOpenAIGPTPreTrainedModel): ...@@ -564,6 +568,7 @@ class TFOpenAIGPTDoubleHeadsModel(TFOpenAIGPTPreTrainedModel):
inputs_embeds=None, inputs_embeds=None,
mc_token_ids=None, mc_token_ids=None,
output_attentions=None, output_attentions=None,
output_hidden_states=None,
training=False, training=False,
): ):
r""" r"""
...@@ -581,7 +586,7 @@ class TFOpenAIGPTDoubleHeadsModel(TFOpenAIGPTPreTrainedModel): ...@@ -581,7 +586,7 @@ class TFOpenAIGPTDoubleHeadsModel(TFOpenAIGPTPreTrainedModel):
Contains pre-computed hidden-states (key and values in the attention blocks). Contains pre-computed hidden-states (key and values in the attention blocks).
Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model
should not be passed as input ids as they have already been computed. should not be passed as input ids as they have already been computed.
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_hidden_states=True``): 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) 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)`. of shape :obj:`(batch_size, sequence_length, hidden_size)`.
...@@ -661,6 +666,7 @@ class TFOpenAIGPTDoubleHeadsModel(TFOpenAIGPTPreTrainedModel): ...@@ -661,6 +666,7 @@ class TFOpenAIGPTDoubleHeadsModel(TFOpenAIGPTPreTrainedModel):
head_mask, head_mask,
inputs_embeds, inputs_embeds,
output_attentions, output_attentions,
output_hidden_states,
] ]
transformer_outputs = self.transformer(flat_inputs, training=training) transformer_outputs = self.transformer(flat_inputs, training=training)
......
src/transformers/modeling_tf_roberta.py
View file @ f4e1f022
...@@ -207,7 +207,8 @@ class TFRobertaModel(TFRobertaPreTrainedModel): ...@@ -207,7 +207,8 @@ class TFRobertaModel(TFRobertaPreTrainedModel):
objective during Bert pretraining. This output is usually *not* a good summary objective during Bert pretraining. This output is usually *not* a good summary
of the semantic content of the input, you're often better with averaging or pooling of the semantic content of the input, you're often better with averaging or pooling
the sequence of hidden-states for the whole input sequence. the sequence of hidden-states for the whole input sequence.
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`): 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) 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)`. of shape :obj:`(batch_size, sequence_length, hidden_size)`.
...@@ -283,7 +284,8 @@ class TFRobertaForMaskedLM(TFRobertaPreTrainedModel): ...@@ -283,7 +284,8 @@ class TFRobertaForMaskedLM(TFRobertaPreTrainedModel):
:obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.RobertaConfig`) and inputs: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.RobertaConfig`) and inputs:
prediction_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`): prediction_scores (:obj:`Numpy array` or :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). Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`): 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) 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)`. of shape :obj:`(batch_size, sequence_length, hidden_size)`.
...@@ -365,6 +367,7 @@ class TFRobertaForSequenceClassification(TFRobertaPreTrainedModel, TFSequenceCla ...@@ -365,6 +367,7 @@ class TFRobertaForSequenceClassification(TFRobertaPreTrainedModel, TFSequenceCla
inputs_embeds=None, inputs_embeds=None,
labels=None, labels=None,
output_attentions=None, output_attentions=None,
output_hidden_states=None,
training=False, training=False,
): ):
r""" r"""
...@@ -372,7 +375,8 @@ class TFRobertaForSequenceClassification(TFRobertaPreTrainedModel, TFSequenceCla ...@@ -372,7 +375,8 @@ class TFRobertaForSequenceClassification(TFRobertaPreTrainedModel, TFSequenceCla
:obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.RobertaConfig`) and inputs: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.RobertaConfig`) and inputs:
logits (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, config.num_labels)`): logits (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, config.num_labels)`):
Classification (or regression if config.num_labels==1) scores (before SoftMax). Classification (or regression if config.num_labels==1) scores (before SoftMax).
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`): 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) 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)`. of shape :obj:`(batch_size, sequence_length, hidden_size)`.
...@@ -404,6 +408,7 @@ class TFRobertaForSequenceClassification(TFRobertaPreTrainedModel, TFSequenceCla ...@@ -404,6 +408,7 @@ class TFRobertaForSequenceClassification(TFRobertaPreTrainedModel, TFSequenceCla
head_mask=head_mask, head_mask=head_mask,
inputs_embeds=inputs_embeds, inputs_embeds=inputs_embeds,
output_attentions=output_attentions, output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
training=training, training=training,
) )
...@@ -454,6 +459,7 @@ class TFRobertaForMultipleChoice(TFRobertaPreTrainedModel, TFMultipleChoiceLoss) ...@@ -454,6 +459,7 @@ class TFRobertaForMultipleChoice(TFRobertaPreTrainedModel, TFMultipleChoiceLoss)
inputs_embeds=None, inputs_embeds=None,
labels=None, labels=None,
output_attentions=None, output_attentions=None,
output_hidden_states=None,
training=False, training=False,
): ):
r""" r"""
...@@ -468,7 +474,8 @@ class TFRobertaForMultipleChoice(TFRobertaPreTrainedModel, TFMultipleChoiceLoss) ...@@ -468,7 +474,8 @@ class TFRobertaForMultipleChoice(TFRobertaPreTrainedModel, TFMultipleChoiceLoss)
`num_choices` is the size of the second dimension of the input tensors. (see `input_ids` above). `num_choices` is the size of the second dimension of the input tensors. (see `input_ids` above).
Classification scores (before SoftMax). Classification scores (before SoftMax).
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`): 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) 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)`. of shape :obj:`(batch_size, sequence_length, hidden_size)`.
...@@ -533,6 +540,8 @@ class TFRobertaForMultipleChoice(TFRobertaPreTrainedModel, TFMultipleChoiceLoss) ...@@ -533,6 +540,8 @@ class TFRobertaForMultipleChoice(TFRobertaPreTrainedModel, TFMultipleChoiceLoss)
flat_position_ids, flat_position_ids,
head_mask, head_mask,
inputs_embeds, inputs_embeds,
output_attentions,
output_hidden_states,
] ]
outputs = self.roberta(flat_inputs, training=training) outputs = self.roberta(flat_inputs, training=training)
...@@ -579,6 +588,7 @@ class TFRobertaForTokenClassification(TFRobertaPreTrainedModel, TFTokenClassific ...@@ -579,6 +588,7 @@ class TFRobertaForTokenClassification(TFRobertaPreTrainedModel, TFTokenClassific
inputs_embeds=None, inputs_embeds=None,
labels=None, labels=None,
output_attentions=None, output_attentions=None,
output_hidden_states=None,
training=False, training=False,
): ):
r""" r"""
...@@ -590,7 +600,8 @@ class TFRobertaForTokenClassification(TFRobertaPreTrainedModel, TFTokenClassific ...@@ -590,7 +600,8 @@ class TFRobertaForTokenClassification(TFRobertaPreTrainedModel, TFTokenClassific
:obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.RobertaConfig`) and inputs: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.RobertaConfig`) and inputs:
scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.num_labels)`): scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.num_labels)`):
Classification scores (before SoftMax). Classification scores (before SoftMax).
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`): 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) 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)`. of shape :obj:`(batch_size, sequence_length, hidden_size)`.
...@@ -622,6 +633,7 @@ class TFRobertaForTokenClassification(TFRobertaPreTrainedModel, TFTokenClassific ...@@ -622,6 +633,7 @@ class TFRobertaForTokenClassification(TFRobertaPreTrainedModel, TFTokenClassific
head_mask=head_mask, head_mask=head_mask,
inputs_embeds=inputs_embeds, inputs_embeds=inputs_embeds,
output_attentions=output_attentions, output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
training=training, training=training,
) )
...@@ -668,6 +680,7 @@ class TFRobertaForQuestionAnswering(TFRobertaPreTrainedModel, TFQuestionAnswerin ...@@ -668,6 +680,7 @@ class TFRobertaForQuestionAnswering(TFRobertaPreTrainedModel, TFQuestionAnswerin
p_mask=None, p_mask=None,
is_impossible=None, is_impossible=None,
output_attentions=None, output_attentions=None,
output_hidden_states=None,
training=False, training=False,
): ):
r""" r"""
...@@ -686,7 +699,8 @@ class TFRobertaForQuestionAnswering(TFRobertaPreTrainedModel, TFQuestionAnswerin ...@@ -686,7 +699,8 @@ class TFRobertaForQuestionAnswering(TFRobertaPreTrainedModel, TFQuestionAnswerin
Span-start scores (before SoftMax). Span-start scores (before SoftMax).
end_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length,)`): end_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length,)`):
Span-end scores (before SoftMax). Span-end scores (before SoftMax).
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`): 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) 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)`. of shape :obj:`(batch_size, sequence_length, hidden_size)`.
...@@ -723,6 +737,7 @@ class TFRobertaForQuestionAnswering(TFRobertaPreTrainedModel, TFQuestionAnswerin ...@@ -723,6 +737,7 @@ class TFRobertaForQuestionAnswering(TFRobertaPreTrainedModel, TFQuestionAnswerin
head_mask=head_mask, head_mask=head_mask,
inputs_embeds=inputs_embeds, inputs_embeds=inputs_embeds,
output_attentions=output_attentions, output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
training=training, training=training,
) )
......
src/transformers/modeling_tf_t5.py
View file @ f4e1f022
...@@ -558,6 +558,7 @@ class TFT5MainLayer(tf.keras.layers.Layer): ...@@ -558,6 +558,7 @@ class TFT5MainLayer(tf.keras.layers.Layer):
past_key_value_states=None, past_key_value_states=None,
use_cache=False, use_cache=False,
output_attentions=None, output_attentions=None,
output_hidden_states=None,
training=False, training=False,
): ):
if isinstance(inputs, (tuple, list)): if isinstance(inputs, (tuple, list)):
...@@ -584,6 +585,7 @@ class TFT5MainLayer(tf.keras.layers.Layer): ...@@ -584,6 +585,7 @@ class TFT5MainLayer(tf.keras.layers.Layer):
input_ids = inputs input_ids = inputs
output_attentions = output_attentions if output_attentions is not None else self.output_attentions output_attentions = output_attentions if output_attentions is not None else self.output_attentions
output_hidden_states = output_hidden_states if output_hidden_states is not None else self.output_hidden_states
if input_ids is not None and inputs_embeds is not None: if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both inputs and inputs_embeds at the same time") raise ValueError("You cannot specify both inputs and inputs_embeds at the same time")
...@@ -696,7 +698,7 @@ class TFT5MainLayer(tf.keras.layers.Layer): ...@@ -696,7 +698,7 @@ class TFT5MainLayer(tf.keras.layers.Layer):
hidden_states = self.dropout(inputs_embeds, training=training) hidden_states = self.dropout(inputs_embeds, training=training)
for i, (layer_module, past_key_value_state) in enumerate(zip(self.block, past_key_value_states)): for i, (layer_module, past_key_value_state) in enumerate(zip(self.block, past_key_value_states)):
if self.output_hidden_states: if cast_bool_to_primitive(output_hidden_states) is True:
all_hidden_states = all_hidden_states + (hidden_states,) all_hidden_states = all_hidden_states + (hidden_states,)
layer_outputs = layer_module( layer_outputs = layer_module(
...@@ -731,14 +733,14 @@ class TFT5MainLayer(tf.keras.layers.Layer): ...@@ -731,14 +733,14 @@ class TFT5MainLayer(tf.keras.layers.Layer):
hidden_states = self.dropout(hidden_states, training=training) hidden_states = self.dropout(hidden_states, training=training)
# Add last layer # Add last layer
if self.output_hidden_states: if cast_bool_to_primitive(output_hidden_states) is True:
all_hidden_states = all_hidden_states + (hidden_states,) all_hidden_states = all_hidden_states + (hidden_states,)
outputs = (hidden_states,) outputs = (hidden_states,)
if use_cache is True: if use_cache is True:
assert self.is_decoder, "`use_cache` can only be set to `True` if {} is used as a decoder".format(self) assert self.is_decoder, "`use_cache` can only be set to `True` if {} is used as a decoder".format(self)
outputs = outputs + (present_key_value_states,) outputs = outputs + (present_key_value_states,)
if self.output_hidden_states: if cast_bool_to_primitive(output_hidden_states) is True:
outputs = outputs + (all_hidden_states,) outputs = outputs + (all_hidden_states,)
if cast_bool_to_primitive(output_attentions) is True: if cast_bool_to_primitive(output_attentions) is True:
outputs = outputs + (all_attentions,) outputs = outputs + (all_attentions,)
...@@ -912,7 +914,7 @@ class TFT5Model(TFT5PreTrainedModel): ...@@ -912,7 +914,7 @@ class TFT5Model(TFT5PreTrainedModel):
Contains pre-computed key and value hidden-states of the attention blocks. Contains pre-computed key and value hidden-states of the attention blocks.
Can be used to speed up sequential decoding (see `decoder_past_key_value_states` input). Can be used to speed up sequential decoding (see `decoder_past_key_value_states` input).
Note that when using `decoder_past_key_value_states`, the model only outputs the last `hidden-state` of the sequence of shape :obj:`(batch_size, 1, config.vocab_size)`. Note that when using `decoder_past_key_value_states`, the model only outputs the last `hidden-state` of the sequence of shape :obj:`(batch_size, 1, config.vocab_size)`.
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_hidden_states=True``): 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) 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)`. of shape :obj:`(batch_size, sequence_length, hidden_size)`.
...@@ -953,6 +955,7 @@ class TFT5Model(TFT5PreTrainedModel): ...@@ -953,6 +955,7 @@ class TFT5Model(TFT5PreTrainedModel):
use_cache = kwargs.get("use_cache", True) use_cache = kwargs.get("use_cache", True)
head_mask = kwargs.get("head_mask", None) head_mask = kwargs.get("head_mask", None)
output_attentions = kwargs.get("output_attentions", None) output_attentions = kwargs.get("output_attentions", None)
output_hidden_states = kwargs.get("output_hidden_states", None)
# Encode if needed (training, first prediction pass) # Encode if needed (training, first prediction pass)
if encoder_outputs is None: if encoder_outputs is None:
...@@ -962,6 +965,7 @@ class TFT5Model(TFT5PreTrainedModel): ...@@ -962,6 +965,7 @@ class TFT5Model(TFT5PreTrainedModel):
inputs_embeds=inputs_embeds, inputs_embeds=inputs_embeds,
head_mask=head_mask, head_mask=head_mask,
output_attentions=output_attentions, output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
) )
hidden_states = encoder_outputs[0] hidden_states = encoder_outputs[0]
...@@ -985,6 +989,7 @@ class TFT5Model(TFT5PreTrainedModel): ...@@ -985,6 +989,7 @@ class TFT5Model(TFT5PreTrainedModel):
head_mask=head_mask, head_mask=head_mask,
use_cache=use_cache, use_cache=use_cache,
output_attentions=output_attentions, output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
) )
if use_cache is True: if use_cache is True:
...@@ -1049,7 +1054,7 @@ class TFT5ForConditionalGeneration(TFT5PreTrainedModel): ...@@ -1049,7 +1054,7 @@ class TFT5ForConditionalGeneration(TFT5PreTrainedModel):
Contains pre-computed key and value hidden-states of the attention blocks. Contains pre-computed key and value hidden-states of the attention blocks.
Can be used to speed up sequential decoding (see `decoder_past_key_value_states` input). Can be used to speed up sequential decoding (see `decoder_past_key_value_states` input).
Note that when using `decoder_past_key_value_states`, the model only outputs the last `prediction_score` of the sequence of shape :obj:`(batch_size, 1, config.vocab_size)`. Note that when using `decoder_past_key_value_states`, the model only outputs the last `prediction_score` of the sequence of shape :obj:`(batch_size, 1, config.vocab_size)`.
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_hidden_states=True``): 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) 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)`. of shape :obj:`(batch_size, sequence_length, hidden_size)`.
...@@ -1094,6 +1099,7 @@ class TFT5ForConditionalGeneration(TFT5PreTrainedModel): ...@@ -1094,6 +1099,7 @@ class TFT5ForConditionalGeneration(TFT5PreTrainedModel):
decoder_inputs_embeds = kwargs.get("decoder_inputs_embeds", None) decoder_inputs_embeds = kwargs.get("decoder_inputs_embeds", None)
head_mask = kwargs.get("head_mask", None) head_mask = kwargs.get("head_mask", None)
output_attentions = kwargs.get("output_attentions", None) output_attentions = kwargs.get("output_attentions", None)
output_hidden_states = kwargs.get("output_hidden_states", None)
# Encode if needed (training, first prediction pass) # Encode if needed (training, first prediction pass)
if encoder_outputs is None: if encoder_outputs is None:
...@@ -1104,6 +1110,7 @@ class TFT5ForConditionalGeneration(TFT5PreTrainedModel): ...@@ -1104,6 +1110,7 @@ class TFT5ForConditionalGeneration(TFT5PreTrainedModel):
inputs_embeds=inputs_embeds, inputs_embeds=inputs_embeds,
head_mask=head_mask, head_mask=head_mask,
output_attentions=output_attentions, output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
) )
hidden_states = encoder_outputs[0] hidden_states = encoder_outputs[0]
...@@ -1127,6 +1134,7 @@ class TFT5ForConditionalGeneration(TFT5PreTrainedModel): ...@@ -1127,6 +1134,7 @@ class TFT5ForConditionalGeneration(TFT5PreTrainedModel):
head_mask=head_mask, head_mask=head_mask,
use_cache=use_cache, use_cache=use_cache,
output_attentions=output_attentions, output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
) )
# insert decoder past at right place # insert decoder past at right place
......
src/transformers/modeling_tf_transfo_xl.py
View file @ f4e1f022
...@@ -520,25 +520,37 @@ class TFTransfoXLMainLayer(tf.keras.layers.Layer): ...@@ -520,25 +520,37 @@ class TFTransfoXLMainLayer(tf.keras.layers.Layer):
return new_mems return new_mems
def call(self, inputs, mems=None, head_mask=None, inputs_embeds=None, output_attentions=None, training=False): def call(
self,
inputs,
mems=None,
head_mask=None,
inputs_embeds=None,
output_attentions=None,
output_hidden_states=None,
training=False,
):
if isinstance(inputs, (tuple, list)): if isinstance(inputs, (tuple, list)):
input_ids = inputs[0] input_ids = inputs[0]
mems = inputs[1] if len(inputs) > 1 else mems mems = inputs[1] if len(inputs) > 1 else mems
head_mask = inputs[2] if len(inputs) > 2 else head_mask head_mask = inputs[2] if len(inputs) > 2 else head_mask
inputs_embeds = inputs[3] if len(inputs) > 3 else inputs_embeds inputs_embeds = inputs[3] if len(inputs) > 3 else inputs_embeds
output_attentions = inputs[4] if len(inputs) > 4 else output_attentions output_attentions = inputs[4] if len(inputs) > 4 else output_attentions
assert len(inputs) <= 5, "Too many inputs." output_hidden_states = inputs[5] if len(inputs) > 4 else output_hidden_states
assert len(inputs) <= 6, "Too many inputs."
elif isinstance(inputs, (dict, BatchEncoding)): elif isinstance(inputs, (dict, BatchEncoding)):
input_ids = inputs.get("input_ids") input_ids = inputs.get("input_ids")
mems = inputs.get("mems", mems) mems = inputs.get("mems", mems)
head_mask = inputs.get("head_mask", head_mask) head_mask = inputs.get("head_mask", head_mask)
inputs_embeds = inputs.get("inputs_embeds", inputs_embeds) inputs_embeds = inputs.get("inputs_embeds", inputs_embeds)
output_attentions = inputs.get("output_attentions", output_attentions) output_attentions = inputs.get("output_attentions", output_attentions)
assert len(inputs) <= 5, "Too many inputs." output_hidden_states = inputs.get("output_hidden_states", output_hidden_states)
assert len(inputs) <= 6, "Too many inputs."
else: else:
input_ids = inputs input_ids = inputs
output_attentions = output_attentions if output_attentions is not None else self.output_attentions output_attentions = output_attentions if output_attentions is not None else self.output_attentions
output_hidden_states = output_hidden_states if output_hidden_states is not None else self.output_hidden_states
# the original code for Transformer-XL used shapes [len, bsz] but we want a unified interface in the library # the original code for Transformer-XL used shapes [len, bsz] but we want a unified interface in the library
# so we transpose here from shape [bsz, len] to shape [len, bsz] # so we transpose here from shape [bsz, len] to shape [len, bsz]
...@@ -625,7 +637,7 @@ class TFTransfoXLMainLayer(tf.keras.layers.Layer): ...@@ -625,7 +637,7 @@ class TFTransfoXLMainLayer(tf.keras.layers.Layer):
# We transpose back here to shape [bsz, len, hidden_dim] # We transpose back here to shape [bsz, len, hidden_dim]
outputs = [tf.transpose(core_out, perm=(1, 0, 2)), new_mems] outputs = [tf.transpose(core_out, perm=(1, 0, 2)), new_mems]
if self.output_hidden_states: if cast_bool_to_primitive(output_hidden_states):
# Add last layer and transpose to library standard shape [bsz, len, hidden_dim] # Add last layer and transpose to library standard shape [bsz, len, hidden_dim]
hids.append(core_out) hids.append(core_out)
hids = list(tf.transpose(t, perm=(1, 0, 2)) for t in hids) hids = list(tf.transpose(t, perm=(1, 0, 2)) for t in hids)
...@@ -720,7 +732,7 @@ class TFTransfoXLModel(TFTransfoXLPreTrainedModel): ...@@ -720,7 +732,7 @@ class TFTransfoXLModel(TFTransfoXLPreTrainedModel):
Contains pre-computed hidden-states (key and values in the attention blocks). Contains pre-computed hidden-states (key and values in the attention blocks).
Can be used (see `mems` input) to speed up sequential decoding. The token ids which have their past given to this model Can be used (see `mems` input) to speed up sequential decoding. The token ids which have their past given to this model
should not be passed as input ids as they have already been computed. should not be passed as input ids as they have already been computed.
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_hidden_states=True``): 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) 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)`. of shape :obj:`(batch_size, sequence_length, hidden_size)`.
...@@ -807,6 +819,7 @@ class TFTransfoXLLMHeadModel(TFTransfoXLPreTrainedModel): ...@@ -807,6 +819,7 @@ class TFTransfoXLLMHeadModel(TFTransfoXLPreTrainedModel):
inputs_embeds=None, inputs_embeds=None,
labels=None, labels=None,
output_attentions=None, output_attentions=None,
output_hidden_states=None,
training=False, training=False,
): ):
r""" r"""
...@@ -818,7 +831,7 @@ class TFTransfoXLLMHeadModel(TFTransfoXLPreTrainedModel): ...@@ -818,7 +831,7 @@ class TFTransfoXLLMHeadModel(TFTransfoXLPreTrainedModel):
Contains pre-computed hidden-states (key and values in the attention blocks). Contains pre-computed hidden-states (key and values in the attention blocks).
Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model
should not be passed as input ids as they have already been computed. should not be passed as input ids as they have already been computed.
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_hidden_states=True``): 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) 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)`. of shape :obj:`(batch_size, sequence_length, hidden_size)`.
...@@ -867,7 +880,7 @@ class TFTransfoXLLMHeadModel(TFTransfoXLPreTrainedModel): ...@@ -867,7 +880,7 @@ class TFTransfoXLLMHeadModel(TFTransfoXLPreTrainedModel):
bsz, tgt_len = shape_list(inputs_embeds)[:2] bsz, tgt_len = shape_list(inputs_embeds)[:2]
transformer_outputs = self.transformer( transformer_outputs = self.transformer(
[input_ids, mems, head_mask, inputs_embeds, output_attentions], training=training [input_ids, mems, head_mask, inputs_embeds, output_attentions, output_hidden_states], training=training
) )
last_hidden = transformer_outputs[0] last_hidden = transformer_outputs[0]
......
src/transformers/modeling_tf_xlm.py
View file @ f4e1f022
...@@ -332,6 +332,7 @@ class TFXLMMainLayer(tf.keras.layers.Layer): ...@@ -332,6 +332,7 @@ class TFXLMMainLayer(tf.keras.layers.Layer):
head_mask=None, head_mask=None,
inputs_embeds=None, inputs_embeds=None,
output_attentions=None, output_attentions=None,
output_hidden_states=None,
training=False, training=False,
): # removed: src_enc=None, src_len=None ): # removed: src_enc=None, src_len=None
if isinstance(inputs, (tuple, list)): if isinstance(inputs, (tuple, list)):
...@@ -345,7 +346,8 @@ class TFXLMMainLayer(tf.keras.layers.Layer): ...@@ -345,7 +346,8 @@ class TFXLMMainLayer(tf.keras.layers.Layer):
head_mask = inputs[7] if len(inputs) > 7 else head_mask head_mask = inputs[7] if len(inputs) > 7 else head_mask
inputs_embeds = inputs[8] if len(inputs) > 8 else inputs_embeds inputs_embeds = inputs[8] if len(inputs) > 8 else inputs_embeds
output_attentions = inputs[9] if len(inputs) > 9 else output_attentions output_attentions = inputs[9] if len(inputs) > 9 else output_attentions
assert len(inputs) <= 10, "Too many inputs." output_hidden_states = inputs[10] if len(inputs) > 10 else output_hidden_states
assert len(inputs) <= 11, "Too many inputs."
elif isinstance(inputs, (dict, BatchEncoding)): elif isinstance(inputs, (dict, BatchEncoding)):
input_ids = inputs.get("input_ids") input_ids = inputs.get("input_ids")
attention_mask = inputs.get("attention_mask", attention_mask) attention_mask = inputs.get("attention_mask", attention_mask)
...@@ -357,11 +359,13 @@ class TFXLMMainLayer(tf.keras.layers.Layer): ...@@ -357,11 +359,13 @@ class TFXLMMainLayer(tf.keras.layers.Layer):
head_mask = inputs.get("head_mask", head_mask) head_mask = inputs.get("head_mask", head_mask)
inputs_embeds = inputs.get("inputs_embeds", inputs_embeds) inputs_embeds = inputs.get("inputs_embeds", inputs_embeds)
output_attentions = inputs.get("output_attentions", output_attentions) output_attentions = inputs.get("output_attentions", output_attentions)
assert len(inputs) <= 10, "Too many inputs." output_hidden_states = inputs.get("output_hidden_states", output_hidden_states)
assert len(inputs) <= 11, "Too many inputs."
else: else:
input_ids = inputs input_ids = inputs
output_attentions = output_attentions if output_attentions is not None else self.output_attentions output_attentions = output_attentions if output_attentions is not None else self.output_attentions
output_hidden_states = output_hidden_states if output_hidden_states is not None else self.output_hidden_states
if input_ids is not None and inputs_embeds is not None: if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
...@@ -445,7 +449,7 @@ class TFXLMMainLayer(tf.keras.layers.Layer): ...@@ -445,7 +449,7 @@ class TFXLMMainLayer(tf.keras.layers.Layer):
hidden_states = () hidden_states = ()
attentions = () attentions = ()
for i in range(self.n_layers): for i in range(self.n_layers):
if self.output_hidden_states: if cast_bool_to_primitive(output_hidden_states) is True:
hidden_states = hidden_states + (tensor,) hidden_states = hidden_states + (tensor,)
# self attention # self attention
...@@ -472,7 +476,7 @@ class TFXLMMainLayer(tf.keras.layers.Layer): ...@@ -472,7 +476,7 @@ class TFXLMMainLayer(tf.keras.layers.Layer):
tensor = tensor * mask[..., tf.newaxis] tensor = tensor * mask[..., tf.newaxis]
# Add last hidden state # Add last hidden state
if self.output_hidden_states: if cast_bool_to_primitive(output_hidden_states) is True:
hidden_states = hidden_states + (tensor,) hidden_states = hidden_states + (tensor,)
# update cache length # update cache length
...@@ -483,7 +487,7 @@ class TFXLMMainLayer(tf.keras.layers.Layer): ...@@ -483,7 +487,7 @@ class TFXLMMainLayer(tf.keras.layers.Layer):
# tensor = tensor.transpose(0, 1) # tensor = tensor.transpose(0, 1)
outputs = (tensor,) outputs = (tensor,)
if self.output_hidden_states: if cast_bool_to_primitive(output_hidden_states) is True:
outputs = outputs + (hidden_states,) outputs = outputs + (hidden_states,)
if cast_bool_to_primitive(output_attentions) is True: if cast_bool_to_primitive(output_attentions) is True:
outputs = outputs + (attentions,) outputs = outputs + (attentions,)
...@@ -610,7 +614,7 @@ class TFXLMModel(TFXLMPreTrainedModel): ...@@ -610,7 +614,7 @@ class TFXLMModel(TFXLMPreTrainedModel):
:obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.XLMConfig`) and inputs: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.XLMConfig`) and inputs:
last_hidden_state (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_length, hidden_size)`): last_hidden_state (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model. Sequence of hidden-states at the output of the last layer of the model.
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_hidden_states=True``): 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` or :obj:`Numpy array` (one for the output of the embeddings + one for the output of each layer) Tuple of :obj:`tf.Tensor` or :obj:`Numpy array` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`. of shape :obj:`(batch_size, sequence_length, hidden_size)`.
...@@ -706,7 +710,7 @@ class TFXLMWithLMHeadModel(TFXLMPreTrainedModel): ...@@ -706,7 +710,7 @@ class TFXLMWithLMHeadModel(TFXLMPreTrainedModel):
:obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.XLMConfig`) and inputs: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.XLMConfig`) and inputs:
prediction_scores (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`): prediction_scores (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_hidden_states=True``): 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` or :obj:`Numpy array` (one for the output of the embeddings + one for the output of each layer) Tuple of :obj:`tf.Tensor` or :obj:`Numpy array` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`. of shape :obj:`(batch_size, sequence_length, hidden_size)`.
...@@ -766,6 +770,7 @@ class TFXLMForSequenceClassification(TFXLMPreTrainedModel, TFSequenceClassificat ...@@ -766,6 +770,7 @@ class TFXLMForSequenceClassification(TFXLMPreTrainedModel, TFSequenceClassificat
inputs_embeds=None, inputs_embeds=None,
labels=None, labels=None,
output_attentions=None, output_attentions=None,
output_hidden_states=None,
training=False, training=False,
): ):
r""" r"""
...@@ -779,7 +784,7 @@ class TFXLMForSequenceClassification(TFXLMPreTrainedModel, TFSequenceClassificat ...@@ -779,7 +784,7 @@ class TFXLMForSequenceClassification(TFXLMPreTrainedModel, TFSequenceClassificat
:obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.XLMConfig`) and inputs: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.XLMConfig`) and inputs:
logits (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, config.num_labels)`): logits (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, config.num_labels)`):
Classification (or regression if config.num_labels==1) scores (before SoftMax). Classification (or regression if config.num_labels==1) scores (before SoftMax).
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_hidden_states=True``): 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` or :obj:`Numpy array` (one for the output of the embeddings + one for the output of each layer) Tuple of :obj:`tf.Tensor` or :obj:`Numpy array` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`. of shape :obj:`(batch_size, sequence_length, hidden_size)`.
...@@ -815,6 +820,7 @@ class TFXLMForSequenceClassification(TFXLMPreTrainedModel, TFSequenceClassificat ...@@ -815,6 +820,7 @@ class TFXLMForSequenceClassification(TFXLMPreTrainedModel, TFSequenceClassificat
head_mask=head_mask, head_mask=head_mask,
inputs_embeds=inputs_embeds, inputs_embeds=inputs_embeds,
output_attentions=output_attentions, output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
training=training, training=training,
) )
output = transformer_outputs[0] output = transformer_outputs[0]
...@@ -865,6 +871,7 @@ class TFXLMForMultipleChoice(TFXLMPreTrainedModel, TFMultipleChoiceLoss): ...@@ -865,6 +871,7 @@ class TFXLMForMultipleChoice(TFXLMPreTrainedModel, TFMultipleChoiceLoss):
inputs_embeds=None, inputs_embeds=None,
labels=None, labels=None,
output_attentions=None, output_attentions=None,
output_hidden_states=None,
training=False, training=False,
): ):
r""" r"""
...@@ -879,7 +886,8 @@ class TFXLMForMultipleChoice(TFXLMPreTrainedModel, TFMultipleChoiceLoss): ...@@ -879,7 +886,8 @@ class TFXLMForMultipleChoice(TFXLMPreTrainedModel, TFMultipleChoiceLoss):
`num_choices` is the size of the second dimension of the input tensors. (see `input_ids` above). `num_choices` is the size of the second dimension of the input tensors. (see `input_ids` above).
Classification scores (before SoftMax). Classification scores (before SoftMax).
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`): 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) 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)`. of shape :obj:`(batch_size, sequence_length, hidden_size)`.
...@@ -956,6 +964,7 @@ class TFXLMForMultipleChoice(TFXLMPreTrainedModel, TFMultipleChoiceLoss): ...@@ -956,6 +964,7 @@ class TFXLMForMultipleChoice(TFXLMPreTrainedModel, TFMultipleChoiceLoss):
head_mask, head_mask,
inputs_embeds, inputs_embeds,
output_attentions, output_attentions,
output_hidden_states,
] ]
transformer_outputs = self.transformer(flat_inputs, training=training) transformer_outputs = self.transformer(flat_inputs, training=training)
...@@ -1002,6 +1011,7 @@ class TFXLMForTokenClassification(TFXLMPreTrainedModel, TFTokenClassificationLos ...@@ -1002,6 +1011,7 @@ class TFXLMForTokenClassification(TFXLMPreTrainedModel, TFTokenClassificationLos
inputs_embeds=None, inputs_embeds=None,
labels=None, labels=None,
output_attentions=None, output_attentions=None,
output_hidden_states=None,
training=False, training=False,
): ):
r""" r"""
...@@ -1013,7 +1023,8 @@ class TFXLMForTokenClassification(TFXLMPreTrainedModel, TFTokenClassificationLos ...@@ -1013,7 +1023,8 @@ class TFXLMForTokenClassification(TFXLMPreTrainedModel, TFTokenClassificationLos
:obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.num_labels)`): scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.num_labels)`):
Classification scores (before SoftMax). Classification scores (before SoftMax).
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`): 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) 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)`. of shape :obj:`(batch_size, sequence_length, hidden_size)`.
...@@ -1045,6 +1056,7 @@ class TFXLMForTokenClassification(TFXLMPreTrainedModel, TFTokenClassificationLos ...@@ -1045,6 +1056,7 @@ class TFXLMForTokenClassification(TFXLMPreTrainedModel, TFTokenClassificationLos
head_mask=head_mask, head_mask=head_mask,
inputs_embeds=inputs_embeds, inputs_embeds=inputs_embeds,
output_attentions=output_attentions, output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
training=training, training=training,
) )
...@@ -1093,6 +1105,7 @@ class TFXLMForQuestionAnsweringSimple(TFXLMPreTrainedModel, TFQuestionAnsweringL ...@@ -1093,6 +1105,7 @@ class TFXLMForQuestionAnsweringSimple(TFXLMPreTrainedModel, TFQuestionAnsweringL
p_mask=None, p_mask=None,
is_impossible=None, is_impossible=None,
output_attentions=None, output_attentions=None,
output_hidden_states=None,
training=False, training=False,
): ):
r""" r"""
...@@ -1111,7 +1124,7 @@ class TFXLMForQuestionAnsweringSimple(TFXLMPreTrainedModel, TFQuestionAnsweringL ...@@ -1111,7 +1124,7 @@ class TFXLMForQuestionAnsweringSimple(TFXLMPreTrainedModel, TFQuestionAnsweringL
Span-start scores (before SoftMax). Span-start scores (before SoftMax).
end_scores (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_length,)`): end_scores (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_length,)`):
Span-end scores (before SoftMax). Span-end scores (before SoftMax).
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_hidden_states=True``): 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` or :obj:`Numpy array` (one for the output of the embeddings + one for the output of each layer) Tuple of :obj:`tf.Tensor` or :obj:`Numpy array` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`. of shape :obj:`(batch_size, sequence_length, hidden_size)`.
...@@ -1150,6 +1163,7 @@ class TFXLMForQuestionAnsweringSimple(TFXLMPreTrainedModel, TFQuestionAnsweringL ...@@ -1150,6 +1163,7 @@ class TFXLMForQuestionAnsweringSimple(TFXLMPreTrainedModel, TFQuestionAnsweringL
head_mask=head_mask, head_mask=head_mask,
inputs_embeds=inputs_embeds, inputs_embeds=inputs_embeds,
output_attentions=output_attentions, output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
training=training, training=training,
) )
......
src/transformers/modeling_tf_xlnet.py
View file @ f4e1f022
...@@ -517,6 +517,7 @@ class TFXLNetMainLayer(tf.keras.layers.Layer): ...@@ -517,6 +517,7 @@ class TFXLNetMainLayer(tf.keras.layers.Layer):
inputs_embeds=None, inputs_embeds=None,
use_cache=True, use_cache=True,
output_attentions=None, output_attentions=None,
output_hidden_states=None,
training=False, training=False,
): ):
if isinstance(inputs, (tuple, list)): if isinstance(inputs, (tuple, list)):
...@@ -530,8 +531,9 @@ class TFXLNetMainLayer(tf.keras.layers.Layer): ...@@ -530,8 +531,9 @@ class TFXLNetMainLayer(tf.keras.layers.Layer):
head_mask = inputs[7] if len(inputs) > 7 else head_mask head_mask = inputs[7] if len(inputs) > 7 else head_mask
inputs_embeds = inputs[8] if len(inputs) > 8 else inputs_embeds inputs_embeds = inputs[8] if len(inputs) > 8 else inputs_embeds
use_cache = inputs[9] if len(inputs) > 9 else use_cache use_cache = inputs[9] if len(inputs) > 9 else use_cache
output_attentions = inputs[-9] if len(inputs) > 10 else output_attentions output_attentions = inputs[10] if len(inputs) > 10 else output_attentions
assert len(inputs) <= 11, "Too many inputs." output_hidden_states = inputs[11] if len(inputs) > 11 else output_hidden_states
assert len(inputs) <= 12, "Too many inputs."
elif isinstance(inputs, (dict, BatchEncoding)): elif isinstance(inputs, (dict, BatchEncoding)):
input_ids = inputs.get("input_ids") input_ids = inputs.get("input_ids")
attention_mask = inputs.get("attention_mask", attention_mask) attention_mask = inputs.get("attention_mask", attention_mask)
...@@ -544,11 +546,13 @@ class TFXLNetMainLayer(tf.keras.layers.Layer): ...@@ -544,11 +546,13 @@ class TFXLNetMainLayer(tf.keras.layers.Layer):
inputs_embeds = inputs.get("inputs_embeds", inputs_embeds) inputs_embeds = inputs.get("inputs_embeds", inputs_embeds)
use_cache = inputs.get("use_cache", use_cache) use_cache = inputs.get("use_cache", use_cache)
output_attentions = inputs.get("output_attentions", output_attentions) output_attentions = inputs.get("output_attentions", output_attentions)
assert len(inputs) <= 11, "Too many inputs." output_hidden_states = inputs.get("output_hidden_states", output_hidden_states)
assert len(inputs) <= 12, "Too many inputs."
else: else:
input_ids = inputs input_ids = inputs
output_attentions = output_attentions if output_attentions is not None else self.output_attentions output_attentions = output_attentions if output_attentions is not None else self.output_attentions
output_hidden_states = output_hidden_states if output_hidden_states is not None else self.output_hidden_states
# the original code for XLNet uses shapes [len, bsz] with the batch dimension at the end # the original code for XLNet uses shapes [len, bsz] with the batch dimension at the end
# but we want a unified interface in the library with the batch size on the first dimension # but we want a unified interface in the library with the batch size on the first dimension
...@@ -677,7 +681,7 @@ class TFXLNetMainLayer(tf.keras.layers.Layer): ...@@ -677,7 +681,7 @@ class TFXLNetMainLayer(tf.keras.layers.Layer):
# cache new mems # cache new mems
if self.mem_len is not None and self.mem_len > 0 and use_cache is True: if self.mem_len is not None and self.mem_len > 0 and use_cache is True:
new_mems = new_mems + (self.cache_mem(output_h, mems[i]),) new_mems = new_mems + (self.cache_mem(output_h, mems[i]),)
if self.output_hidden_states: if cast_bool_to_primitive(output_hidden_states) is True:
hidden_states.append((output_h, output_g) if output_g is not None else output_h) hidden_states.append((output_h, output_g) if output_g is not None else output_h)
outputs = layer_module( outputs = layer_module(
...@@ -700,7 +704,7 @@ class TFXLNetMainLayer(tf.keras.layers.Layer): ...@@ -700,7 +704,7 @@ class TFXLNetMainLayer(tf.keras.layers.Layer):
attentions.append(outputs[2]) attentions.append(outputs[2])
# Add last hidden state # Add last hidden state
if self.output_hidden_states: if cast_bool_to_primitive(output_hidden_states) is True:
hidden_states.append((output_h, output_g) if output_g is not None else output_h) hidden_states.append((output_h, output_g) if output_g is not None else output_h)
output = self.dropout(output_g if output_g is not None else output_h, training=training) output = self.dropout(output_g if output_g is not None else output_h, training=training)
...@@ -711,7 +715,7 @@ class TFXLNetMainLayer(tf.keras.layers.Layer): ...@@ -711,7 +715,7 @@ class TFXLNetMainLayer(tf.keras.layers.Layer):
if self.mem_len is not None and self.mem_len > 0 and use_cache is True: if self.mem_len is not None and self.mem_len > 0 and use_cache is True:
outputs = outputs + (new_mems,) outputs = outputs + (new_mems,)
if self.output_hidden_states: if cast_bool_to_primitive(output_hidden_states) is True:
if output_g is not None: if output_g is not None:
hidden_states = tuple(tf.transpose(h, perm=(1, 0, 2)) for hs in hidden_states for h in hs) hidden_states = tuple(tf.transpose(h, perm=(1, 0, 2)) for hs in hidden_states for h in hs)
else: else:
...@@ -838,7 +842,7 @@ class TFXLNetModel(TFXLNetPreTrainedModel): ...@@ -838,7 +842,7 @@ class TFXLNetModel(TFXLNetPreTrainedModel):
Contains pre-computed hidden-states (key and values in the attention blocks). Contains pre-computed hidden-states (key and values in the attention blocks).
Can be used (see `mems` input) to speed up sequential decoding. The token ids which have their past given to this model Can be used (see `mems` input) to speed up sequential decoding. The token ids which have their past given to this model
should not be passed as input ids as they have already been computed. should not be passed as input ids as they have already been computed.
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_hidden_states=True``): 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` or :obj:`Numpy array` (one for the output of the embeddings + one for the output of each layer) Tuple of :obj:`tf.Tensor` or :obj:`Numpy array` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`. of shape :obj:`(batch_size, sequence_length, hidden_size)`.
...@@ -922,7 +926,7 @@ class TFXLNetLMHeadModel(TFXLNetPreTrainedModel): ...@@ -922,7 +926,7 @@ class TFXLNetLMHeadModel(TFXLNetPreTrainedModel):
Contains pre-computed hidden-states (key and values in the attention blocks). Contains pre-computed hidden-states (key and values in the attention blocks).
Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model
should not be passed as input ids as they have already been computed. should not be passed as input ids as they have already been computed.
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_hidden_states=True``): 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` or :obj:`Numpy array` (one for the output of the embeddings + one for the output of each layer) Tuple of :obj:`tf.Tensor` or :obj:`Numpy array` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`. of shape :obj:`(batch_size, sequence_length, hidden_size)`.
...@@ -996,6 +1000,7 @@ class TFXLNetForSequenceClassification(TFXLNetPreTrainedModel, TFSequenceClassif ...@@ -996,6 +1000,7 @@ class TFXLNetForSequenceClassification(TFXLNetPreTrainedModel, TFSequenceClassif
use_cache=True, use_cache=True,
labels=None, labels=None,
output_attentions=None, output_attentions=None,
output_hidden_states=None,
training=False, training=False,
): ):
r""" r"""
...@@ -1013,7 +1018,7 @@ class TFXLNetForSequenceClassification(TFXLNetPreTrainedModel, TFSequenceClassif ...@@ -1013,7 +1018,7 @@ class TFXLNetForSequenceClassification(TFXLNetPreTrainedModel, TFSequenceClassif
Contains pre-computed hidden-states (key and values in the attention blocks). Contains pre-computed hidden-states (key and values in the attention blocks).
Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model
should not be passed as input ids as they have already been computed. should not be passed as input ids as they have already been computed.
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_hidden_states=True``): 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` or :obj:`Numpy array` (one for the output of the embeddings + one for the output of each layer) Tuple of :obj:`tf.Tensor` or :obj:`Numpy array` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`. of shape :obj:`(batch_size, sequence_length, hidden_size)`.
...@@ -1050,6 +1055,7 @@ class TFXLNetForSequenceClassification(TFXLNetPreTrainedModel, TFSequenceClassif ...@@ -1050,6 +1055,7 @@ class TFXLNetForSequenceClassification(TFXLNetPreTrainedModel, TFSequenceClassif
inputs_embeds=inputs_embeds, inputs_embeds=inputs_embeds,
use_cache=use_cache, use_cache=use_cache,
output_attentions=output_attentions, output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
) )
output = transformer_outputs[0] output = transformer_outputs[0]
...@@ -1106,6 +1112,7 @@ class TFXLNetForMultipleChoice(TFXLNetPreTrainedModel, TFMultipleChoiceLoss): ...@@ -1106,6 +1112,7 @@ class TFXLNetForMultipleChoice(TFXLNetPreTrainedModel, TFMultipleChoiceLoss):
use_cache=True, use_cache=True,
labels=None, labels=None,
output_attentions=None, output_attentions=None,
output_hidden_states=None,
training=False, training=False,
): ):
r""" r"""
...@@ -1120,7 +1127,8 @@ class TFXLNetForMultipleChoice(TFXLNetPreTrainedModel, TFMultipleChoiceLoss): ...@@ -1120,7 +1127,8 @@ class TFXLNetForMultipleChoice(TFXLNetPreTrainedModel, TFMultipleChoiceLoss):
`num_choices` is the size of the second dimension of the input tensors. (see `input_ids` above). `num_choices` is the size of the second dimension of the input tensors. (see `input_ids` above).
Classification scores (before SoftMax). Classification scores (before SoftMax).
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`): 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) 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)`. of shape :obj:`(batch_size, sequence_length, hidden_size)`.
...@@ -1158,8 +1166,9 @@ class TFXLNetForMultipleChoice(TFXLNetPreTrainedModel, TFMultipleChoiceLoss): ...@@ -1158,8 +1166,9 @@ class TFXLNetForMultipleChoice(TFXLNetPreTrainedModel, TFMultipleChoiceLoss):
head_mask = inputs[7] if len(inputs) > 7 else head_mask head_mask = inputs[7] if len(inputs) > 7 else head_mask
inputs_embeds = inputs[8] if len(inputs) > 8 else inputs_embeds inputs_embeds = inputs[8] if len(inputs) > 8 else inputs_embeds
use_cache = inputs[9] if len(inputs) > 9 else use_cache use_cache = inputs[9] if len(inputs) > 9 else use_cache
output_attentions = inputs[-9] if len(inputs) > 10 else output_attentions output_attentions = inputs[10] if len(inputs) > 10 else output_attentions
assert len(inputs) <= 11, "Too many inputs." output_hidden_states = inputs[11] if len(inputs) > 11 else output_hidden_states
assert len(inputs) <= 12, "Too many inputs."
elif isinstance(inputs, (dict, BatchEncoding)): elif isinstance(inputs, (dict, BatchEncoding)):
input_ids = inputs.get("input_ids") input_ids = inputs.get("input_ids")
attention_mask = inputs.get("attention_mask", attention_mask) attention_mask = inputs.get("attention_mask", attention_mask)
...@@ -1172,7 +1181,8 @@ class TFXLNetForMultipleChoice(TFXLNetPreTrainedModel, TFMultipleChoiceLoss): ...@@ -1172,7 +1181,8 @@ class TFXLNetForMultipleChoice(TFXLNetPreTrainedModel, TFMultipleChoiceLoss):
inputs_embeds = inputs.get("inputs_embeds", inputs_embeds) inputs_embeds = inputs.get("inputs_embeds", inputs_embeds)
use_cache = inputs.get("use_cache", use_cache) use_cache = inputs.get("use_cache", use_cache)
output_attentions = inputs.get("output_attentions", output_attentions) output_attentions = inputs.get("output_attentions", output_attentions)
assert len(inputs) <= 11, "Too many inputs." output_hidden_states = inputs.get("output_hidden_states", output_attentions)
assert len(inputs) <= 12, "Too many inputs."
else: else:
input_ids = inputs input_ids = inputs
...@@ -1200,6 +1210,7 @@ class TFXLNetForMultipleChoice(TFXLNetPreTrainedModel, TFMultipleChoiceLoss): ...@@ -1200,6 +1210,7 @@ class TFXLNetForMultipleChoice(TFXLNetPreTrainedModel, TFMultipleChoiceLoss):
inputs_embeds, inputs_embeds,
use_cache, use_cache,
output_attentions, output_attentions,
output_hidden_states,
] ]
transformer_outputs = self.transformer(flat_inputs, training=training) transformer_outputs = self.transformer(flat_inputs, training=training)
...@@ -1246,6 +1257,7 @@ class TFXLNetForTokenClassification(TFXLNetPreTrainedModel, TFTokenClassificatio ...@@ -1246,6 +1257,7 @@ class TFXLNetForTokenClassification(TFXLNetPreTrainedModel, TFTokenClassificatio
use_cache=True, use_cache=True,
labels=None, labels=None,
output_attentions=None, output_attentions=None,
output_hidden_states=None,
training=False, training=False,
): ):
r""" r"""
...@@ -1261,7 +1273,7 @@ class TFXLNetForTokenClassification(TFXLNetPreTrainedModel, TFTokenClassificatio ...@@ -1261,7 +1273,7 @@ class TFXLNetForTokenClassification(TFXLNetPreTrainedModel, TFTokenClassificatio
Contains pre-computed hidden-states (key and values in the attention blocks). Contains pre-computed hidden-states (key and values in the attention blocks).
Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model
should not be passed as input ids as they have already been computed. should not be passed as input ids as they have already been computed.
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_hidden_states=True``): 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` or :obj:`Numpy array` (one for the output of the embeddings + one for the output of each layer) Tuple of :obj:`tf.Tensor` or :obj:`Numpy array` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`. of shape :obj:`(batch_size, sequence_length, hidden_size)`.
...@@ -1298,6 +1310,7 @@ class TFXLNetForTokenClassification(TFXLNetPreTrainedModel, TFTokenClassificatio ...@@ -1298,6 +1310,7 @@ class TFXLNetForTokenClassification(TFXLNetPreTrainedModel, TFTokenClassificatio
inputs_embeds=inputs_embeds, inputs_embeds=inputs_embeds,
use_cache=use_cache, use_cache=use_cache,
output_attentions=output_attentions, output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
training=training, training=training,
) )
output = transformer_outputs[0] output = transformer_outputs[0]
...@@ -1345,6 +1358,7 @@ class TFXLNetForQuestionAnsweringSimple(TFXLNetPreTrainedModel, TFQuestionAnswer ...@@ -1345,6 +1358,7 @@ class TFXLNetForQuestionAnsweringSimple(TFXLNetPreTrainedModel, TFQuestionAnswer
p_mask=None, p_mask=None,
is_impossible=None, is_impossible=None,
output_attentions=None, output_attentions=None,
output_hidden_states=None,
training=False, training=False,
): ):
r""" r"""
...@@ -1369,7 +1383,7 @@ class TFXLNetForQuestionAnsweringSimple(TFXLNetPreTrainedModel, TFQuestionAnswer ...@@ -1369,7 +1383,7 @@ class TFXLNetForQuestionAnsweringSimple(TFXLNetPreTrainedModel, TFQuestionAnswer
Contains pre-computed hidden-states (key and values in the attention blocks). Contains pre-computed hidden-states (key and values in the attention blocks).
Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model
should not be passed as input ids as they have already been computed. should not be passed as input ids as they have already been computed.
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_hidden_states=True``): 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` or :obj:`Numpy array` (one for the output of the embeddings + one for the output of each layer) Tuple of :obj:`tf.Tensor` or :obj:`Numpy array` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`. of shape :obj:`(batch_size, sequence_length, hidden_size)`.
...@@ -1408,6 +1422,7 @@ class TFXLNetForQuestionAnsweringSimple(TFXLNetPreTrainedModel, TFQuestionAnswer ...@@ -1408,6 +1422,7 @@ class TFXLNetForQuestionAnsweringSimple(TFXLNetPreTrainedModel, TFQuestionAnswer
inputs_embeds=inputs_embeds, inputs_embeds=inputs_embeds,
use_cache=use_cache, use_cache=use_cache,
output_attentions=output_attentions, output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
training=training, training=training,
) )
...@@ -1457,7 +1472,7 @@ class TFXLNetForQuestionAnsweringSimple(TFXLNetPreTrainedModel, TFQuestionAnswer ...@@ -1457,7 +1472,7 @@ class TFXLNetForQuestionAnsweringSimple(TFXLNetPreTrainedModel, TFQuestionAnswer
# that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model # that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
# if config.mem_len > 0 else tuple of None. Can be used to speed up sequential decoding and attend to longer context. # if config.mem_len > 0 else tuple of None. Can be used to speed up sequential decoding and attend to longer context.
# See details in the docstring of the `mems` input above. # See details in the docstring of the `mems` input above.
# **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``) # **hidden_states**: (`optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``)
# list of ``tf.Tensor`` (one for the output of each layer + the output of the embeddings) # list of ``tf.Tensor`` (one for the output of each layer + the output of the embeddings)
# of shape ``(batch_size, sequence_length, hidden_size)``: # of shape ``(batch_size, sequence_length, hidden_size)``:
# Hidden-states of the model at the output of each layer plus the initial embedding outputs. # Hidden-states of the model at the output of each layer plus the initial embedding outputs.
......
src/transformers/modeling_transfo_xl.py
View file @ f4e1f022
...@@ -634,7 +634,6 @@ TRANSFO_XL_INPUTS_DOCSTRING = r""" ...@@ -634,7 +634,6 @@ TRANSFO_XL_INPUTS_DOCSTRING = r"""
class TransfoXLModel(TransfoXLPreTrainedModel): class TransfoXLModel(TransfoXLPreTrainedModel):
def __init__(self, config): def __init__(self, config):
super().__init__(config) super().__init__(config)
self.output_hidden_states = config.output_hidden_states
self.n_token = config.vocab_size self.n_token = config.vocab_size
...@@ -750,7 +749,15 @@ class TransfoXLModel(TransfoXLPreTrainedModel): ...@@ -750,7 +749,15 @@ class TransfoXLModel(TransfoXLPreTrainedModel):
return new_mems return new_mems
@add_start_docstrings_to_callable(TRANSFO_XL_INPUTS_DOCSTRING) @add_start_docstrings_to_callable(TRANSFO_XL_INPUTS_DOCSTRING)
def forward(self, input_ids=None, mems=None, head_mask=None, inputs_embeds=None, output_attentions=None): def forward(
self,
input_ids=None,
mems=None,
head_mask=None,
inputs_embeds=None,
output_attentions=None,
output_hidden_states=None,
):
r""" r"""
Return: Return:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.TransfoXLConfig`) and inputs: :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.TransfoXLConfig`) and inputs:
...@@ -760,7 +767,7 @@ class TransfoXLModel(TransfoXLPreTrainedModel): ...@@ -760,7 +767,7 @@ class TransfoXLModel(TransfoXLPreTrainedModel):
Contains pre-computed hidden-states (key and values in the attention blocks). Contains pre-computed hidden-states (key and values in the attention blocks).
Can be used (see `mems` input) to speed up sequential decoding. The token ids which have their past given to this model Can be used (see `mems` input) to speed up sequential decoding. The token ids which have their past given to this model
should not be passed as input ids as they have already been computed. should not be passed as input ids as they have already been computed.
hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``): hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`. of shape :obj:`(batch_size, sequence_length, hidden_size)`.
...@@ -785,6 +792,9 @@ class TransfoXLModel(TransfoXLPreTrainedModel): ...@@ -785,6 +792,9 @@ class TransfoXLModel(TransfoXLPreTrainedModel):
""" """
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
# the original code for Transformer-XL used shapes [len, bsz] but we want a unified interface in the library # the original code for Transformer-XL used shapes [len, bsz] but we want a unified interface in the library
# so we transpose here from shape [bsz, len] to shape [len, bsz] # so we transpose here from shape [bsz, len] to shape [len, bsz]
...@@ -873,7 +883,7 @@ class TransfoXLModel(TransfoXLPreTrainedModel): ...@@ -873,7 +883,7 @@ class TransfoXLModel(TransfoXLPreTrainedModel):
# We transpose back here to shape [bsz, len, hidden_dim] # We transpose back here to shape [bsz, len, hidden_dim]
outputs = [core_out.transpose(0, 1).contiguous(), new_mems] outputs = [core_out.transpose(0, 1).contiguous(), new_mems]
if self.output_hidden_states: if output_hidden_states:
# Add last layer and transpose to library standard shape [bsz, len, hidden_dim] # Add last layer and transpose to library standard shape [bsz, len, hidden_dim]
hids.append(core_out) hids.append(core_out)
hids = list(t.transpose(0, 1).contiguous() for t in hids) hids = list(t.transpose(0, 1).contiguous() for t in hids)
...@@ -936,7 +946,14 @@ class TransfoXLLMHeadModel(TransfoXLPreTrainedModel): ...@@ -936,7 +946,14 @@ class TransfoXLLMHeadModel(TransfoXLPreTrainedModel):
@add_start_docstrings_to_callable(TRANSFO_XL_INPUTS_DOCSTRING) @add_start_docstrings_to_callable(TRANSFO_XL_INPUTS_DOCSTRING)
def forward( def forward(
self, input_ids=None, mems=None, head_mask=None, inputs_embeds=None, labels=None, output_attentions=None self,
input_ids=None,
mems=None,
head_mask=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
): ):
r""" r"""
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`): labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
...@@ -956,7 +973,7 @@ class TransfoXLLMHeadModel(TransfoXLPreTrainedModel): ...@@ -956,7 +973,7 @@ class TransfoXLLMHeadModel(TransfoXLPreTrainedModel):
Contains pre-computed hidden-states (key and values in the attention blocks). Contains pre-computed hidden-states (key and values in the attention blocks).
Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model
should not be passed as input ids as they have already been computed. should not be passed as input ids as they have already been computed.
hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``): hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`. of shape :obj:`(batch_size, sequence_length, hidden_size)`.
...@@ -988,7 +1005,12 @@ class TransfoXLLMHeadModel(TransfoXLPreTrainedModel): ...@@ -988,7 +1005,12 @@ class TransfoXLLMHeadModel(TransfoXLPreTrainedModel):
raise ValueError("You have to specify either input_ids or inputs_embeds") raise ValueError("You have to specify either input_ids or inputs_embeds")
transformer_outputs = self.transformer( transformer_outputs = self.transformer(
input_ids, mems=mems, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions input_ids,
mems=mems,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
) )
last_hidden = transformer_outputs[0] last_hidden = transformer_outputs[0]
......
src/transformers/modeling_xlm.py
View file @ f4e1f022
...@@ -314,7 +314,6 @@ XLM_INPUTS_DOCSTRING = r""" ...@@ -314,7 +314,6 @@ XLM_INPUTS_DOCSTRING = r"""
class XLMModel(XLMPreTrainedModel): class XLMModel(XLMPreTrainedModel):
def __init__(self, config): # , dico, is_encoder, with_output): def __init__(self, config): # , dico, is_encoder, with_output):
super().__init__(config) super().__init__(config)
self.output_hidden_states = config.output_hidden_states
# encoder / decoder, output layer # encoder / decoder, output layer
self.is_encoder = config.is_encoder self.is_encoder = config.is_encoder
...@@ -408,13 +407,14 @@ class XLMModel(XLMPreTrainedModel): ...@@ -408,13 +407,14 @@ class XLMModel(XLMPreTrainedModel):
head_mask=None, head_mask=None,
inputs_embeds=None, inputs_embeds=None,
output_attentions=None, output_attentions=None,
output_hidden_states=None,
): ):
r""" r"""
Return: Return:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.XLMConfig`) and inputs: :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.XLMConfig`) and inputs:
last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`): last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model. Sequence of hidden-states at the output of the last layer of the model.
hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``): hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`. of shape :obj:`(batch_size, sequence_length, hidden_size)`.
...@@ -439,6 +439,9 @@ class XLMModel(XLMPreTrainedModel): ...@@ -439,6 +439,9 @@ class XLMModel(XLMPreTrainedModel):
""" """
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
if input_ids is not None: if input_ids is not None:
bs, slen = input_ids.size() bs, slen = input_ids.size()
...@@ -511,7 +514,7 @@ class XLMModel(XLMPreTrainedModel): ...@@ -511,7 +514,7 @@ class XLMModel(XLMPreTrainedModel):
hidden_states = () hidden_states = ()
attentions = () attentions = ()
for i in range(self.n_layers): for i in range(self.n_layers):
if self.output_hidden_states: if output_hidden_states:
hidden_states = hidden_states + (tensor,) hidden_states = hidden_states + (tensor,)
# self attention # self attention
...@@ -538,7 +541,7 @@ class XLMModel(XLMPreTrainedModel): ...@@ -538,7 +541,7 @@ class XLMModel(XLMPreTrainedModel):
tensor *= mask.unsqueeze(-1).to(tensor.dtype) tensor *= mask.unsqueeze(-1).to(tensor.dtype)
# Add last hidden state # Add last hidden state
if self.output_hidden_states: if output_hidden_states:
hidden_states = hidden_states + (tensor,) hidden_states = hidden_states + (tensor,)
# update cache length # update cache length
...@@ -549,7 +552,7 @@ class XLMModel(XLMPreTrainedModel): ...@@ -549,7 +552,7 @@ class XLMModel(XLMPreTrainedModel):
# tensor = tensor.transpose(0, 1) # tensor = tensor.transpose(0, 1)
outputs = (tensor,) outputs = (tensor,)
if self.output_hidden_states: if output_hidden_states:
outputs = outputs + (hidden_states,) outputs = outputs + (hidden_states,)
if output_attentions: if output_attentions:
outputs = outputs + (attentions,) outputs = outputs + (attentions,)
...@@ -642,6 +645,7 @@ class XLMWithLMHeadModel(XLMPreTrainedModel): ...@@ -642,6 +645,7 @@ class XLMWithLMHeadModel(XLMPreTrainedModel):
inputs_embeds=None, inputs_embeds=None,
labels=None, labels=None,
output_attentions=None, output_attentions=None,
output_hidden_states=None,
): ):
r""" r"""
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`): labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
...@@ -657,7 +661,7 @@ class XLMWithLMHeadModel(XLMPreTrainedModel): ...@@ -657,7 +661,7 @@ class XLMWithLMHeadModel(XLMPreTrainedModel):
Language modeling loss. Language modeling loss.
prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`): prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``): hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`. of shape :obj:`(batch_size, sequence_length, hidden_size)`.
...@@ -692,6 +696,7 @@ class XLMWithLMHeadModel(XLMPreTrainedModel): ...@@ -692,6 +696,7 @@ class XLMWithLMHeadModel(XLMPreTrainedModel):
head_mask=head_mask, head_mask=head_mask,
inputs_embeds=inputs_embeds, inputs_embeds=inputs_embeds,
output_attentions=output_attentions, output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
) )
output = transformer_outputs[0] output = transformer_outputs[0]
...@@ -730,6 +735,7 @@ class XLMForSequenceClassification(XLMPreTrainedModel): ...@@ -730,6 +735,7 @@ class XLMForSequenceClassification(XLMPreTrainedModel):
inputs_embeds=None, inputs_embeds=None,
labels=None, labels=None,
output_attentions=None, output_attentions=None,
output_hidden_states=None,
): ):
r""" r"""
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`): labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
...@@ -744,7 +750,7 @@ class XLMForSequenceClassification(XLMPreTrainedModel): ...@@ -744,7 +750,7 @@ class XLMForSequenceClassification(XLMPreTrainedModel):
Classification (or regression if config.num_labels==1) loss. Classification (or regression if config.num_labels==1) loss.
logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, config.num_labels)`): logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, config.num_labels)`):
Classification (or regression if config.num_labels==1) scores (before SoftMax). Classification (or regression if config.num_labels==1) scores (before SoftMax).
hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``): hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`. of shape :obj:`(batch_size, sequence_length, hidden_size)`.
...@@ -780,6 +786,7 @@ class XLMForSequenceClassification(XLMPreTrainedModel): ...@@ -780,6 +786,7 @@ class XLMForSequenceClassification(XLMPreTrainedModel):
head_mask=head_mask, head_mask=head_mask,
inputs_embeds=inputs_embeds, inputs_embeds=inputs_embeds,
output_attentions=output_attentions, output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
) )
output = transformer_outputs[0] output = transformer_outputs[0]
...@@ -829,6 +836,7 @@ class XLMForQuestionAnsweringSimple(XLMPreTrainedModel): ...@@ -829,6 +836,7 @@ class XLMForQuestionAnsweringSimple(XLMPreTrainedModel):
start_positions=None, start_positions=None,
end_positions=None, end_positions=None,
output_attentions=None, output_attentions=None,
output_hidden_states=None,
): ):
r""" r"""
start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`): start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
...@@ -848,7 +856,7 @@ class XLMForQuestionAnsweringSimple(XLMPreTrainedModel): ...@@ -848,7 +856,7 @@ class XLMForQuestionAnsweringSimple(XLMPreTrainedModel):
Span-start scores (before SoftMax). Span-start scores (before SoftMax).
end_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length,)`): end_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length,)`):
Span-end scores (before SoftMax). Span-end scores (before SoftMax).
hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``): hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`. of shape :obj:`(batch_size, sequence_length, hidden_size)`.
...@@ -885,6 +893,7 @@ class XLMForQuestionAnsweringSimple(XLMPreTrainedModel): ...@@ -885,6 +893,7 @@ class XLMForQuestionAnsweringSimple(XLMPreTrainedModel):
head_mask=head_mask, head_mask=head_mask,
inputs_embeds=inputs_embeds, inputs_embeds=inputs_embeds,
output_attentions=output_attentions, output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
) )
sequence_output = transformer_outputs[0] sequence_output = transformer_outputs[0]
...@@ -952,6 +961,7 @@ class XLMForQuestionAnswering(XLMPreTrainedModel): ...@@ -952,6 +961,7 @@ class XLMForQuestionAnswering(XLMPreTrainedModel):
cls_index=None, cls_index=None,
p_mask=None, p_mask=None,
output_attentions=None, output_attentions=None,
output_hidden_states=None,
): ):
r""" r"""
start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`): start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
...@@ -984,7 +994,7 @@ class XLMForQuestionAnswering(XLMPreTrainedModel): ...@@ -984,7 +994,7 @@ class XLMForQuestionAnswering(XLMPreTrainedModel):
Indices for the top ``config.start_n_top * config.end_n_top`` end token possibilities (beam-search). Indices for the top ``config.start_n_top * config.end_n_top`` end token possibilities (beam-search).
cls_logits (``torch.FloatTensor`` of shape ``(batch_size,)``, `optional`, returned if ``start_positions`` or ``end_positions`` is not provided): cls_logits (``torch.FloatTensor`` of shape ``(batch_size,)``, `optional`, returned if ``start_positions`` or ``end_positions`` is not provided):
Log probabilities for the ``is_impossible`` label of the answers. Log probabilities for the ``is_impossible`` label of the answers.
hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``): hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`. of shape :obj:`(batch_size, sequence_length, hidden_size)`.
...@@ -1021,6 +1031,7 @@ class XLMForQuestionAnswering(XLMPreTrainedModel): ...@@ -1021,6 +1031,7 @@ class XLMForQuestionAnswering(XLMPreTrainedModel):
head_mask=head_mask, head_mask=head_mask,
inputs_embeds=inputs_embeds, inputs_embeds=inputs_embeds,
output_attentions=output_attentions, output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
) )
output = transformer_outputs[0] output = transformer_outputs[0]
...@@ -1066,6 +1077,7 @@ class XLMForTokenClassification(XLMPreTrainedModel): ...@@ -1066,6 +1077,7 @@ class XLMForTokenClassification(XLMPreTrainedModel):
head_mask=None, head_mask=None,
labels=None, labels=None,
output_attentions=None, output_attentions=None,
output_hidden_states=None,
): ):
r""" r"""
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`): labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
...@@ -1078,7 +1090,7 @@ class XLMForTokenClassification(XLMPreTrainedModel): ...@@ -1078,7 +1090,7 @@ class XLMForTokenClassification(XLMPreTrainedModel):
Classification loss. Classification loss.
scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.num_labels)`) scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.num_labels)`)
Classification scores (before SoftMax). Classification scores (before SoftMax).
hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``): hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`. of shape :obj:`(batch_size, sequence_length, hidden_size)`.
...@@ -1111,6 +1123,7 @@ class XLMForTokenClassification(XLMPreTrainedModel): ...@@ -1111,6 +1123,7 @@ class XLMForTokenClassification(XLMPreTrainedModel):
position_ids=position_ids, position_ids=position_ids,
head_mask=head_mask, head_mask=head_mask,
output_attentions=output_attentions, output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
) )
sequence_output = outputs[0] sequence_output = outputs[0]
......
src/transformers/modeling_xlnet.py
View file @ f4e1f022
...@@ -630,7 +630,6 @@ XLNET_INPUTS_DOCSTRING = r""" ...@@ -630,7 +630,6 @@ XLNET_INPUTS_DOCSTRING = r"""
class XLNetModel(XLNetPreTrainedModel): class XLNetModel(XLNetPreTrainedModel):
def __init__(self, config): def __init__(self, config):
super().__init__(config) super().__init__(config)
self.output_hidden_states = config.output_hidden_states
self.mem_len = config.mem_len self.mem_len = config.mem_len
self.reuse_len = config.reuse_len self.reuse_len = config.reuse_len
...@@ -763,6 +762,7 @@ class XLNetModel(XLNetPreTrainedModel): ...@@ -763,6 +762,7 @@ class XLNetModel(XLNetPreTrainedModel):
inputs_embeds=None, inputs_embeds=None,
use_cache=True, use_cache=True,
output_attentions=None, output_attentions=None,
output_hidden_states=None,
): ):
r""" r"""
Return: Return:
...@@ -774,7 +774,7 @@ class XLNetModel(XLNetPreTrainedModel): ...@@ -774,7 +774,7 @@ class XLNetModel(XLNetPreTrainedModel):
Contains pre-computed hidden-states (key and values in the attention blocks). Contains pre-computed hidden-states (key and values in the attention blocks).
Can be used (see `mems` input) to speed up sequential decoding. The token ids which have their past given to this model Can be used (see `mems` input) to speed up sequential decoding. The token ids which have their past given to this model
should not be passed as input ids as they have already been computed. should not be passed as input ids as they have already been computed.
hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``): hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`. of shape :obj:`(batch_size, sequence_length, hidden_size)`.
...@@ -801,6 +801,9 @@ class XLNetModel(XLNetPreTrainedModel): ...@@ -801,6 +801,9 @@ class XLNetModel(XLNetPreTrainedModel):
""" """
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
# the original code for XLNet uses shapes [len, bsz] with the batch dimension at the end # the original code for XLNet uses shapes [len, bsz] with the batch dimension at the end
# but we want a unified interface in the library with the batch size on the first dimension # but we want a unified interface in the library with the batch size on the first dimension
...@@ -934,7 +937,7 @@ class XLNetModel(XLNetPreTrainedModel): ...@@ -934,7 +937,7 @@ class XLNetModel(XLNetPreTrainedModel):
if self.mem_len is not None and self.mem_len > 0 and use_cache is True: if self.mem_len is not None and self.mem_len > 0 and use_cache is True:
# cache new mems # cache new mems
new_mems = new_mems + (self.cache_mem(output_h, mems[i]),) new_mems = new_mems + (self.cache_mem(output_h, mems[i]),)
if self.output_hidden_states: if output_hidden_states:
hidden_states.append((output_h, output_g) if output_g is not None else output_h) hidden_states.append((output_h, output_g) if output_g is not None else output_h)
outputs = layer_module( outputs = layer_module(
...@@ -954,7 +957,7 @@ class XLNetModel(XLNetPreTrainedModel): ...@@ -954,7 +957,7 @@ class XLNetModel(XLNetPreTrainedModel):
attentions.append(outputs[2]) attentions.append(outputs[2])
# Add last hidden state # Add last hidden state
if self.output_hidden_states: if output_hidden_states:
hidden_states.append((output_h, output_g) if output_g is not None else output_h) hidden_states.append((output_h, output_g) if output_g is not None else output_h)
output = self.dropout(output_g if output_g is not None else output_h) output = self.dropout(output_g if output_g is not None else output_h)
...@@ -965,7 +968,7 @@ class XLNetModel(XLNetPreTrainedModel): ...@@ -965,7 +968,7 @@ class XLNetModel(XLNetPreTrainedModel):
if self.mem_len is not None and self.mem_len > 0 and use_cache is True: if self.mem_len is not None and self.mem_len > 0 and use_cache is True:
outputs = outputs + (new_mems,) outputs = outputs + (new_mems,)
if self.output_hidden_states: if output_hidden_states:
if output_g is not None: if output_g is not None:
hidden_states = tuple(h.permute(1, 0, 2).contiguous() for hs in hidden_states for h in hs) hidden_states = tuple(h.permute(1, 0, 2).contiguous() for hs in hidden_states for h in hs)
else: else:
...@@ -1051,6 +1054,7 @@ class XLNetLMHeadModel(XLNetPreTrainedModel): ...@@ -1051,6 +1054,7 @@ class XLNetLMHeadModel(XLNetPreTrainedModel):
use_cache=True, use_cache=True,
labels=None, labels=None,
output_attentions=None, output_attentions=None,
output_hidden_states=None,
): ):
r""" r"""
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, num_predict)`, `optional`, defaults to :obj:`None`): labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, num_predict)`, `optional`, defaults to :obj:`None`):
...@@ -1072,7 +1076,7 @@ class XLNetLMHeadModel(XLNetPreTrainedModel): ...@@ -1072,7 +1076,7 @@ class XLNetLMHeadModel(XLNetPreTrainedModel):
Contains pre-computed hidden-states (key and values in the attention blocks). Contains pre-computed hidden-states (key and values in the attention blocks).
Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model
should not be passed as input ids as they have already been computed. should not be passed as input ids as they have already been computed.
hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``): hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`. of shape :obj:`(batch_size, sequence_length, hidden_size)`.
...@@ -1127,6 +1131,7 @@ class XLNetLMHeadModel(XLNetPreTrainedModel): ...@@ -1127,6 +1131,7 @@ class XLNetLMHeadModel(XLNetPreTrainedModel):
inputs_embeds=inputs_embeds, inputs_embeds=inputs_embeds,
use_cache=use_cache, use_cache=use_cache,
output_attentions=output_attentions, output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
) )
logits = self.lm_loss(transformer_outputs[0]) logits = self.lm_loss(transformer_outputs[0])
...@@ -1173,6 +1178,7 @@ class XLNetForSequenceClassification(XLNetPreTrainedModel): ...@@ -1173,6 +1178,7 @@ class XLNetForSequenceClassification(XLNetPreTrainedModel):
use_cache=True, use_cache=True,
labels=None, labels=None,
output_attentions=None, output_attentions=None,
output_hidden_states=None,
): ):
r""" r"""
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`) labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`)
...@@ -1191,7 +1197,7 @@ class XLNetForSequenceClassification(XLNetPreTrainedModel): ...@@ -1191,7 +1197,7 @@ class XLNetForSequenceClassification(XLNetPreTrainedModel):
Contains pre-computed hidden-states (key and values in the attention blocks). Contains pre-computed hidden-states (key and values in the attention blocks).
Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model
should not be passed as input ids as they have already been computed. should not be passed as input ids as they have already been computed.
hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``): hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`. of shape :obj:`(batch_size, sequence_length, hidden_size)`.
...@@ -1229,6 +1235,7 @@ class XLNetForSequenceClassification(XLNetPreTrainedModel): ...@@ -1229,6 +1235,7 @@ class XLNetForSequenceClassification(XLNetPreTrainedModel):
inputs_embeds=inputs_embeds, inputs_embeds=inputs_embeds,
use_cache=use_cache, use_cache=use_cache,
output_attentions=output_attentions, output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
) )
output = transformer_outputs[0] output = transformer_outputs[0]
...@@ -1280,6 +1287,7 @@ class XLNetForTokenClassification(XLNetPreTrainedModel): ...@@ -1280,6 +1287,7 @@ class XLNetForTokenClassification(XLNetPreTrainedModel):
use_cache=True, use_cache=True,
labels=None, labels=None,
output_attentions=None, output_attentions=None,
output_hidden_states=None,
): ):
r""" r"""
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`): labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
...@@ -1297,7 +1305,7 @@ class XLNetForTokenClassification(XLNetPreTrainedModel): ...@@ -1297,7 +1305,7 @@ class XLNetForTokenClassification(XLNetPreTrainedModel):
Contains pre-computed hidden-states (key and values in the attention blocks). Contains pre-computed hidden-states (key and values in the attention blocks).
Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model
should not be passed as input ids as they have already been computed. should not be passed as input ids as they have already been computed.
hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``): hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`. of shape :obj:`(batch_size, sequence_length, hidden_size)`.
...@@ -1337,6 +1345,7 @@ class XLNetForTokenClassification(XLNetPreTrainedModel): ...@@ -1337,6 +1345,7 @@ class XLNetForTokenClassification(XLNetPreTrainedModel):
inputs_embeds=inputs_embeds, inputs_embeds=inputs_embeds,
use_cache=use_cache, use_cache=use_cache,
output_attentions=output_attentions, output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
) )
sequence_output = outputs[0] sequence_output = outputs[0]
...@@ -1391,6 +1400,7 @@ class XLNetForMultipleChoice(XLNetPreTrainedModel): ...@@ -1391,6 +1400,7 @@ class XLNetForMultipleChoice(XLNetPreTrainedModel):
use_cache=True, use_cache=True,
labels=None, labels=None,
output_attentions=None, output_attentions=None,
output_hidden_states=None,
): ):
r""" r"""
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`): labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
...@@ -1410,7 +1420,7 @@ class XLNetForMultipleChoice(XLNetPreTrainedModel): ...@@ -1410,7 +1420,7 @@ class XLNetForMultipleChoice(XLNetPreTrainedModel):
Contains pre-computed hidden-states (key and values in the attention blocks). Contains pre-computed hidden-states (key and values in the attention blocks).
Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model
should not be passed as input ids as they have already been computed. should not be passed as input ids as they have already been computed.
hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``): hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`. of shape :obj:`(batch_size, sequence_length, hidden_size)`.
...@@ -1462,6 +1472,7 @@ class XLNetForMultipleChoice(XLNetPreTrainedModel): ...@@ -1462,6 +1472,7 @@ class XLNetForMultipleChoice(XLNetPreTrainedModel):
inputs_embeds=flat_inputs_embeds, inputs_embeds=flat_inputs_embeds,
use_cache=use_cache, use_cache=use_cache,
output_attentions=output_attentions, output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
) )
output = transformer_outputs[0] output = transformer_outputs[0]
...@@ -1512,6 +1523,7 @@ class XLNetForQuestionAnsweringSimple(XLNetPreTrainedModel): ...@@ -1512,6 +1523,7 @@ class XLNetForQuestionAnsweringSimple(XLNetPreTrainedModel):
start_positions=None, start_positions=None,
end_positions=None, end_positions=None,
output_attentions=None, output_attentions=None,
output_hidden_states=None,
): ):
r""" r"""
start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`): start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
...@@ -1535,7 +1547,7 @@ class XLNetForQuestionAnsweringSimple(XLNetPreTrainedModel): ...@@ -1535,7 +1547,7 @@ class XLNetForQuestionAnsweringSimple(XLNetPreTrainedModel):
Contains pre-computed hidden-states (key and values in the attention blocks). Contains pre-computed hidden-states (key and values in the attention blocks).
Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model
should not be passed as input ids as they have already been computed. should not be passed as input ids as they have already been computed.
hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``): hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`. of shape :obj:`(batch_size, sequence_length, hidden_size)`.
...@@ -1576,6 +1588,7 @@ class XLNetForQuestionAnsweringSimple(XLNetPreTrainedModel): ...@@ -1576,6 +1588,7 @@ class XLNetForQuestionAnsweringSimple(XLNetPreTrainedModel):
inputs_embeds=inputs_embeds, inputs_embeds=inputs_embeds,
use_cache=use_cache, use_cache=use_cache,
output_attentions=output_attentions, output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
) )
sequence_output = outputs[0] sequence_output = outputs[0]
...@@ -1643,6 +1656,7 @@ class XLNetForQuestionAnswering(XLNetPreTrainedModel): ...@@ -1643,6 +1656,7 @@ class XLNetForQuestionAnswering(XLNetPreTrainedModel):
cls_index=None, cls_index=None,
p_mask=None, p_mask=None,
output_attentions=None, output_attentions=None,
output_hidden_states=None,
): ):
r""" r"""
start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`): start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
...@@ -1679,7 +1693,7 @@ class XLNetForQuestionAnswering(XLNetPreTrainedModel): ...@@ -1679,7 +1693,7 @@ class XLNetForQuestionAnswering(XLNetPreTrainedModel):
Contains pre-computed hidden-states (key and values in the attention blocks). Contains pre-computed hidden-states (key and values in the attention blocks).
Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model
should not be passed as input ids as they have already been computed. should not be passed as input ids as they have already been computed.
hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``): hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`. of shape :obj:`(batch_size, sequence_length, hidden_size)`.
...@@ -1718,6 +1732,7 @@ class XLNetForQuestionAnswering(XLNetPreTrainedModel): ...@@ -1718,6 +1732,7 @@ class XLNetForQuestionAnswering(XLNetPreTrainedModel):
inputs_embeds=inputs_embeds, inputs_embeds=inputs_embeds,
use_cache=use_cache, use_cache=use_cache,
output_attentions=output_attentions, output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
) )
hidden_states = transformer_outputs[0] hidden_states = transformer_outputs[0]
start_logits = self.start_logits(hidden_states, p_mask=p_mask) start_logits = self.start_logits(hidden_states, p_mask=p_mask)
......
tests/test_modeling_common.py
View file @ f4e1f022
...@@ -143,14 +143,13 @@ class ModelTesterMixin: ...@@ -143,14 +143,13 @@ class ModelTesterMixin:
for model_class in self.all_model_classes: for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True inputs_dict["output_attentions"] = True
config.output_hidden_states = False inputs_dict["output_hidden_states"] = False
model = model_class(config) model = model_class(config)
model.to(torch_device) model.to(torch_device)
model.eval() model.eval()
with torch.no_grad(): with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class)) outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs[-1] attentions = outputs[-1]
self.assertEqual(model.config.output_hidden_states, False)
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
# check that output_attentions also work using config # check that output_attentions also work using config
...@@ -162,7 +161,6 @@ class ModelTesterMixin: ...@@ -162,7 +161,6 @@ class ModelTesterMixin:
with torch.no_grad(): with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class)) outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs[-1] attentions = outputs[-1]
self.assertEqual(model.config.output_hidden_states, False)
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
if chunk_length is not None: if chunk_length is not None:
...@@ -201,14 +199,13 @@ class ModelTesterMixin: ...@@ -201,14 +199,13 @@ class ModelTesterMixin:
# Check attention is always last and order is fine # Check attention is always last and order is fine
inputs_dict["output_attentions"] = True inputs_dict["output_attentions"] = True
config.output_hidden_states = True inputs_dict["output_hidden_states"] = True
model = model_class(config) model = model_class(config)
model.to(torch_device) model.to(torch_device)
model.eval() model.eval()
with torch.no_grad(): with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class)) outputs = model(**self._prepare_for_class(inputs_dict, model_class))
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1), len(outputs)) self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1), len(outputs))
self.assertEqual(model.config.output_hidden_states, True)
self_attentions = outputs[-1] self_attentions = outputs[-1]
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers) self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
...@@ -493,19 +490,16 @@ class ModelTesterMixin: ...@@ -493,19 +490,16 @@ class ModelTesterMixin:
self.assertDictEqual(model.config.pruned_heads, {0: [0], 1: [1, 2], 2: [1, 2]}) self.assertDictEqual(model.config.pruned_heads, {0: [0], 1: [1, 2], 2: [1, 2]})
def test_hidden_states_output(self): def test_hidden_states_output(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() def check_hidden_states_output(inputs_dict, config, model_class):
for model_class in self.all_model_classes:
config.output_hidden_states = True
model = model_class(config) model = model_class(config)
model.to(torch_device) model.to(torch_device)
model.eval() model.eval()
with torch.no_grad(): with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class)) outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs[-1] hidden_states = outputs[-1]
self.assertEqual(model.config.output_hidden_states, True)
self.assertEqual(len(hidden_states), self.model_tester.num_hidden_layers + 1)
self.assertEqual(len(hidden_states), self.model_tester.num_hidden_layers + 1)
if hasattr(self.model_tester, "encoder_seq_length"): if hasattr(self.model_tester, "encoder_seq_length"):
seq_length = self.model_tester.encoder_seq_length seq_length = self.model_tester.encoder_seq_length
if hasattr(self.model_tester, "chunk_length") and self.model_tester.chunk_length > 1: if hasattr(self.model_tester, "chunk_length") and self.model_tester.chunk_length > 1:
...@@ -517,6 +511,18 @@ class ModelTesterMixin: ...@@ -517,6 +511,18 @@ class ModelTesterMixin:
list(hidden_states[0].shape[-2:]), [seq_length, self.model_tester.hidden_size], list(hidden_states[0].shape[-2:]), [seq_length, self.model_tester.hidden_size],
) )
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
def test_resize_tokens_embeddings(self): def test_resize_tokens_embeddings(self):
(original_config, inputs_dict,) = self.model_tester.prepare_config_and_inputs_for_common() (original_config, inputs_dict,) = self.model_tester.prepare_config_and_inputs_for_common()
if not self.test_resize_embeddings: if not self.test_resize_embeddings:
......
tests/test_modeling_tf_common.py
View file @ f4e1f022
...@@ -392,17 +392,23 @@ class TFModelTesterMixin: ...@@ -392,17 +392,23 @@ class TFModelTesterMixin:
def test_hidden_states_output(self): def test_hidden_states_output(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes: def check_hidden_states_output(config, inputs_dict, model_class):
config.output_hidden_states = True
model = model_class(config) model = model_class(config)
outputs = model(self._prepare_for_class(inputs_dict, model_class)) outputs = model(self._prepare_for_class(inputs_dict, model_class))
hidden_states = [t.numpy() for t in outputs[-1]] hidden_states = [t.numpy() for t in outputs[-1]]
self.assertEqual(model.config.output_hidden_states, True)
self.assertEqual(len(hidden_states), self.model_tester.num_hidden_layers + 1) self.assertEqual(len(hidden_states), self.model_tester.num_hidden_layers + 1)
self.assertListEqual( self.assertListEqual(
list(hidden_states[0].shape[-2:]), [self.model_tester.seq_length, self.model_tester.hidden_size], list(hidden_states[0].shape[-2:]), [self.model_tester.seq_length, self.model_tester.hidden_size],
) )
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(config, inputs_dict, model_class)
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(config, inputs_dict, model_class)
def test_model_common_attributes(self): def test_model_common_attributes(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
......
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