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Adding missing type hints for BigBird model #16555

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merged 16 commits into from
Apr 5, 2022
Merged

Adding missing type hints for BigBird model #16555

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8357e88
added type hints for mbart tensorflow tf implementation
reichenbch Mar 20, 2022
ef9ef21
Adding missing type hints for mBART model
reichenbch Mar 20, 2022
5b64448
Missing Type hints - correction
reichenbch Mar 21, 2022
92f5c84
Code fixup using make quality tests
reichenbch Mar 21, 2022
33d95f0
Hint types - typo error
reichenbch Mar 21, 2022
46375b4
make fix-copies and make fixup
Rocketknight1 Mar 21, 2022
52fb2b3
type hints
reichenbch Mar 23, 2022
198a01d
Merge branch 'missing-type-hints-mBART' of https://github.com/reichen…
reichenbch Mar 23, 2022
9d8021c
updated files
reichenbch Mar 23, 2022
0325d90
type hints update
reichenbch Mar 26, 2022
09e3808
Merge branch 'huggingface:main' into missing-type-hints-mBART
reichenbch Mar 27, 2022
bc85442
making dependent modesls coherent
reichenbch Mar 27, 2022
70a406a
Merge branch 'missing-type-hints-mBART' of https://github.com/reichen…
reichenbch Mar 27, 2022
363d6ce
Type hints for BigBird
reichenbch Mar 30, 2022
0e58f4d
removing typos
reichenbch Apr 1, 2022
949ebda
Merge branch 'huggingface:main' into missing-type-hints-BigBird
reichenbch Apr 1, 2022
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Adding missing type hints for mBART model 
Tensorflow Implementation model added with missing type hints
  • Loading branch information
@reichenbch @Rocketknight1
reichenbch authored and Rocketknight1 committed Mar 21, 2022
commit ef9ef212d2ff56b80bdaa2c47a2316e89d0fbfe7
147 changes: 74 additions & 73 deletions src/transformers/models/mbart/modeling_tf_mbart.py
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Original file line number Diff line number Diff line change
Expand Up @@ -39,6 +39,7 @@
from ...modeling_tf_utils import (
DUMMY_INPUTS,
TFCausalLanguageModelingLoss,
TFModelInputType,
TFPreTrainedModel,
TFSharedEmbeddings,
TFWrappedEmbeddings,
Expand Down Expand Up @@ -299,7 +300,7 @@ def __init__(self, config: MBartConfig, **kwargs):
self.fc2 = tf.keras.layers.Dense(self.embed_dim, name="fc2")
self.final_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm")

def call(self, hidden_states: tf.Tensor, attention_mask: tf.Tensor, layer_head_mask: tf.Tensor, training=False):
def call(self, hidden_states: tf.Tensor, attention_mask: tf.Tensor, layer_head_mask: tf.Tensor, training: Optional[bool] = False):
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shape *(seq_len, batch, embed_dim)*
Expand Down Expand Up @@ -374,7 +375,7 @@ def call(
layer_head_mask: Optional[tf.Tensor] = None,
cross_attn_layer_head_mask: Optional[tf.Tensor] = None,
past_key_value: Optional[Tuple[tf.Tensor]] = None,
training=False,
training: Optional[bool] = False,
) -> Tuple[tf.Tensor, tf.Tensor, Tuple[Tuple[tf.Tensor]]]:
"""
Args:
Expand Down Expand Up @@ -669,16 +670,16 @@ def set_embed_tokens(self, embed_tokens):
@unpack_inputs
def call(
self,
input_ids=None,
inputs_embeds=None,
attention_mask=None,
head_mask=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
training=False,
input_ids: Optional[TFModelInputType] = None,
inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: Optional[bool] = False,
**kwargs,
):
) -> TFBaseModelOutput:
"""
Args:
input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`):
Expand Down Expand Up @@ -828,21 +829,21 @@ def set_embed_tokens(self, embed_tokens):
@unpack_inputs
def call(
self,
input_ids=None,
inputs_embeds=None,
attention_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
head_mask=None,
cross_attn_head_mask=None,
past_key_values=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
training=False,
input_ids: TFModelInputType = None,
inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
encoder_hidden_states: Optional[Union[np.ndarray, tf.Tensor]] = None,
encoder_attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
cross_attn_head_mask: Optional[tf.Tensor] = None,
past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: Optional[bool] = False,
**kwargs,
):
) -> Union[TFBaseModelOutputWithPastAndCrossAttentions, tuple(tf.Tensor, tf.Tensor, tf.Tensor, tf.Tensor, tf.Tensor)]:
r"""
Args:
input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`):
Expand Down Expand Up @@ -1049,24 +1050,24 @@ def set_input_embeddings(self, new_embeddings):
@unpack_inputs
def call(
self,
input_ids=None,
attention_mask=None,
decoder_input_ids=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
input_ids: TFModelInputType = None,
attention_mask: Optional[tf.Tensor] = None,
decoder_input_ids: Optional[tf.Tensor] = None,
decoder_attention_mask: Optional[tf.Tensor] = None,
head_mask: Optional[tf.Tensor] = None,
decoder_head_mask: Optional[tf.Tensor] = None,
cross_attn_head_mask: Optional[tf.Tensor] = None,
encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None,
past_key_values=None,
inputs_embeds=None,
decoder_inputs_embeds=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
training=False,
past_key_values: Optional[Tuple[Tuple[tf.Tensor]]] = None,
inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
decoder_inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: Optional[bool] = False,
**kwargs
):
) -> Union[TFSeq2SeqModelOutput, tf.Tensor]:

if decoder_input_ids is None and decoder_inputs_embeds is None:
use_cache = False
Expand Down Expand Up @@ -1157,24 +1158,24 @@ def get_decoder(self):
)
def call(
self,
input_ids=None,
attention_mask=None,
decoder_input_ids=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
input_ids: tf.Tensor = None,
attention_mask: Optional[tf.Tensor] = None,
decoder_input_ids: Optional[tf.Tensor] = None,
decoder_attention_mask: Optional[tf.Tensor] = None,
head_mask: Optional[tf.Tensor] = None,
decoder_head_mask: Optional[tf.Tensor] = None,
cross_attn_head_mask: Optional[tf.Tensor] = None,
encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None,
past_key_values=None,
inputs_embeds=None,
decoder_inputs_embeds=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
training=False,
past_key_values: Optional[Tuple[Tuple[tf.Tensor]]] = None,
inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
decoder_inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: Optional[bool] = False,
**kwargs
):
) -> Union[TFSeq2SeqModelOutput, Tuple[tf.tensor]] :

outputs = self.model(
input_ids=input_ids,
Expand Down Expand Up @@ -1261,25 +1262,25 @@ def set_bias(self, value):
@add_end_docstrings(MBART_GENERATION_EXAMPLE)
def call(
self,
input_ids=None,
attention_mask=None,
decoder_input_ids=None,
input_ids: TFModelInputType = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
decoder_input_ids: Optional[tf.Tensor] = None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
head_mask: Optional[tf.Tensor] = None,
decoder_head_mask: Optional[tf.Tensor] = None,
cross_attn_head_mask: Optional[tf.Tensor] = None,
encoder_outputs: Optional[TFBaseModelOutput] = None,
past_key_values=None,
inputs_embeds=None,
decoder_inputs_embeds=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
labels=None,
training=False,
past_key_values: [Tuple[Tuple[tf.Tensor]]] = None,
inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
decoder_inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[tf.Tensor] = None,
training: Optional[bool] = False,
**kwargs,
):
) -> Union[TFSeq2SeqLMOutput, Tuple[tf.Tensor]] :
"""
labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
Expand Down