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class MBartForQuestionAnswering(MBartPreTrainedModel):
_tied_weights_keys = ["model.encoder.embed_tokens.weight", "model.decoder.embed_tokens.weight"]
def __init__(self, config):
super().__init__(config)
config.num_labels = 2
self.num_labels = config.num_labels
self.model = MB... | 10,082 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
@add_start_docstrings_to_model_forward(MBART_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=Seq2SeqQuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
)
# Copied from transformers.models.bart.modeling_bart.BartForQuestionAnswering.f... | 10,082 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, Seq2SeqQuestionAnsweringModelOutput]:
r"""... | 10,082 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
Positions are clamped to the length of the sequence (*sequence_length*). Position outside of the sequence
are not taken into account for computing the loss.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if start_positions is not None and en... | 10,082 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
outputs = self.model(
input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
head_mask=head_mask,
decoder_head_mask=decoder_head_mask,
cross_attn_head_mask=cross_att... | 10,082 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
... | 10,082 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
if not return_dict:
output = (
start_logits,
end_logits,
) + outputs[1:]
return ((total_loss,) + output) if total_loss is not None else output
return Seq2SeqQuestionAnsweringModelOutput(
loss=total_loss,
start_logits=st... | 10,082 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
class MBartDecoderWrapper(MBartPreTrainedModel):
"""
This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is
used in combination with the [`EncoderDecoderModel`] framework.
"""
def __init__(self, config):
super().__init__(config)
... | 10,083 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
class MBartForCausalLM(MBartPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
config = copy.deepcopy(config)
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MBartDecoder... | 10,084 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
@replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_att... | 10,084 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it. | 10,084 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
... | 10,084 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
[What are attention masks?](../glossary#attention-mask)
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
if the... | 10,084 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0,... | 10,084 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
shape `(batch_size, num_heads, sequence_length, ... | 10,084 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
label... | 10,084 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
ou... | 10,084 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/mbart-large-cc25")
>>> model = MBartForCausalLM.from_pretrained("facebook/mbart-large-cc25", add_cross_attention=False)
>>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder."
>>> inputs = tokenizer("Hell... | 10,084 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model.decoder(
input_ids=input_ids,
attention_mask=attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
... | 10,084 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithCrossAttentions(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs... | 10,084 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
class TFMBartLearnedPositionalEmbedding(keras.layers.Embedding):
"""
This module learns positional embeddings up to a fixed maximum size.
"""
def __init__(self, num_embeddings: int, embedding_dim: int, **kwargs):
# MBart is set up so that if padding_idx is specified then offset the embedding id... | 10,085 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
offset_dtype = position_ids.dtype if isinstance(position_ids, tf.Tensor) else tf.int32
return super().call(position_ids + tf.constant(self.offset, dtype=offset_dtype)) | 10,085 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
class TFMBartAttention(keras.layers.Layer):
"""Multi-headed attention from "Attention Is All You Need"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
**kwargs,
):
super(... | 10,086 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
self.k_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="k_proj")
self.q_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="q_proj")
self.v_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="v_proj")
self.out_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="out_pro... | 10,086 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
bsz, tgt_len, embed_dim = shape_list(hidden_states) | 10,086 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
# get query proj
query_states = self.q_proj(hidden_states) * self.scaling
# get key, value proj
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_states = past_key_value[0]
value_states = past_key_value[1]
elif is_... | 10,086 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz) | 10,086 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
if self.is_decoder:
# if cross_attention save Tuple(tf.Tensor, tf.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (decoder... | 10,086 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
src_len = shape_list(key_states)[1]
attn_weights = tf.matmul(query_states, key_states, transpose_b=True)
tf.debugging.assert_equal(
shape_list(attn_weights),
[bsz * self.num_heads, tgt_len, src_len],
message=(
f"Attention weights should be of size {(b... | 10,086 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
attention_mask = tf.cast(attention_mask, dtype=attn_weights.dtype)
attn_weights = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len)) + attention_mask
attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len))
attn_weights = stable_softmax(attn_weigh... | 10,086 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
attn_probs = self.dropout(attn_weights, training=training)
attn_output = tf.matmul(attn_probs, value_states)
tf.debugging.assert_equal(
shape_list(attn_output),
[bsz * self.num_heads, tgt_len, self.head_dim],
message=(
f"`attn_output` should be of siz... | 10,086 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "k_proj", None) is not None:
with tf.name_scope(self.k_proj.name):
self.k_proj.build([None, None, self.embed_dim])
if getattr(self, "q_proj", None) is not N... | 10,086 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
class TFMBartEncoderLayer(keras.layers.Layer):
def __init__(self, config: MBartConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.d_model
self.self_attn = TFMBartAttention(
self.embed_dim, config.encoder_attention_heads, dropout=config.attention_dropout, name="... | 10,087 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
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 *(batch, seq_len, embed_dim)*
atten... | 10,087 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
tf.debugging.assert_equal(
shape_list(hidden_states),
shape_list(residual),
message=f"Self attn modified the shape of query {shape_list(residual)} to {shape_list(hidden_states)}",
)
hidden_states = self.dropout(hidden_states, training=training)
hidden_states ... | 10,087 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "self_attn", None) is not None:
with tf.name_scope(self.self_attn.name):
self.self_attn.build(None)
if getattr(self, "self_attn_layer_norm", None) is not No... | 10,087 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
class TFMBartDecoderLayer(keras.layers.Layer):
def __init__(self, config: MBartConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.d_model
self.self_attn = TFMBartAttention(
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_heads,
... | 10,088 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
self.self_attn_layer_norm = keras.layers.LayerNormalization(epsilon=1e-5, name="self_attn_layer_norm")
self.encoder_attn = TFMBartAttention(
self.embed_dim,
config.decoder_attention_heads,
dropout=config.attention_dropout,
name="encoder_attn",
is_decod... | 10,088 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor | None = None,
encoder_hidden_states: tf.Tensor | None = None,
encoder_attention_mask: tf.Tensor | None = None,
layer_head_mask: tf.Tensor | None = None,
cross_attn_layer_head_mask: tf.Tensor | No... | 10,088 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
*(batch, 1, tgt_len, src_len)* where padding elements are indicated by very large negative values.
layer_head_mask (`tf.Tensor`): mask for attention heads in a given layer of size
*(decoder_attention_heads,)*
cross_attn_layer_head_mask (`tf.Tensor`): mask for heads of the cross-a... | 10,088 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
# Self Attention
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
# add present self-attn cache to positions 1,2 of present_key_value tuple
hidden_states, self_attn_w... | 10,088 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
# cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn(
hidden_states=hidden_st... | 10,088 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
# Fully Connected
residual = hidden_states
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = self.activation_dropout(hidden_states, training=training)
hidden_states = self.fc2(hidden_states)
hi... | 10,088 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "self_attn", None) is not None:
with tf.name_scope(self.self_attn.name):
self.self_attn.build(None)
if getattr(self, "self_attn_layer_norm", None) is not No... | 10,088 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
if getattr(self, "fc2", None) is not None:
with tf.name_scope(self.fc2.name):
self.fc2.build([None, None, self.config.decoder_ffn_dim])
if getattr(self, "final_layer_norm", None) is not None:
with tf.name_scope(self.final_layer_norm.name):
self.final_layer... | 10,088 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
class TFMBartPreTrainedModel(TFPreTrainedModel):
config_class = MBartConfig
base_model_prefix = "model" | 10,089 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
class TFMBartEncoder(keras.layers.Layer):
config_class = MBartConfig
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`TFMBartEncoderLayer`].
Args:
config: MBartConfig
"""
def __init__(self, config: MBartConfig, embed_tokens: Opt... | 10,090 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
self.embed_tokens = embed_tokens
self.embed_positions = TFMBartLearnedPositionalEmbedding(
config.max_position_embeddings,
config.d_model,
name="embed_positions",
)
self.layers = [TFMBartEncoderLayer(config, name=f"layers.{i}") for i in range(config.encoder_la... | 10,090 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
@unpack_inputs
def call(
self,
input_ids: TFModelInputType | None = None,
inputs_embeds: tf.Tensor | None = None,
attention_mask: tf.Tensor | None = None,
head_mask: tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optio... | 10,090 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
[What are input IDs?](../glossary#input-ids)
attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
... | 10,090 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
inputs_embeds (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into assoc... | 10,090 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
for more detail. This argument can be used only in eager mode, in graph mode the value in the config
will be used instead.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used
i... | 10,090 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
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")
elif input_ids is not None:
input_shape = shape_list(input_ids)
elif inputs_embeds is not None:
input_shape = shape_list(in... | 10,090 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
# check attention mask and invert
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
attention_mask = _expand_mask(attention_mask)
else:
attention_mask = None
encoder_states = () if output_hidden_states else None
all... | 10,090 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
# encoder layers
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
dropout_probability = random.uniform(0, 1)
... | 10,090 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
if not return_dict:
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
return TFBaseModelOutput(
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
) | 10,090 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "embed_positions", None) is not None:
with tf.name_scope(self.embed_positions.name):
self.embed_positions.build(None)
if getattr(self, "layernorm_embedding"... | 10,090 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
class TFMBartDecoder(keras.layers.Layer):
config_class = MBartConfig
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`TFMBartDecoderLayer`]
Args:
config: MBartConfig
embed_tokens: output embedding
""" | 10,091 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
def __init__(self, config: MBartConfig, embed_tokens: Optional[keras.layers.Embedding] = None, **kwargs):
super().__init__(**kwargs)
self.config = config
self.padding_idx = config.pad_token_id
self.embed_tokens = embed_tokens
self.layerdrop = config.decoder_layerdrop
self... | 10,091 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
def get_embed_tokens(self):
return self.embed_tokens
def set_embed_tokens(self, embed_tokens):
self.embed_tokens = embed_tokens | 10,091 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
@unpack_inputs
def call(
self,
input_ids: TFModelInputType = None,
inputs_embeds: tf.Tensor | None = None,
attention_mask: tf.Tensor | None = None,
position_ids: tf.Tensor | None = None,
encoder_hidden_states: tf.Tensor | None = None,
encoder_attention_mask: t... | 10,091 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it. | 10,091 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
... | 10,091 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
[What are attention masks?](../glossary#attention-mask)
position_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
range `[0, config.max_position_embed... | 10,091 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
[What are attention masks?](../glossary#attention-mask)
head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked... | 10,091 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers` with each tuple having 2 tuples each of which has 2 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains precomputed key and value hidden-states of the attention blocks. Can be used to speed... | 10,091 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
input... | 10,091 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
in the config will be used instead.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail. This argument can be used only in eager mode, in graph mode the value in the co... | 10,091 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
elif input_ids is not None:
input_shape = shape_list(input_ids)
elif inputs_embeds is not None:
input_shape... | 10,091 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
hidden_states = inputs_embeds
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
if input_shape[-1] > 1:
combined_attention_mask = _make_causal_mask(input_shape, past_key_values_length=past_key_values_length)
else:
combined_attention_mask = _expand_mask(
... | 10,091 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
all_cross_attns = () if (output_attentions and encoder_hidden_states is not None) else None
present_key_values = () if use_cache else None
# check if head... | 10,091 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
for idx, decoder_layer in enumerate(self.layers):
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
if output_hidden_states:
all_hidden_states += (hidden_states,)
dropout_probability = random.uniform(0, 1)
if training and (dropout_pro... | 10,091 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
hidden_states, layer_self_attn, layer_cross_attn, present_key_value = decoder_layer(
hidden_states,
attention_mask=combined_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
layer_he... | 10,091 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
if not return_dict:
return hidden_states, present_key_values, all_hidden_states, all_self_attns, all_cross_attns
else:
return TFBaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=present_key_values,
... | 10,091 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "embed_positions", None) is not None:
with tf.name_scope(self.embed_positions.name):
self.embed_positions.build(None)
if getattr(self, "layernorm_embedding"... | 10,091 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
class TFMBartMainLayer(keras.layers.Layer):
config_class = MBartConfig
def __init__(self, config: MBartConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.shared = keras.layers.Embedding(
input_dim=config.vocab_size,
output_dim=config.d_model... | 10,092 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
def set_input_embeddings(self, new_embeddings):
self.shared = new_embeddings
self.encoder.embed_tokens = self.shared
self.decoder.embed_tokens = self.shared | 10,092 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
@unpack_inputs
def call(
self,
input_ids: TFModelInputType = None,
attention_mask: tf.Tensor | None = None,
decoder_input_ids: tf.Tensor | None = None,
decoder_attention_mask: tf.Tensor | None = None,
decoder_position_ids: tf.Tensor | None = None,
head_mask: t... | 10,092 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
if decoder_input_ids is None and decoder_inputs_embeds is None:
use_cache = False | 10,092 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
if decoder_input_ids is None and input_ids is not None:
decoder_input_ids = shift_tokens_right(input_ids, self.config.pad_token_id) | 10,092 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
if encoder_outputs is None:
encoder_outputs = self.encoder(
input_ids=input_ids,
attention_mask=attention_mask,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden... | 10,092 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
# If the user passed a TFBaseModelOutput for encoder_outputs, we wrap it in a tuple when return_dict=False
elif not return_dict and not isinstance(encoder_outputs, tuple):
encoder_outputs = encoder_outputs.to_tuple() | 10,092 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
decoder_outputs = self.decoder(
decoder_input_ids,
attention_mask=decoder_attention_mask,
position_ids=decoder_position_ids,
encoder_hidden_states=encoder_outputs[0],
encoder_attention_mask=attention_mask,
head_mask=decoder_head_mask,
c... | 10,092 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
return TFSeq2SeqModelOutput(
last_hidden_state=decoder_outputs.last_hidden_state,
past_key_values=decoder_outputs.past_key_values,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
cross_attentions=decoder_outp... | 10,092 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
# The shared/tied weights expect to be in the model base namespace
# Adding "/" to the end (not the start!) of a tf.name_scope puts it in the root namespace rather than
# the current one.
... | 10,092 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
class TFMBartModel(TFMBartPreTrainedModel):
def __init__(self, config: MBartConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.model = TFMBartMainLayer(config, name="model")
def get_encoder(self):
return self.model.encoder
def get_decoder(self):
r... | 10,093 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(MBART_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFSeq2SeqModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input... | 10,093 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
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,
... | 10,093 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
return_dict=return_dict,
training=training,
) | 10,093 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
return outputs
# Copied from transformers.models.bart.modeling_tf_bart.TFBartModel.serving_output
def serving_output(self, output):
pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None
dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidd... | 10,093 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
return TFSeq2SeqModelOutput(
last_hidden_state=output.last_hidden_state,
past_key_values=pkv,
decoder_hidden_states=dec_hs,
decoder_attentions=dec_attns,
cross_attentions=cross_attns,
encoder_last_hidden_state=output.encoder_last_hidden_state,
... | 10,093 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
class BiasLayer(keras.layers.Layer):
"""
Bias as a layer. It is used for serialization purposes: `keras.Model.save_weights` stores on a per-layer basis,
so all weights have to be registered in a layer.
"""
def __init__(self, shape, initializer, trainable, name, **kwargs):
super().__init__(n... | 10,094 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
class TFMBartForConditionalGeneration(TFMBartPreTrainedModel, TFCausalLanguageModelingLoss):
_keys_to_ignore_on_load_unexpected = [
r"model.encoder.embed_tokens.weight",
r"model.decoder.embed_tokens.weight",
]
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *... | 10,095 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
def get_bias(self):
return {"final_logits_bias": self.bias_layer.bias}
def set_bias(self, value):
# Replaces the existing layers containing bias for correct (de)serialization.
vocab_size = value["final_logits_bias"].shape[-1]
self.bias_layer = BiasLayer(
name="final_logi... | 10,095 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(MBART_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=TFSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
@add_end_docstrings(MBART_GENERATION_EXAMPLE)
def call(
self,
input_ids: TFModelInputType = None,
attention_mask: ... | 10,095 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: tf.Tensor | None = None,
training: Optional[bool] = False,
) -> Union[TFSeq2SeqLMOutput, Tuple[tf.Tensor]]:
"""
labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
... | 10,095 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
Returns:
"""
if labels is not None:
labels = tf.where(
labels == self.config.pad_token_id,
tf.cast(tf.fill(shape_list(labels), -100), labels.dtype),
labels,
)
use_cache = False
if decoder_input_ids is None ... | 10,095 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
outputs = self.model(
input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
encoder_outputs=encoder_outputs,
decoder_attention_mask=decoder_attention_mask,
decoder_position_ids=decoder_position_ids,
head_mask=he... | 10,095 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
masked_lm_loss = None if labels is None else self.hf_compute_loss(labels, lm_logits) | 10,095 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
if not return_dict:
output = (lm_logits,) + outputs[1:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return TFSeq2SeqLMOutput(
loss=masked_lm_loss,
logits=lm_logits,
past_key_values=outputs.past_key_values, # index 1 o... | 10,095 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
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