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# coding=utf-8
# Copyright 2021 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" TF 2.0 Bart model."""
from __future__ import annotations
import random
from typing import Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import (
TFBaseModelOutput,
TFBaseModelOutputWithPastAndCrossAttentions,
TFSeq2SeqLMOutput,
TFSeq2SeqModelOutput,
TFSeq2SeqSequenceClassifierOutput,
)
# Public API
from ...modeling_tf_utils import (
TFCausalLanguageModelingLoss,
TFModelInputType,
TFPreTrainedModel,
TFSequenceClassificationLoss,
keras_serializable,
unpack_inputs,
)
from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
from ...utils import (
ContextManagers,
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from .configuration_bart import BartConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "facebook/bart-large"
_CONFIG_FOR_DOC = "BartConfig"
LARGE_NEGATIVE = -1e8
def shift_tokens_right(input_ids: tf.Tensor, pad_token_id: int, decoder_start_token_id: int):
pad_token_id = tf.cast(pad_token_id, input_ids.dtype)
decoder_start_token_id = tf.cast(decoder_start_token_id, input_ids.dtype)
start_tokens = tf.fill(
(shape_list(input_ids)[0], 1), tf.convert_to_tensor(decoder_start_token_id, input_ids.dtype)
)
shifted_input_ids = tf.concat([start_tokens, input_ids[:, :-1]], -1)
# replace possible -100 values in labels by `pad_token_id`
shifted_input_ids = tf.where(
shifted_input_ids == -100,
tf.fill(shape_list(shifted_input_ids), tf.convert_to_tensor(pad_token_id, input_ids.dtype)),
shifted_input_ids,
)
# "Verify that `labels` has only positive values and -100"
assert_gte0 = tf.debugging.assert_greater_equal(shifted_input_ids, tf.constant(0, dtype=input_ids.dtype))
# Make sure the assertion op is called by wrapping the result in an identity no-op
with tf.control_dependencies([assert_gte0]):
shifted_input_ids = tf.identity(shifted_input_ids)
return shifted_input_ids
def _make_causal_mask(input_ids_shape: tf.TensorShape, past_key_values_length: int = 0):
"""
Make causal mask used for bi-directional self-attention.
"""
bsz = input_ids_shape[0]
tgt_len = input_ids_shape[1]
mask = tf.ones((tgt_len, tgt_len)) * LARGE_NEGATIVE
mask_cond = tf.range(shape_list(mask)[-1])
mask = tf.where(mask_cond < tf.reshape(mask_cond + 1, (shape_list(mask)[-1], 1)), 0.0, mask)
if past_key_values_length > 0:
mask = tf.concat([tf.zeros((tgt_len, past_key_values_length)), mask], axis=-1)
return tf.tile(mask[None, None, :, :], (bsz, 1, 1, 1))
def _expand_mask(mask: tf.Tensor, tgt_len: Optional[int] = None):
"""
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
"""
src_len = shape_list(mask)[1]
tgt_len = tgt_len if tgt_len is not None else src_len
one_cst = tf.constant(1.0)
mask = tf.cast(mask, dtype=one_cst.dtype)
expanded_mask = tf.tile(mask[:, None, None, :], (1, 1, tgt_len, 1))
return (one_cst - expanded_mask) * LARGE_NEGATIVE
class TFBartLearnedPositionalEmbedding(tf.keras.layers.Embedding):
"""
This module learns positional embeddings up to a fixed maximum size.
"""
def __init__(self, num_embeddings: int, embedding_dim: int, **kwargs):
# Bart is set up so that if padding_idx is specified then offset the embedding ids by 2
# and adjust num_embeddings appropriately. Other models don't have this hack
self.offset = 2
super().__init__(num_embeddings + self.offset, embedding_dim, **kwargs)
def call(
self,
input_shape: Optional[tf.TensorShape] = None,
past_key_values_length: int = 0,
position_ids: tf.Tensor | None = None,
):
"""Input is expected to be of size [bsz x seqlen]."""
if position_ids is None:
seq_len = input_shape[1]
position_ids = tf.range(seq_len, delta=1, name="range")
position_ids += past_key_values_length
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))
class TFBartAttention(tf.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().__init__(**kwargs)
self.embed_dim = embed_dim
self.num_heads = num_heads
self.dropout = tf.keras.layers.Dropout(dropout)
self.head_dim = embed_dim // num_heads
if (self.head_dim * num_heads) != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
f" and `num_heads`: {num_heads})."
)
self.scaling = self.head_dim**-0.5
self.is_decoder = is_decoder
self.k_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="k_proj")
self.q_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="q_proj")
self.v_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="v_proj")
self.out_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="out_proj")
def _shape(self, tensor: tf.Tensor, seq_len: int, bsz: int):
return tf.transpose(tf.reshape(tensor, (bsz, seq_len, self.num_heads, self.head_dim)), (0, 2, 1, 3))
def call(
self,
hidden_states: tf.Tensor,
key_value_states: tf.Tensor | None = None,
past_key_value: Tuple[Tuple[tf.Tensor]] | None = None,
attention_mask: tf.Tensor | None = None,
layer_head_mask: tf.Tensor | None = None,
training: Optional[bool] = False,
) -> Tuple[tf.Tensor, tf.Tensor | None]:
"""Input shape: Batch x Time x Channel"""
# 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)
# 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_cross_attention:
# cross_attentions
key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
elif past_key_value is not None:
# reuse k, v, self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
key_states = tf.concat([past_key_value[0], key_states], axis=2)
value_states = tf.concat([past_key_value[1], value_states], axis=2)
else:
# self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
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) save Tuple(tf.Tensor, tf.Tensor) of
# all previous decoder key/value_states. Further calls to uni-directional self-attention
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
# if encoder bi-directional self-attention `past_key_value` is always `None`
past_key_value = (key_states, value_states)
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
query_states = tf.reshape(self._shape(query_states, tgt_len, bsz), proj_shape)
key_states = tf.reshape(key_states, proj_shape)
value_states = tf.reshape(value_states, proj_shape)
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 {(bsz * self.num_heads, tgt_len, src_len)}, but is"
f" {shape_list(attn_weights)}"
),
)
if attention_mask is not None:
tf.debugging.assert_equal(
shape_list(attention_mask),
[bsz, 1, tgt_len, src_len],
message=(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is"
f" {shape_list(attention_mask)}"
),
)
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_weights, axis=-1)
if layer_head_mask is not None:
tf.debugging.assert_equal(
shape_list(layer_head_mask),
[self.num_heads],
message=(
f"Head mask for a single layer should be of size {(self.num_heads)}, but is"
f" {shape_list(layer_head_mask)}"
),
)
attn_weights = tf.reshape(layer_head_mask, (1, -1, 1, 1)) * tf.reshape(
attn_weights, (bsz, self.num_heads, tgt_len, src_len)
)
attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len))
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 size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
f" {shape_list(attn_output)}"
),
)
attn_output = tf.transpose(
tf.reshape(attn_output, (bsz, self.num_heads, tgt_len, self.head_dim)), (0, 2, 1, 3)
)
attn_output = tf.reshape(attn_output, (bsz, tgt_len, embed_dim))
attn_output = self.out_proj(attn_output)
attn_weights: tf.Tensor = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len))
return attn_output, attn_weights, past_key_value
class TFBartEncoderLayer(tf.keras.layers.Layer):
def __init__(self, config: BartConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.d_model
self.self_attn = TFBartAttention(
self.embed_dim, config.encoder_attention_heads, dropout=config.attention_dropout, name="self_attn"
)
self.self_attn_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="self_attn_layer_norm")
self.dropout = tf.keras.layers.Dropout(config.dropout)
self.activation_fn = get_tf_activation(config.activation_function)
self.activation_dropout = tf.keras.layers.Dropout(config.activation_dropout)
self.fc1 = tf.keras.layers.Dense(config.encoder_ffn_dim, name="fc1")
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: np.ndarray | tf.Tensor | None,
layer_head_mask: tf.Tensor | None,
training: Optional[bool] = False,
) -> tf.Tensor:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`tf.Tensor`): attention mask of size
`(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
`(encoder_attention_heads,)`
"""
residual = hidden_states
hidden_states, self_attn_weights, _ = self.self_attn(
hidden_states=hidden_states, attention_mask=attention_mask, layer_head_mask=layer_head_mask
)
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 = residual + hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
residual = 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)
hidden_states = self.dropout(hidden_states, training=training)
hidden_states = residual + hidden_states
hidden_states = self.final_layer_norm(hidden_states)
return hidden_states, self_attn_weights
class TFBartDecoderLayer(tf.keras.layers.Layer):
def __init__(self, config: BartConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.d_model
self.self_attn = TFBartAttention(
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_heads,
dropout=config.attention_dropout,
name="self_attn",
is_decoder=True,
)
self.dropout = tf.keras.layers.Dropout(config.dropout)
self.activation_fn = get_tf_activation(config.activation_function)
self.activation_dropout = tf.keras.layers.Dropout(config.activation_dropout)
self.self_attn_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="self_attn_layer_norm")
self.encoder_attn = TFBartAttention(
self.embed_dim,
config.decoder_attention_heads,
dropout=config.attention_dropout,
name="encoder_attn",
is_decoder=True,
)
self.encoder_attn_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="encoder_attn_layer_norm")
self.fc1 = tf.keras.layers.Dense(config.decoder_ffn_dim, name="fc1")
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: np.ndarray | tf.Tensor | None = None,
encoder_hidden_states: np.ndarray | tf.Tensor | None = None,
encoder_attention_mask: np.ndarray | tf.Tensor | None = None,
layer_head_mask: tf.Tensor | None = None,
cross_attn_layer_head_mask: tf.Tensor | None = None,
past_key_value: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None,
training: Optional[bool] = False,
) -> Tuple[tf.Tensor, tf.Tensor, Tuple[Tuple[tf.Tensor]]]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`tf.Tensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
encoder_hidden_states (`tf.Tensor`):
cross attention input to the layer of shape `(batch, seq_len, embed_dim)`
encoder_attention_mask (`tf.Tensor`): encoder attention mask of size
`(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-attention module.
`(decoder_attention_heads,)`
past_key_value (`Tuple(tf.Tensor)`): cached past key and value projection states
"""
residual = hidden_states
# 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_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
past_key_value=self_attn_past_key_value,
attention_mask=attention_mask,
layer_head_mask=layer_head_mask,
)
hidden_states = self.dropout(hidden_states, training=training)
hidden_states = residual + hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
# Cross-Attention Block
cross_attn_present_key_value = None
cross_attn_weights = None
if encoder_hidden_states is not None:
residual = hidden_states
# 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_states,
key_value_states=encoder_hidden_states,
attention_mask=encoder_attention_mask,
layer_head_mask=cross_attn_layer_head_mask,
past_key_value=cross_attn_past_key_value,
)
hidden_states = self.dropout(hidden_states, training=training)
hidden_states = residual + hidden_states
hidden_states = self.encoder_attn_layer_norm(hidden_states)
# add cross-attn to positions 3,4 of present_key_value tuple
present_key_value = present_key_value + cross_attn_present_key_value
# Fully Connected
residual = 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)
hidden_states = self.dropout(hidden_states, training=training)
hidden_states = residual + hidden_states
hidden_states = self.final_layer_norm(hidden_states)
return (
hidden_states,
self_attn_weights,
cross_attn_weights,
present_key_value,
)
class TFBartClassificationHead(tf.keras.layers.Layer):
"""Head for sentence-level classification tasks."""
def __init__(self, inner_dim: int, num_classes: int, pooler_dropout: float, name: str, **kwargs):
super().__init__(name=name, **kwargs)
self.dense = tf.keras.layers.Dense(inner_dim, name="dense")
self.dropout = tf.keras.layers.Dropout(pooler_dropout)
self.out_proj = tf.keras.layers.Dense(num_classes, name="out_proj")
def call(self, inputs):
hidden_states = self.dropout(inputs)
hidden_states = self.dense(hidden_states)
hidden_states = tf.keras.activations.tanh(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.out_proj(hidden_states)
return hidden_states
class TFBartPretrainedModel(TFPreTrainedModel):
config_class = BartConfig
base_model_prefix = "model"
@property
def dummy_inputs(self):
dummy_inputs = super().dummy_inputs
# Dummy inputs should not contain the default val of 1
# as this is the padding token and some assertions check it
dummy_inputs["input_ids"] = dummy_inputs["input_ids"] * 2
if "decoder_input_ids" in dummy_inputs:
dummy_inputs["decoder_input_ids"] = dummy_inputs["decoder_input_ids"] * 2
return dummy_inputs
BART_START_DOCSTRING = r"""
This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.
<Tip>
TensorFlow models and layers in `transformers` accept two formats as input:
- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional argument.
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just
pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second
format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with
the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:
- a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
- a dictionary with one or several input Tensors associated to the input names given in the docstring:
`model({"input_ids": input_ids, "token_type_ids": token_type_ids})`
Note that when creating models and layers with
[subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry
about any of this, as you can just pass inputs like you would to any other Python function!
</Tip>
Args:
config ([`BartConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
"""
BART_GENERATION_EXAMPLE = r"""
Summarization example:
```python
>>> from transformers import AutoTokenizer, TFBartForConditionalGeneration
>>> model = TFBartForConditionalGeneration.from_pretrained("facebook/bart-large")
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large")
>>> ARTICLE_TO_SUMMARIZE = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=1024, return_tensors="tf")
>>> # Generate Summary
>>> summary_ids = model.generate(inputs["input_ids"], num_beams=4, max_length=5)
>>> print(tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False))
```
Mask filling example:
```python
>>> from transformers import AutoTokenizer, TFBartForConditionalGeneration
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large")
>>> TXT = "My friends are <mask> but they eat too many carbs."
>>> model = TFBartForConditionalGeneration.from_pretrained("facebook/bart-large")
>>> input_ids = tokenizer([TXT], return_tensors="tf")["input_ids"]
>>> logits = model(input_ids).logits
>>> probs = tf.nn.softmax(logits[0])
>>> # probs[5] is associated with the mask token
```
"""
BART_INPUTS_DOCSTRING = r"""
Args:
input_ids (`tf.Tensor` of shape `({0})`):
Indices of input sequence tokens in the vocabulary.
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 `({0})`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
decoder_input_ids (`tf.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are decoder input IDs?](../glossary#decoder-input-ids)
Bart uses the `eos_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values`
is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`).
For translation and summarization training, `decoder_input_ids` should be provided. If no
`decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right
for denoising pre-training following the paper.
decoder_attention_mask (`tf.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*):
will be made by default and ignore pad tokens. It is not recommended to set this for most use cases.
decoder_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_embeddings - 1]`.
head_mask (`tf.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
decoder_head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
cross_attn_head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
encoder_outputs (`tf.FloatTensor`, *optional*):
hidden states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
of shape `(batch_size, sequence_length, hidden_size)` is a sequence of
past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers`)
contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
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)`.
use_cache (`bool`, *optional*, defaults to `True`):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`). Set to `False` during training, `True` during generation
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value 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 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 in
eager mode, in graph mode the value will always be set to True.
training (`bool`, *optional*, defaults to `False`):
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
"""
@keras_serializable
class TFBartEncoder(tf.keras.layers.Layer):
config_class = BartConfig
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`TFBartEncoderLayer`].
Args:
config: BartConfig
"""
def __init__(self, config: BartConfig, embed_tokens: Optional[tf.keras.layers.Embedding] = None, **kwargs):
super().__init__(**kwargs)
self.config = config
self.dropout = tf.keras.layers.Dropout(config.dropout)
self.layerdrop = config.encoder_layerdrop
self.padding_idx = config.pad_token_id
self.max_source_positions = config.max_position_embeddings
self.embed_scale = tf.math.sqrt(float(config.d_model)) if config.scale_embedding else 1.0
self.embed_tokens = embed_tokens
self.embed_positions = TFBartLearnedPositionalEmbedding(
config.max_position_embeddings,
config.d_model,
name="embed_positions",
)
self.layers = [TFBartEncoderLayer(config, name=f"layers.{i}") for i in range(config.encoder_layers)]
self.layernorm_embedding = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="layernorm_embedding")
@unpack_inputs
def call(
self,
input_ids: TFModelInputType | None = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
head_mask: np.ndarray | tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: Optional[bool] = False,
) -> Union[TFBaseModelOutput, Tuple[tf.Tensor]]:
"""
Args:
input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
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*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`tf.Tensor` of shape `(encoder_layers, encoder_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**,
- 0 indicates the head is **masked**.
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 associated vectors
than the model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
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.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
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(inputs_embeds)[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if inputs_embeds is None:
# if `self.embed_tokens.load_weight_prefix` is set, runs the embedding operation with the correct name
# scope, so that its weights are registered with the desired name for loading/storing. When `tf.name_scope`
# is used with a name ending in `/`, that name replaces the current name scope.
# (embeddings with tf.name_scope: self.embed_tokens.load_weight_prefix/self.embed_tokens.name/embeddings:0)
context = []
if hasattr(self.embed_tokens, "load_weight_prefix"):
context.append(tf.name_scope(self.embed_tokens.load_weight_prefix + "/"))
with ContextManagers(context):
check_embeddings_within_bounds(input_ids, self.embed_tokens.input_dim)
inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
embed_pos = self.embed_positions(input_shape)
hidden_states = inputs_embeds + embed_pos
hidden_states = self.layernorm_embedding(hidden_states)
hidden_states = self.dropout(hidden_states, training=training)
# 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_attentions = () if output_attentions else None
# check if head_mask has a correct number of layers specified if desired
if head_mask is not None:
tf.debugging.assert_equal(
shape_list(head_mask)[0],
len(self.layers),
message=(
f"The head_mask should be specified for {len(self.layers)} layers, but it is for"
f" {shape_list(head_mask)[0]}."
),
)
# 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)
if training and (dropout_probability < self.layerdrop): # skip the layer
continue
hidden_states, attn = encoder_layer(
hidden_states,
attention_mask,
head_mask[idx] if head_mask is not None else None,
)
if output_attentions:
all_attentions += (attn,)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
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
)
@keras_serializable
class TFBartDecoder(tf.keras.layers.Layer):
config_class = BartConfig
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`TFBartDecoderLayer`]
Args:
config: BartConfig
embed_tokens: output embedding
"""
def __init__(self, config: BartConfig, embed_tokens: Optional[tf.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.embed_positions = TFBartLearnedPositionalEmbedding(
config.max_position_embeddings,
config.d_model,
name="embed_positions",
)
self.embed_scale = tf.math.sqrt(float(config.d_model)) if config.scale_embedding else 1.0
self.layers = [TFBartDecoderLayer(config, name=f"layers.{i}") for i in range(config.decoder_layers)]
self.layernorm_embedding = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="layernorm_embedding")
self.dropout = tf.keras.layers.Dropout(config.dropout)
@unpack_inputs
def call(
self,
input_ids: TFModelInputType | None = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
encoder_hidden_states: np.ndarray | tf.Tensor | None = None,
encoder_attention_mask: np.ndarray | tf.Tensor | None = None,
head_mask: np.ndarray | tf.Tensor | None = None,
cross_attn_head_mask: np.ndarray | tf.Tensor | None = 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,
) -> Union[TFBaseModelOutputWithPastAndCrossAttentions, Tuple[tf.Tensor]]:
r"""
Args:
input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
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*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[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_embeddings - 1]`.
encoder_hidden_states (`tf.Tensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
of the decoder.
encoder_attention_mask (`tf.Tensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values
selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[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**,
- 0 indicates the head is **masked**.
cross_attn_head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
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 up
decoding.
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)`. 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 associated vectors than the model's internal embedding
lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
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.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
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 = shape_list(inputs_embeds)[:-1]
else:
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
past_key_values_length = shape_list(past_key_values[0][0])[2] if past_key_values is not None else 0
# embed positions
if position_ids is None:
positions = self.embed_positions(input_shape, past_key_values_length)
else:
positions = self.embed_positions(input_shape, position_ids=position_ids)
if inputs_embeds is None:
# if `self.embed_tokens.load_weight_prefix` is set, runs the embedding operation with the correct name
# scope, so that its weights are registered with the desired name for loading/storing. When `tf.name_scope`
# is used with a name ending in `/`, that name replaces the current name scope.
# (embeddings with tf.name_scope: self.embed_tokens.load_weight_prefix/self.embed_tokens.name/embeddings:0)
context = []
if hasattr(self.embed_tokens, "load_weight_prefix"):
context.append(tf.name_scope(self.embed_tokens.load_weight_prefix + "/"))
with ContextManagers(context):
check_embeddings_within_bounds(input_ids, self.embed_tokens.input_dim)
inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
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(
tf.ones((input_shape[0], input_shape[1] + past_key_values_length)), tgt_len=input_shape[-1]
)
if attention_mask is not None:
combined_attention_mask = combined_attention_mask + _expand_mask(attention_mask, tgt_len=input_shape[-1])
if encoder_hidden_states is not None and encoder_attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
encoder_attention_mask = _expand_mask(encoder_attention_mask, tgt_len=input_shape[-1])
hidden_states = self.layernorm_embedding(hidden_states + positions)
hidden_states = self.dropout(hidden_states, training=training)
# 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_mask and cross_attn_head_mask have a correct number of layers specified if desired
for attn_mask_name, attn_mask in [("head_mask", head_mask), ("cross_attn_head_mask", cross_attn_head_mask)]:
if attn_mask is not None:
tf.debugging.assert_equal(
shape_list(attn_mask)[0],
len(self.layers),
message=(
f"The {attn_mask_name} should be specified for {len(self.layers)} layers, but it is for"
f" {shape_list(attn_mask)[0]}."
),
)
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_probability < self.layerdrop):
continue
past_key_value = past_key_values[idx] if past_key_values is not None else None
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_head_mask=head_mask[idx] if head_mask is not None else None,
cross_attn_layer_head_mask=cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None,
past_key_value=past_key_value,
)
if use_cache:
present_key_values += (present_key_value,)
if output_attentions:
all_self_attns += (layer_self_attn,)
if encoder_hidden_states is not None:
all_cross_attns += (layer_cross_attn,)
if output_hidden_states:
all_hidden_states += (hidden_states,)
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,
hidden_states=all_hidden_states,
attentions=all_self_attns,
cross_attentions=all_cross_attns,
)
@keras_serializable
class TFBartMainLayer(tf.keras.layers.Layer):
config_class = BartConfig
def __init__(self, config: BartConfig, load_weight_prefix=None, **kwargs):
super().__init__(**kwargs)
self.config = config
self.shared = tf.keras.layers.Embedding(
input_dim=config.vocab_size,
output_dim=config.d_model,
embeddings_initializer=tf.keras.initializers.TruncatedNormal(stddev=self.config.init_std),
name="model.shared",
)
# Additional attribute to specify the expected name scope of the layer (for loading/storing weights)
self.shared.load_weight_prefix = "model.shared" if load_weight_prefix is None else load_weight_prefix
self.encoder = TFBartEncoder(config, self.shared, name="encoder")
self.decoder = TFBartDecoder(config, self.shared, name="decoder")
def get_input_embeddings(self):
return self.shared
def set_input_embeddings(self, new_embeddings):
self.shared = new_embeddings
self.encoder.embed_tokens = self.shared
self.decoder.embed_tokens = self.shared
@unpack_inputs
def call(
self,
input_ids: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
decoder_input_ids: np.ndarray | tf.Tensor | None = None,
decoder_attention_mask: np.ndarray | tf.Tensor | None = None,
decoder_position_ids: np.ndarray | tf.Tensor | None = None,
head_mask: np.ndarray | tf.Tensor | None = None,
decoder_head_mask: np.ndarray | tf.Tensor | None = None,
cross_attn_head_mask: np.ndarray | tf.Tensor | None = None,
encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None,
past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
decoder_inputs_embeds: np.ndarray | tf.Tensor | None = 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]]:
# different to other models, Bart automatically creates decoder_input_ids from
# input_ids if no decoder_input_ids are provided
if decoder_input_ids is None and decoder_inputs_embeds is None:
if input_ids is None:
raise ValueError(
"If no `decoder_input_ids` or `decoder_inputs_embeds` are "
"passed, `input_ids` cannot be `None`. Please pass either "
"`input_ids` or `decoder_input_ids` or `decoder_inputs_embeds`."
)
decoder_input_ids = shift_tokens_right(
input_ids, self.config.pad_token_id, self.config.decoder_start_token_id
)
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_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
# If the user passed a tuple for encoder_outputs, we wrap it in a TFBaseModelOutput when return_dict=True
elif return_dict and not isinstance(encoder_outputs, TFBaseModelOutput):
encoder_outputs = TFBaseModelOutput(
last_hidden_state=encoder_outputs[0],
hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
)
# 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()
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,
cross_attn_head_mask=cross_attn_head_mask,
past_key_values=past_key_values,
inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
if not return_dict:
return decoder_outputs + encoder_outputs
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_outputs.cross_attentions,
encoder_last_hidden_state=encoder_outputs.last_hidden_state,
encoder_hidden_states=encoder_outputs.hidden_states,
encoder_attentions=encoder_outputs.attentions,
)
@add_start_docstrings(
"The bare BART Model outputting raw hidden-states without any specific head on top.",
BART_START_DOCSTRING,
)
class TFBartModel(TFBartPretrainedModel):
_requires_load_weight_prefix = True
def __init__(self, config: BartConfig, load_weight_prefix=None, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.model = TFBartMainLayer(config, load_weight_prefix=load_weight_prefix, name="model")
def get_encoder(self):
return self.model.encoder
def get_decoder(self):
return self.model.decoder
@add_start_docstrings_to_model_forward(BART_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFSeq2SeqModelOutput,
config_class=_CONFIG_FOR_DOC,
)
@unpack_inputs
def call(
self,
input_ids: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
decoder_input_ids: np.ndarray | tf.Tensor | None = None,
decoder_attention_mask: np.ndarray | tf.Tensor | None = None,
decoder_position_ids: np.ndarray | tf.Tensor | None = None,
head_mask: np.ndarray | tf.Tensor | None = None,
decoder_head_mask: np.ndarray | tf.Tensor | None = None,
cross_attn_head_mask: np.ndarray | tf.Tensor | None = None,
encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None,
past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
decoder_inputs_embeds: np.ndarray | tf.Tensor | None = 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[TFBaseModelOutput, Tuple[tf.Tensor]]:
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
decoder_position_ids=decoder_position_ids,
head_mask=head_mask,
decoder_head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
encoder_outputs=encoder_outputs,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
decoder_inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
return outputs
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_hidden_states else None
dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None
cross_attns = tf.convert_to_tensor(output.cross_attentions) if self.config.output_attentions else None
enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None
enc_attns = tf.convert_to_tensor(output.encoder_attentions) if self.config.output_attentions else None
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,
encoder_hidden_states=enc_hs,
encoder_attentions=enc_attns,
)
class BiasLayer(tf.keras.layers.Layer):
"""
Bias as a layer. It is used for serialization purposes: `tf.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__(name=name, **kwargs)
# Note: the name of this variable will NOT be scoped when serialized, i.e. it will not be in the format of
# "outer_layer/inner_layer/.../name:0". Instead, it will be "name:0". For further details, see:
# https://github.com/huggingface/transformers/pull/18833#issuecomment-1233090214
self.bias = self.add_weight(name=name, shape=shape, initializer=initializer, trainable=trainable)
def call(self, x):
return x + self.bias
@add_start_docstrings(
"The BART Model with a language modeling head. Can be used for summarization.",
BART_START_DOCSTRING,
)
class TFBartForConditionalGeneration(TFBartPretrainedModel, TFCausalLanguageModelingLoss):
_keys_to_ignore_on_load_missing = [r"final_logits_bias"]
_requires_load_weight_prefix = True
def __init__(self, config, load_weight_prefix=None, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.model = TFBartMainLayer(config, load_weight_prefix=load_weight_prefix, name="model")
self.use_cache = config.use_cache
# final_bias_logits is registered as a buffer in pytorch, so not trainable for the sake of consistency.
self.bias_layer = BiasLayer(
name="final_logits_bias", shape=[1, config.vocab_size], initializer="zeros", trainable=False
)
def get_decoder(self):
return self.model.decoder
def get_encoder(self):
return self.model.encoder
def get_output_embeddings(self):
return self.get_input_embeddings()
def set_output_embeddings(self, value):
self.set_input_embeddings(value)
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_logits_bias", shape=[1, vocab_size], initializer="zeros", trainable=False
)
self.bias_layer.bias.assign(value["final_logits_bias"])
@add_start_docstrings_to_model_forward(BART_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=TFSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
@add_end_docstrings(BART_GENERATION_EXAMPLE)
@unpack_inputs
def call(
self,
input_ids: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
decoder_input_ids: np.ndarray | tf.Tensor | None = None,
decoder_attention_mask: np.ndarray | tf.Tensor | None = None,
decoder_position_ids: np.ndarray | tf.Tensor | None = None,
head_mask: np.ndarray | tf.Tensor | None = None,
decoder_head_mask: np.ndarray | tf.Tensor | None = None,
cross_attn_head_mask: np.ndarray | tf.Tensor | None = None,
encoder_outputs: Optional[TFBaseModelOutput] = None,
past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
decoder_inputs_embeds: np.ndarray | tf.Tensor | None = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
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]]:
r"""
labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
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 and decoder_inputs_embeds is None:
decoder_input_ids = shift_tokens_right(
labels, self.config.pad_token_id, self.config.decoder_start_token_id
)
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=head_mask,
decoder_head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
decoder_inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
lm_logits = tf.matmul(outputs[0], self.model.shared.weights, transpose_b=True)
lm_logits = self.bias_layer(lm_logits)
masked_lm_loss = None if labels is None else self.hf_compute_loss(labels, lm_logits)
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 of d outputs
decoder_hidden_states=outputs.decoder_hidden_states, # index 2 of d outputs
decoder_attentions=outputs.decoder_attentions, # index 3 of d outputs
cross_attentions=outputs.cross_attentions, # index 4 of d outputs
encoder_last_hidden_state=outputs.encoder_last_hidden_state, # index 0 of encoder outputs
encoder_hidden_states=outputs.encoder_hidden_states, # 1 of e out
encoder_attentions=outputs.encoder_attentions, # 2 of e out
)
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_hidden_states else None
dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None
cross_attns = tf.convert_to_tensor(output.cross_attentions) if self.config.output_attentions else None
enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None
enc_attns = tf.convert_to_tensor(output.encoder_attentions) if self.config.output_attentions else None
return TFSeq2SeqLMOutput(
logits=output.logits,
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,
encoder_hidden_states=enc_hs,
encoder_attentions=enc_attns,
)
def prepare_inputs_for_generation(
self,
decoder_input_ids,
past_key_values=None,
attention_mask=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
use_cache=None,
encoder_outputs=None,
**kwargs,
):
# cut decoder_input_ids if past_key_values is used
if past_key_values is not None:
decoder_input_ids = decoder_input_ids[:, -1:]
if decoder_attention_mask is not None: # xla
decoder_position_ids = tf.math.cumsum(decoder_attention_mask, axis=-1, exclusive=True)[:, -1:]
elif past_key_values is not None: # no xla + past_key_values
decoder_position_ids = past_key_values[0][0].shape[2]
else: # no xla + no past_key_values
decoder_position_ids = tf.range(decoder_input_ids.shape[1])
return {
"input_ids": None, # encoder_outputs is defined. input_ids not needed
"encoder_outputs": encoder_outputs,
"past_key_values": past_key_values,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"decoder_position_ids": decoder_position_ids,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
"use_cache": use_cache, # change this to avoid caching (presumably for debugging)
}
def prepare_decoder_input_ids_from_labels(self, labels: tf.Tensor):
return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)
@add_start_docstrings(
"""
Bart model with a sequence classification/head on top (a linear layer on top of the pooled output) e.g. for GLUE
tasks.
""",
BART_START_DOCSTRING,
)
class TFBartForSequenceClassification(TFBartPretrainedModel, TFSequenceClassificationLoss):
def __init__(self, config: BartConfig, load_weight_prefix=None, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.model = TFBartMainLayer(config, load_weight_prefix=load_weight_prefix, name="model")
self.classification_head = TFBartClassificationHead(
config.d_model, config.num_labels, config.classifier_dropout, name="classification_head"
)
@add_start_docstrings_to_model_forward(BART_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=TFSeq2SeqSequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
@unpack_inputs
def call(
self,
input_ids: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
decoder_input_ids: np.ndarray | tf.Tensor | None = None,
decoder_attention_mask: np.ndarray | tf.Tensor | None = None,
decoder_position_ids: np.ndarray | tf.Tensor | None = None,
head_mask: np.ndarray | tf.Tensor | None = None,
decoder_head_mask: np.ndarray | tf.Tensor | None = None,
cross_attn_head_mask: np.ndarray | tf.Tensor | None = None,
encoder_outputs: Optional[TFBaseModelOutput] = None,
past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
decoder_inputs_embeds: np.ndarray | tf.Tensor | None = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: tf.Tensor | None = None,
training: Optional[bool] = False,
) -> Union[TFSeq2SeqSequenceClassifierOutput, Tuple[tf.Tensor]]:
r"""
labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
Returns:
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if labels is not None:
use_cache = False
if input_ids is None and inputs_embeds is not None:
raise NotImplementedError(
f"Passing input embeddings is currently not supported for {self.__class__.__name__}"
)
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
decoder_position_ids=decoder_position_ids,
head_mask=head_mask,
decoder_head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
encoder_outputs=encoder_outputs,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
decoder_inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
last_hidden_state = outputs[0]
eos_mask = tf.equal(input_ids, self.config.eos_token_id)
# out the rows with False where present. Then verify all the final
# entries are True
self_masked = tf.reshape(tf.boolean_mask(eos_mask, eos_mask), (tf.shape(input_ids)[0], -1))
tf.Assert(tf.reduce_all(self_masked[:, -1]), ["All examples must have the same number of <eos> tokens."])
masked = tf.reshape(
tf.boolean_mask(last_hidden_state, eos_mask),
(tf.shape(input_ids)[0], tf.shape(self_masked)[1], tf.shape(last_hidden_state)[-1]),
)
sentence_representation = masked[:, -1, :]
logits = self.classification_head(sentence_representation)
loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=logits)
if not return_dict:
output = (logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return TFSeq2SeqSequenceClassifierOutput(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
decoder_hidden_states=outputs.decoder_hidden_states,
decoder_attentions=outputs.decoder_attentions,
cross_attentions=outputs.cross_attentions,
encoder_last_hidden_state=outputs.encoder_last_hidden_state,
encoder_hidden_states=outputs.encoder_hidden_states,
encoder_attentions=outputs.encoder_attentions,
)
def serving_output(self, output):
logits = tf.convert_to_tensor(output.logits)
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_hidden_states else None
dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None
cross_attns = tf.convert_to_tensor(output.cross_attentions) if self.config.output_attentions else None
enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None
enc_attns = tf.convert_to_tensor(output.encoder_attentions) if self.config.output_attentions else None
return TFSeq2SeqSequenceClassifierOutput(
logits=logits,
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,
encoder_hidden_states=enc_hs,
encoder_attentions=enc_attns,
)
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