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# coding=utf-8 | |
# Copyright 2021 Microsoft 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 DeBERTa model.""" | |
from __future__ import annotations | |
import math | |
from typing import Dict, Optional, Sequence, Tuple, Union | |
import numpy as np | |
import tensorflow as tf | |
from ...activations_tf import get_tf_activation | |
from ...modeling_tf_outputs import ( | |
TFBaseModelOutput, | |
TFMaskedLMOutput, | |
TFQuestionAnsweringModelOutput, | |
TFSequenceClassifierOutput, | |
TFTokenClassifierOutput, | |
) | |
from ...modeling_tf_utils import ( | |
TFMaskedLanguageModelingLoss, | |
TFModelInputType, | |
TFPreTrainedModel, | |
TFQuestionAnsweringLoss, | |
TFSequenceClassificationLoss, | |
TFTokenClassificationLoss, | |
get_initializer, | |
unpack_inputs, | |
) | |
from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax | |
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging | |
from .configuration_deberta import DebertaConfig | |
logger = logging.get_logger(__name__) | |
_CONFIG_FOR_DOC = "DebertaConfig" | |
_CHECKPOINT_FOR_DOC = "kamalkraj/deberta-base" | |
TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = [ | |
"kamalkraj/deberta-base", | |
# See all DeBERTa models at https://huggingface.co/models?filter=DeBERTa | |
] | |
class TFDebertaContextPooler(tf.keras.layers.Layer): | |
def __init__(self, config: DebertaConfig, **kwargs): | |
super().__init__(**kwargs) | |
self.dense = tf.keras.layers.Dense(config.pooler_hidden_size, name="dense") | |
self.dropout = TFDebertaStableDropout(config.pooler_dropout, name="dropout") | |
self.config = config | |
def call(self, hidden_states, training: bool = False): | |
# We "pool" the model by simply taking the hidden state corresponding | |
# to the first token. | |
context_token = hidden_states[:, 0] | |
context_token = self.dropout(context_token, training=training) | |
pooled_output = self.dense(context_token) | |
pooled_output = get_tf_activation(self.config.pooler_hidden_act)(pooled_output) | |
return pooled_output | |
def output_dim(self) -> int: | |
return self.config.hidden_size | |
class TFDebertaXSoftmax(tf.keras.layers.Layer): | |
""" | |
Masked Softmax which is optimized for saving memory | |
Args: | |
input (`tf.Tensor`): The input tensor that will apply softmax. | |
mask (`tf.Tensor`): The mask matrix where 0 indicate that element will be ignored in the softmax calculation. | |
dim (int): The dimension that will apply softmax | |
""" | |
def __init__(self, axis=-1, **kwargs): | |
super().__init__(**kwargs) | |
self.axis = axis | |
def call(self, inputs: tf.Tensor, mask: tf.Tensor): | |
rmask = tf.logical_not(tf.cast(mask, tf.bool)) | |
output = tf.where(rmask, float("-inf"), inputs) | |
output = stable_softmax(output, self.axis) | |
output = tf.where(rmask, 0.0, output) | |
return output | |
class TFDebertaStableDropout(tf.keras.layers.Layer): | |
""" | |
Optimized dropout module for stabilizing the training | |
Args: | |
drop_prob (float): the dropout probabilities | |
""" | |
def __init__(self, drop_prob, **kwargs): | |
super().__init__(**kwargs) | |
self.drop_prob = drop_prob | |
def xdropout(self, inputs): | |
""" | |
Applies dropout to the inputs, as vanilla dropout, but also scales the remaining elements up by 1/drop_prob. | |
""" | |
mask = tf.cast( | |
1 | |
- tf.compat.v1.distributions.Bernoulli(probs=1.0 - self.drop_prob).sample(sample_shape=shape_list(inputs)), | |
tf.bool, | |
) | |
scale = tf.convert_to_tensor(1.0 / (1 - self.drop_prob), dtype=tf.float32) | |
if self.drop_prob > 0: | |
inputs = tf.where(mask, 0.0, inputs) * scale | |
def grad(upstream): | |
if self.drop_prob > 0: | |
return tf.where(mask, 0.0, upstream) * scale | |
else: | |
return upstream | |
return inputs, grad | |
def call(self, inputs: tf.Tensor, training: tf.Tensor = False): | |
if training: | |
return self.xdropout(inputs) | |
return inputs | |
class TFDebertaLayerNorm(tf.keras.layers.Layer): | |
"""LayerNorm module in the TF style (epsilon inside the square root).""" | |
def __init__(self, size, eps=1e-12, **kwargs): | |
super().__init__(**kwargs) | |
self.size = size | |
self.eps = eps | |
def build(self, input_shape): | |
self.gamma = self.add_weight(shape=[self.size], initializer=tf.ones_initializer(), name="weight") | |
self.beta = self.add_weight(shape=[self.size], initializer=tf.zeros_initializer(), name="bias") | |
return super().build(input_shape) | |
def call(self, x: tf.Tensor) -> tf.Tensor: | |
mean = tf.reduce_mean(x, axis=[-1], keepdims=True) | |
variance = tf.reduce_mean(tf.square(x - mean), axis=[-1], keepdims=True) | |
std = tf.math.sqrt(variance + self.eps) | |
return self.gamma * (x - mean) / std + self.beta | |
class TFDebertaSelfOutput(tf.keras.layers.Layer): | |
def __init__(self, config: DebertaConfig, **kwargs): | |
super().__init__(**kwargs) | |
self.dense = tf.keras.layers.Dense(config.hidden_size, name="dense") | |
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") | |
self.dropout = TFDebertaStableDropout(config.hidden_dropout_prob, name="dropout") | |
def call(self, hidden_states, input_tensor, training: bool = False): | |
hidden_states = self.dense(hidden_states) | |
hidden_states = self.dropout(hidden_states, training=training) | |
hidden_states = self.LayerNorm(hidden_states + input_tensor) | |
return hidden_states | |
class TFDebertaAttention(tf.keras.layers.Layer): | |
def __init__(self, config: DebertaConfig, **kwargs): | |
super().__init__(**kwargs) | |
self.self = TFDebertaDisentangledSelfAttention(config, name="self") | |
self.dense_output = TFDebertaSelfOutput(config, name="output") | |
self.config = config | |
def call( | |
self, | |
input_tensor: tf.Tensor, | |
attention_mask: tf.Tensor, | |
query_states: tf.Tensor = None, | |
relative_pos: tf.Tensor = None, | |
rel_embeddings: tf.Tensor = None, | |
output_attentions: bool = False, | |
training: bool = False, | |
) -> Tuple[tf.Tensor]: | |
self_outputs = self.self( | |
hidden_states=input_tensor, | |
attention_mask=attention_mask, | |
query_states=query_states, | |
relative_pos=relative_pos, | |
rel_embeddings=rel_embeddings, | |
output_attentions=output_attentions, | |
training=training, | |
) | |
if query_states is None: | |
query_states = input_tensor | |
attention_output = self.dense_output( | |
hidden_states=self_outputs[0], input_tensor=query_states, training=training | |
) | |
output = (attention_output,) + self_outputs[1:] | |
return output | |
class TFDebertaIntermediate(tf.keras.layers.Layer): | |
def __init__(self, config: DebertaConfig, **kwargs): | |
super().__init__(**kwargs) | |
self.dense = tf.keras.layers.Dense( | |
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" | |
) | |
if isinstance(config.hidden_act, str): | |
self.intermediate_act_fn = get_tf_activation(config.hidden_act) | |
else: | |
self.intermediate_act_fn = config.hidden_act | |
def call(self, hidden_states: tf.Tensor) -> tf.Tensor: | |
hidden_states = self.dense(inputs=hidden_states) | |
hidden_states = self.intermediate_act_fn(hidden_states) | |
return hidden_states | |
class TFDebertaOutput(tf.keras.layers.Layer): | |
def __init__(self, config: DebertaConfig, **kwargs): | |
super().__init__(**kwargs) | |
self.dense = tf.keras.layers.Dense( | |
units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" | |
) | |
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") | |
self.dropout = TFDebertaStableDropout(config.hidden_dropout_prob, name="dropout") | |
def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor: | |
hidden_states = self.dense(inputs=hidden_states) | |
hidden_states = self.dropout(hidden_states, training=training) | |
hidden_states = self.LayerNorm(hidden_states + input_tensor) | |
return hidden_states | |
class TFDebertaLayer(tf.keras.layers.Layer): | |
def __init__(self, config: DebertaConfig, **kwargs): | |
super().__init__(**kwargs) | |
self.attention = TFDebertaAttention(config, name="attention") | |
self.intermediate = TFDebertaIntermediate(config, name="intermediate") | |
self.bert_output = TFDebertaOutput(config, name="output") | |
def call( | |
self, | |
hidden_states: tf.Tensor, | |
attention_mask: tf.Tensor, | |
query_states: tf.Tensor = None, | |
relative_pos: tf.Tensor = None, | |
rel_embeddings: tf.Tensor = None, | |
output_attentions: bool = False, | |
training: bool = False, | |
) -> Tuple[tf.Tensor]: | |
attention_outputs = self.attention( | |
input_tensor=hidden_states, | |
attention_mask=attention_mask, | |
query_states=query_states, | |
relative_pos=relative_pos, | |
rel_embeddings=rel_embeddings, | |
output_attentions=output_attentions, | |
training=training, | |
) | |
attention_output = attention_outputs[0] | |
intermediate_output = self.intermediate(hidden_states=attention_output) | |
layer_output = self.bert_output( | |
hidden_states=intermediate_output, input_tensor=attention_output, training=training | |
) | |
outputs = (layer_output,) + attention_outputs[1:] # add attentions if we output them | |
return outputs | |
class TFDebertaEncoder(tf.keras.layers.Layer): | |
def __init__(self, config: DebertaConfig, **kwargs): | |
super().__init__(**kwargs) | |
self.layer = [TFDebertaLayer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)] | |
self.relative_attention = getattr(config, "relative_attention", False) | |
self.config = config | |
if self.relative_attention: | |
self.max_relative_positions = getattr(config, "max_relative_positions", -1) | |
if self.max_relative_positions < 1: | |
self.max_relative_positions = config.max_position_embeddings | |
def build(self, input_shape): | |
if self.relative_attention: | |
self.rel_embeddings = self.add_weight( | |
name="rel_embeddings.weight", | |
shape=[self.max_relative_positions * 2, self.config.hidden_size], | |
initializer=get_initializer(self.config.initializer_range), | |
) | |
return super().build(input_shape) | |
def get_rel_embedding(self): | |
rel_embeddings = self.rel_embeddings if self.relative_attention else None | |
return rel_embeddings | |
def get_attention_mask(self, attention_mask): | |
if len(shape_list(attention_mask)) <= 2: | |
extended_attention_mask = tf.expand_dims(tf.expand_dims(attention_mask, 1), 2) | |
attention_mask = extended_attention_mask * tf.expand_dims(tf.squeeze(extended_attention_mask, -2), -1) | |
attention_mask = tf.cast(attention_mask, tf.uint8) | |
elif len(shape_list(attention_mask)) == 3: | |
attention_mask = tf.expand_dims(attention_mask, 1) | |
return attention_mask | |
def get_rel_pos(self, hidden_states, query_states=None, relative_pos=None): | |
if self.relative_attention and relative_pos is None: | |
q = shape_list(query_states)[-2] if query_states is not None else shape_list(hidden_states)[-2] | |
relative_pos = build_relative_position(q, shape_list(hidden_states)[-2]) | |
return relative_pos | |
def call( | |
self, | |
hidden_states: tf.Tensor, | |
attention_mask: tf.Tensor, | |
query_states: tf.Tensor = None, | |
relative_pos: tf.Tensor = None, | |
output_attentions: bool = False, | |
output_hidden_states: bool = False, | |
return_dict: bool = True, | |
training: bool = False, | |
) -> Union[TFBaseModelOutput, Tuple[tf.Tensor]]: | |
all_hidden_states = () if output_hidden_states else None | |
all_attentions = () if output_attentions else None | |
attention_mask = self.get_attention_mask(attention_mask) | |
relative_pos = self.get_rel_pos(hidden_states, query_states, relative_pos) | |
if isinstance(hidden_states, Sequence): | |
next_kv = hidden_states[0] | |
else: | |
next_kv = hidden_states | |
rel_embeddings = self.get_rel_embedding() | |
for i, layer_module in enumerate(self.layer): | |
if output_hidden_states: | |
all_hidden_states = all_hidden_states + (hidden_states,) | |
layer_outputs = layer_module( | |
hidden_states=next_kv, | |
attention_mask=attention_mask, | |
query_states=query_states, | |
relative_pos=relative_pos, | |
rel_embeddings=rel_embeddings, | |
output_attentions=output_attentions, | |
training=training, | |
) | |
hidden_states = layer_outputs[0] | |
if query_states is not None: | |
query_states = hidden_states | |
if isinstance(hidden_states, Sequence): | |
next_kv = hidden_states[i + 1] if i + 1 < len(self.layer) else None | |
else: | |
next_kv = hidden_states | |
if output_attentions: | |
all_attentions = all_attentions + (layer_outputs[1],) | |
# Add last layer | |
if output_hidden_states: | |
all_hidden_states = all_hidden_states + (hidden_states,) | |
if not return_dict: | |
return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None) | |
return TFBaseModelOutput( | |
last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions | |
) | |
def build_relative_position(query_size, key_size): | |
""" | |
Build relative position according to the query and key | |
We assume the absolute position of query \\(P_q\\) is range from (0, query_size) and the absolute position of key | |
\\(P_k\\) is range from (0, key_size), The relative positions from query to key is \\(R_{q \\rightarrow k} = P_q - | |
P_k\\) | |
Args: | |
query_size (int): the length of query | |
key_size (int): the length of key | |
Return: | |
`tf.Tensor`: A tensor with shape [1, query_size, key_size] | |
""" | |
q_ids = tf.range(query_size, dtype=tf.int32) | |
k_ids = tf.range(key_size, dtype=tf.int32) | |
rel_pos_ids = q_ids[:, None] - tf.tile(tf.reshape(k_ids, [1, -1]), [query_size, 1]) | |
rel_pos_ids = rel_pos_ids[:query_size, :] | |
rel_pos_ids = tf.expand_dims(rel_pos_ids, axis=0) | |
return tf.cast(rel_pos_ids, tf.int64) | |
def c2p_dynamic_expand(c2p_pos, query_layer, relative_pos): | |
shapes = [ | |
shape_list(query_layer)[0], | |
shape_list(query_layer)[1], | |
shape_list(query_layer)[2], | |
shape_list(relative_pos)[-1], | |
] | |
return tf.broadcast_to(c2p_pos, shapes) | |
def p2c_dynamic_expand(c2p_pos, query_layer, key_layer): | |
shapes = [ | |
shape_list(query_layer)[0], | |
shape_list(query_layer)[1], | |
shape_list(key_layer)[-2], | |
shape_list(key_layer)[-2], | |
] | |
return tf.broadcast_to(c2p_pos, shapes) | |
def pos_dynamic_expand(pos_index, p2c_att, key_layer): | |
shapes = shape_list(p2c_att)[:2] + [shape_list(pos_index)[-2], shape_list(key_layer)[-2]] | |
return tf.broadcast_to(pos_index, shapes) | |
def torch_gather(x, indices, gather_axis): | |
if gather_axis < 0: | |
gather_axis = tf.rank(x) + gather_axis | |
if gather_axis != tf.rank(x) - 1: | |
pre_roll = tf.rank(x) - 1 - gather_axis | |
permutation = tf.roll(tf.range(tf.rank(x)), pre_roll, axis=0) | |
x = tf.transpose(x, perm=permutation) | |
indices = tf.transpose(indices, perm=permutation) | |
else: | |
pre_roll = 0 | |
flat_x = tf.reshape(x, (-1, tf.shape(x)[-1])) | |
flat_indices = tf.reshape(indices, (-1, tf.shape(indices)[-1])) | |
gathered = tf.gather(flat_x, flat_indices, batch_dims=1) | |
gathered = tf.reshape(gathered, tf.shape(indices)) | |
if pre_roll != 0: | |
permutation = tf.roll(tf.range(tf.rank(x)), -pre_roll, axis=0) | |
gathered = tf.transpose(gathered, perm=permutation) | |
return gathered | |
class TFDebertaDisentangledSelfAttention(tf.keras.layers.Layer): | |
""" | |
Disentangled self-attention module | |
Parameters: | |
config (`str`): | |
A model config class instance with the configuration to build a new model. The schema is similar to | |
*BertConfig*, for more details, please refer [`DebertaConfig`] | |
""" | |
def __init__(self, config: DebertaConfig, **kwargs): | |
super().__init__(**kwargs) | |
if config.hidden_size % config.num_attention_heads != 0: | |
raise ValueError( | |
f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " | |
f"heads ({config.num_attention_heads})" | |
) | |
self.num_attention_heads = config.num_attention_heads | |
self.attention_head_size = int(config.hidden_size / config.num_attention_heads) | |
self.all_head_size = self.num_attention_heads * self.attention_head_size | |
self.in_proj = tf.keras.layers.Dense( | |
self.all_head_size * 3, | |
kernel_initializer=get_initializer(config.initializer_range), | |
name="in_proj", | |
use_bias=False, | |
) | |
self.pos_att_type = config.pos_att_type if config.pos_att_type is not None else [] | |
self.relative_attention = getattr(config, "relative_attention", False) | |
self.talking_head = getattr(config, "talking_head", False) | |
if self.talking_head: | |
self.head_logits_proj = tf.keras.layers.Dense( | |
self.num_attention_heads, | |
kernel_initializer=get_initializer(config.initializer_range), | |
name="head_logits_proj", | |
use_bias=False, | |
) | |
self.head_weights_proj = tf.keras.layers.Dense( | |
self.num_attention_heads, | |
kernel_initializer=get_initializer(config.initializer_range), | |
name="head_weights_proj", | |
use_bias=False, | |
) | |
self.softmax = TFDebertaXSoftmax(axis=-1) | |
if self.relative_attention: | |
self.max_relative_positions = getattr(config, "max_relative_positions", -1) | |
if self.max_relative_positions < 1: | |
self.max_relative_positions = config.max_position_embeddings | |
self.pos_dropout = TFDebertaStableDropout(config.hidden_dropout_prob, name="pos_dropout") | |
if "c2p" in self.pos_att_type: | |
self.pos_proj = tf.keras.layers.Dense( | |
self.all_head_size, | |
kernel_initializer=get_initializer(config.initializer_range), | |
name="pos_proj", | |
use_bias=False, | |
) | |
if "p2c" in self.pos_att_type: | |
self.pos_q_proj = tf.keras.layers.Dense( | |
self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="pos_q_proj" | |
) | |
self.dropout = TFDebertaStableDropout(config.attention_probs_dropout_prob, name="dropout") | |
def build(self, input_shape): | |
self.q_bias = self.add_weight( | |
name="q_bias", shape=(self.all_head_size), initializer=tf.keras.initializers.Zeros() | |
) | |
self.v_bias = self.add_weight( | |
name="v_bias", shape=(self.all_head_size), initializer=tf.keras.initializers.Zeros() | |
) | |
return super().build(input_shape) | |
def transpose_for_scores(self, tensor: tf.Tensor) -> tf.Tensor: | |
shape = shape_list(tensor)[:-1] + [self.num_attention_heads, -1] | |
# Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size] | |
tensor = tf.reshape(tensor=tensor, shape=shape) | |
# Transpose the tensor from [batch_size, seq_length, num_attention_heads, attention_head_size] to [batch_size, num_attention_heads, seq_length, attention_head_size] | |
return tf.transpose(tensor, perm=[0, 2, 1, 3]) | |
def call( | |
self, | |
hidden_states: tf.Tensor, | |
attention_mask: tf.Tensor, | |
query_states: tf.Tensor = None, | |
relative_pos: tf.Tensor = None, | |
rel_embeddings: tf.Tensor = None, | |
output_attentions: bool = False, | |
training: bool = False, | |
) -> Tuple[tf.Tensor]: | |
""" | |
Call the module | |
Args: | |
hidden_states (`tf.Tensor`): | |
Input states to the module usually the output from previous layer, it will be the Q,K and V in | |
*Attention(Q,K,V)* | |
attention_mask (`tf.Tensor`): | |
An attention mask matrix of shape [*B*, *N*, *N*] where *B* is the batch size, *N* is the maximum | |
sequence length in which element [i,j] = *1* means the *i* th token in the input can attend to the *j* | |
th token. | |
return_att (`bool`, optional): | |
Whether return the attention matrix. | |
query_states (`tf.Tensor`, optional): | |
The *Q* state in *Attention(Q,K,V)*. | |
relative_pos (`tf.Tensor`): | |
The relative position encoding between the tokens in the sequence. It's of shape [*B*, *N*, *N*] with | |
values ranging in [*-max_relative_positions*, *max_relative_positions*]. | |
rel_embeddings (`tf.Tensor`): | |
The embedding of relative distances. It's a tensor of shape [\\(2 \\times | |
\\text{max_relative_positions}\\), *hidden_size*]. | |
""" | |
if query_states is None: | |
qp = self.in_proj(hidden_states) # .split(self.all_head_size, dim=-1) | |
query_layer, key_layer, value_layer = tf.split( | |
self.transpose_for_scores(qp), num_or_size_splits=3, axis=-1 | |
) | |
else: | |
def linear(w, b, x): | |
out = tf.matmul(x, w, transpose_b=True) | |
if b is not None: | |
out += tf.transpose(b) | |
return out | |
ws = tf.split( | |
tf.transpose(self.in_proj.weight[0]), num_or_size_splits=self.num_attention_heads * 3, axis=0 | |
) | |
qkvw = tf.TensorArray(dtype=tf.float32, size=3) | |
for k in tf.range(3): | |
qkvw_inside = tf.TensorArray(dtype=tf.float32, size=self.num_attention_heads) | |
for i in tf.range(self.num_attention_heads): | |
qkvw_inside = qkvw_inside.write(i, ws[i * 3 + k]) | |
qkvw = qkvw.write(k, qkvw_inside.concat()) | |
qkvb = [None] * 3 | |
q = linear(qkvw[0], qkvb[0], query_states) | |
k = linear(qkvw[1], qkvb[1], hidden_states) | |
v = linear(qkvw[2], qkvb[2], hidden_states) | |
query_layer = self.transpose_for_scores(q) | |
key_layer = self.transpose_for_scores(k) | |
value_layer = self.transpose_for_scores(v) | |
query_layer = query_layer + self.transpose_for_scores(self.q_bias[None, None, :]) | |
value_layer = value_layer + self.transpose_for_scores(self.v_bias[None, None, :]) | |
rel_att = None | |
# Take the dot product between "query" and "key" to get the raw attention scores. | |
scale_factor = 1 + len(self.pos_att_type) | |
scale = math.sqrt(shape_list(query_layer)[-1] * scale_factor) | |
query_layer = query_layer / scale | |
attention_scores = tf.matmul(query_layer, tf.transpose(key_layer, [0, 1, 3, 2])) | |
if self.relative_attention: | |
rel_embeddings = self.pos_dropout(rel_embeddings, training=training) | |
rel_att = self.disentangled_att_bias(query_layer, key_layer, relative_pos, rel_embeddings, scale_factor) | |
if rel_att is not None: | |
attention_scores = attention_scores + rel_att | |
if self.talking_head: | |
attention_scores = tf.transpose( | |
self.head_logits_proj(tf.transpose(attention_scores, [0, 2, 3, 1])), [0, 3, 1, 2] | |
) | |
attention_probs = self.softmax(attention_scores, attention_mask) | |
attention_probs = self.dropout(attention_probs, training=training) | |
if self.talking_head: | |
attention_probs = tf.transpose( | |
self.head_weights_proj(tf.transpose(attention_probs, [0, 2, 3, 1])), [0, 3, 1, 2] | |
) | |
context_layer = tf.matmul(attention_probs, value_layer) | |
context_layer = tf.transpose(context_layer, [0, 2, 1, 3]) | |
context_layer_shape = shape_list(context_layer) | |
# Set the final dimension here explicitly. | |
# Calling tf.reshape(context_layer, (*context_layer_shape[:-2], -1)) raises an error when executing | |
# the model in graph mode as context_layer is reshaped to (None, 7, None) and Dense layer in TFDebertaV2SelfOutput | |
# requires final input dimension to be defined | |
new_context_layer_shape = context_layer_shape[:-2] + [context_layer_shape[-2] * context_layer_shape[-1]] | |
context_layer = tf.reshape(context_layer, new_context_layer_shape) | |
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) | |
return outputs | |
def disentangled_att_bias(self, query_layer, key_layer, relative_pos, rel_embeddings, scale_factor): | |
if relative_pos is None: | |
q = shape_list(query_layer)[-2] | |
relative_pos = build_relative_position(q, shape_list(key_layer)[-2]) | |
shape_list_pos = shape_list(relative_pos) | |
if len(shape_list_pos) == 2: | |
relative_pos = tf.expand_dims(tf.expand_dims(relative_pos, 0), 0) | |
elif len(shape_list_pos) == 3: | |
relative_pos = tf.expand_dims(relative_pos, 1) | |
# bxhxqxk | |
elif len(shape_list_pos) != 4: | |
raise ValueError(f"Relative position ids must be of dim 2 or 3 or 4. {len(shape_list_pos)}") | |
att_span = tf.cast( | |
tf.minimum( | |
tf.maximum(shape_list(query_layer)[-2], shape_list(key_layer)[-2]), self.max_relative_positions | |
), | |
tf.int64, | |
) | |
rel_embeddings = tf.expand_dims( | |
rel_embeddings[self.max_relative_positions - att_span : self.max_relative_positions + att_span, :], 0 | |
) | |
score = 0 | |
# content->position | |
if "c2p" in self.pos_att_type: | |
pos_key_layer = self.pos_proj(rel_embeddings) | |
pos_key_layer = self.transpose_for_scores(pos_key_layer) | |
c2p_att = tf.matmul(query_layer, tf.transpose(pos_key_layer, [0, 1, 3, 2])) | |
c2p_pos = tf.clip_by_value(relative_pos + att_span, 0, att_span * 2 - 1) | |
c2p_att = torch_gather(c2p_att, c2p_dynamic_expand(c2p_pos, query_layer, relative_pos), -1) | |
score += c2p_att | |
# position->content | |
if "p2c" in self.pos_att_type: | |
pos_query_layer = self.pos_q_proj(rel_embeddings) | |
pos_query_layer = self.transpose_for_scores(pos_query_layer) | |
pos_query_layer /= tf.math.sqrt(tf.cast(shape_list(pos_query_layer)[-1] * scale_factor, dtype=tf.float32)) | |
if shape_list(query_layer)[-2] != shape_list(key_layer)[-2]: | |
r_pos = build_relative_position(shape_list(key_layer)[-2], shape_list(key_layer)[-2]) | |
else: | |
r_pos = relative_pos | |
p2c_pos = tf.clip_by_value(-r_pos + att_span, 0, att_span * 2 - 1) | |
p2c_att = tf.matmul(key_layer, tf.transpose(pos_query_layer, [0, 1, 3, 2])) | |
p2c_att = tf.transpose( | |
torch_gather(p2c_att, p2c_dynamic_expand(p2c_pos, query_layer, key_layer), -1), [0, 1, 3, 2] | |
) | |
if shape_list(query_layer)[-2] != shape_list(key_layer)[-2]: | |
pos_index = tf.expand_dims(relative_pos[:, :, :, 0], -1) | |
p2c_att = torch_gather(p2c_att, pos_dynamic_expand(pos_index, p2c_att, key_layer), -2) | |
score += p2c_att | |
return score | |
class TFDebertaEmbeddings(tf.keras.layers.Layer): | |
"""Construct the embeddings from word, position and token_type embeddings.""" | |
def __init__(self, config, **kwargs): | |
super().__init__(**kwargs) | |
self.config = config | |
self.embedding_size = getattr(config, "embedding_size", config.hidden_size) | |
self.hidden_size = config.hidden_size | |
self.max_position_embeddings = config.max_position_embeddings | |
self.position_biased_input = getattr(config, "position_biased_input", True) | |
self.initializer_range = config.initializer_range | |
if self.embedding_size != config.hidden_size: | |
self.embed_proj = tf.keras.layers.Dense( | |
config.hidden_size, | |
kernel_initializer=get_initializer(config.initializer_range), | |
name="embed_proj", | |
use_bias=False, | |
) | |
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") | |
self.dropout = TFDebertaStableDropout(config.hidden_dropout_prob, name="dropout") | |
def build(self, input_shape: tf.TensorShape): | |
with tf.name_scope("word_embeddings"): | |
self.weight = self.add_weight( | |
name="weight", | |
shape=[self.config.vocab_size, self.embedding_size], | |
initializer=get_initializer(self.initializer_range), | |
) | |
with tf.name_scope("token_type_embeddings"): | |
if self.config.type_vocab_size > 0: | |
self.token_type_embeddings = self.add_weight( | |
name="embeddings", | |
shape=[self.config.type_vocab_size, self.embedding_size], | |
initializer=get_initializer(self.initializer_range), | |
) | |
else: | |
self.token_type_embeddings = None | |
with tf.name_scope("position_embeddings"): | |
if self.position_biased_input: | |
self.position_embeddings = self.add_weight( | |
name="embeddings", | |
shape=[self.max_position_embeddings, self.hidden_size], | |
initializer=get_initializer(self.initializer_range), | |
) | |
else: | |
self.position_embeddings = None | |
super().build(input_shape) | |
def call( | |
self, | |
input_ids: tf.Tensor = None, | |
position_ids: tf.Tensor = None, | |
token_type_ids: tf.Tensor = None, | |
inputs_embeds: tf.Tensor = None, | |
mask: tf.Tensor = None, | |
training: bool = False, | |
) -> tf.Tensor: | |
""" | |
Applies embedding based on inputs tensor. | |
Returns: | |
final_embeddings (`tf.Tensor`): output embedding tensor. | |
""" | |
if input_ids is None and inputs_embeds is None: | |
raise ValueError("Need to provide either `input_ids` or `input_embeds`.") | |
if input_ids is not None: | |
check_embeddings_within_bounds(input_ids, self.config.vocab_size) | |
inputs_embeds = tf.gather(params=self.weight, indices=input_ids) | |
input_shape = shape_list(inputs_embeds)[:-1] | |
if token_type_ids is None: | |
token_type_ids = tf.fill(dims=input_shape, value=0) | |
if position_ids is None: | |
position_ids = tf.expand_dims(tf.range(start=0, limit=input_shape[-1]), axis=0) | |
final_embeddings = inputs_embeds | |
if self.position_biased_input: | |
position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids) | |
final_embeddings += position_embeds | |
if self.config.type_vocab_size > 0: | |
token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=token_type_ids) | |
final_embeddings += token_type_embeds | |
if self.embedding_size != self.hidden_size: | |
final_embeddings = self.embed_proj(final_embeddings) | |
final_embeddings = self.LayerNorm(final_embeddings) | |
if mask is not None: | |
if len(shape_list(mask)) != len(shape_list(final_embeddings)): | |
if len(shape_list(mask)) == 4: | |
mask = tf.squeeze(tf.squeeze(mask, axis=1), axis=1) | |
mask = tf.cast(tf.expand_dims(mask, axis=2), tf.float32) | |
final_embeddings = final_embeddings * mask | |
final_embeddings = self.dropout(final_embeddings, training=training) | |
return final_embeddings | |
class TFDebertaPredictionHeadTransform(tf.keras.layers.Layer): | |
def __init__(self, config: DebertaConfig, **kwargs): | |
super().__init__(**kwargs) | |
self.embedding_size = getattr(config, "embedding_size", config.hidden_size) | |
self.dense = tf.keras.layers.Dense( | |
units=self.embedding_size, | |
kernel_initializer=get_initializer(config.initializer_range), | |
name="dense", | |
) | |
if isinstance(config.hidden_act, str): | |
self.transform_act_fn = get_tf_activation(config.hidden_act) | |
else: | |
self.transform_act_fn = config.hidden_act | |
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") | |
def call(self, hidden_states: tf.Tensor) -> tf.Tensor: | |
hidden_states = self.dense(inputs=hidden_states) | |
hidden_states = self.transform_act_fn(hidden_states) | |
hidden_states = self.LayerNorm(hidden_states) | |
return hidden_states | |
class TFDebertaLMPredictionHead(tf.keras.layers.Layer): | |
def __init__(self, config: DebertaConfig, input_embeddings: tf.keras.layers.Layer, **kwargs): | |
super().__init__(**kwargs) | |
self.config = config | |
self.embedding_size = getattr(config, "embedding_size", config.hidden_size) | |
self.transform = TFDebertaPredictionHeadTransform(config, name="transform") | |
# The output weights are the same as the input embeddings, but there is | |
# an output-only bias for each token. | |
self.input_embeddings = input_embeddings | |
def build(self, input_shape: tf.TensorShape): | |
self.bias = self.add_weight(shape=(self.config.vocab_size,), initializer="zeros", trainable=True, name="bias") | |
super().build(input_shape) | |
def get_output_embeddings(self) -> tf.keras.layers.Layer: | |
return self.input_embeddings | |
def set_output_embeddings(self, value: tf.Variable): | |
self.input_embeddings.weight = value | |
self.input_embeddings.vocab_size = shape_list(value)[0] | |
def get_bias(self) -> Dict[str, tf.Variable]: | |
return {"bias": self.bias} | |
def set_bias(self, value: tf.Variable): | |
self.bias = value["bias"] | |
self.config.vocab_size = shape_list(value["bias"])[0] | |
def call(self, hidden_states: tf.Tensor) -> tf.Tensor: | |
hidden_states = self.transform(hidden_states=hidden_states) | |
seq_length = shape_list(hidden_states)[1] | |
hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.embedding_size]) | |
hidden_states = tf.matmul(a=hidden_states, b=self.input_embeddings.weight, transpose_b=True) | |
hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, seq_length, self.config.vocab_size]) | |
hidden_states = tf.nn.bias_add(value=hidden_states, bias=self.bias) | |
return hidden_states | |
class TFDebertaOnlyMLMHead(tf.keras.layers.Layer): | |
def __init__(self, config: DebertaConfig, input_embeddings: tf.keras.layers.Layer, **kwargs): | |
super().__init__(**kwargs) | |
self.predictions = TFDebertaLMPredictionHead(config, input_embeddings, name="predictions") | |
def call(self, sequence_output: tf.Tensor) -> tf.Tensor: | |
prediction_scores = self.predictions(hidden_states=sequence_output) | |
return prediction_scores | |
# @keras_serializable | |
class TFDebertaMainLayer(tf.keras.layers.Layer): | |
config_class = DebertaConfig | |
def __init__(self, config: DebertaConfig, **kwargs): | |
super().__init__(**kwargs) | |
self.config = config | |
self.embeddings = TFDebertaEmbeddings(config, name="embeddings") | |
self.encoder = TFDebertaEncoder(config, name="encoder") | |
def get_input_embeddings(self) -> tf.keras.layers.Layer: | |
return self.embeddings | |
def set_input_embeddings(self, value: tf.Variable): | |
self.embeddings.weight = value | |
self.embeddings.vocab_size = shape_list(value)[0] | |
def _prune_heads(self, heads_to_prune): | |
""" | |
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base | |
class PreTrainedModel | |
""" | |
raise NotImplementedError | |
def call( | |
self, | |
input_ids: TFModelInputType | None = None, | |
attention_mask: np.ndarray | tf.Tensor | None = None, | |
token_type_ids: np.ndarray | tf.Tensor | None = None, | |
position_ids: np.ndarray | tf.Tensor | None = None, | |
inputs_embeds: np.ndarray | tf.Tensor | None = None, | |
output_attentions: Optional[bool] = None, | |
output_hidden_states: Optional[bool] = None, | |
return_dict: Optional[bool] = None, | |
training: bool = False, | |
) -> Union[TFBaseModelOutput, Tuple[tf.Tensor]]: | |
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 attention_mask is None: | |
attention_mask = tf.fill(dims=input_shape, value=1) | |
if token_type_ids is None: | |
token_type_ids = tf.fill(dims=input_shape, value=0) | |
embedding_output = self.embeddings( | |
input_ids=input_ids, | |
position_ids=position_ids, | |
token_type_ids=token_type_ids, | |
inputs_embeds=inputs_embeds, | |
mask=attention_mask, | |
training=training, | |
) | |
encoder_outputs = self.encoder( | |
hidden_states=embedding_output, | |
attention_mask=attention_mask, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
training=training, | |
) | |
sequence_output = encoder_outputs[0] | |
if not return_dict: | |
return (sequence_output,) + encoder_outputs[1:] | |
return TFBaseModelOutput( | |
last_hidden_state=sequence_output, | |
hidden_states=encoder_outputs.hidden_states, | |
attentions=encoder_outputs.attentions, | |
) | |
class TFDebertaPreTrainedModel(TFPreTrainedModel): | |
""" | |
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained | |
models. | |
""" | |
config_class = DebertaConfig | |
base_model_prefix = "deberta" | |
DEBERTA_START_DOCSTRING = r""" | |
The DeBERTa model was proposed in [DeBERTa: Decoding-enhanced BERT with Disentangled | |
Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen. It's build | |
on top of BERT/RoBERTa with two improvements, i.e. disentangled attention and enhanced mask decoder. With those two | |
improvements, it out perform BERT/RoBERTa on a majority of tasks with 80GB pretraining data. | |
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> | |
Parameters: | |
config ([`DebertaConfig`]): 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 [`~PreTrainedModel.from_pretrained`] method to load the model weights. | |
""" | |
DEBERTA_INPUTS_DOCSTRING = r""" | |
Args: | |
input_ids (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` ``Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the 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 (`np.ndarray` or `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) | |
token_type_ids (`np.ndarray` or `tf.Tensor` of shape `({0})`, *optional*): | |
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, | |
1]`: | |
- 0 corresponds to a *sentence A* token, | |
- 1 corresponds to a *sentence B* token. | |
[What are token type IDs?](../glossary#token-type-ids) | |
position_ids (`np.ndarray` or `tf.Tensor` of shape `({0})`, *optional*): | |
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, | |
config.max_position_embeddings - 1]`. | |
[What are position IDs?](../glossary#position-ids) | |
inputs_embeds (`np.ndarray` or `tf.Tensor` of shape `({0}, 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. | |
""" | |
class TFDebertaModel(TFDebertaPreTrainedModel): | |
def __init__(self, config: DebertaConfig, *inputs, **kwargs): | |
super().__init__(config, *inputs, **kwargs) | |
self.deberta = TFDebertaMainLayer(config, name="deberta") | |
def call( | |
self, | |
input_ids: TFModelInputType | None = None, | |
attention_mask: np.ndarray | tf.Tensor | None = None, | |
token_type_ids: np.ndarray | tf.Tensor | None = None, | |
position_ids: np.ndarray | tf.Tensor | None = None, | |
inputs_embeds: 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]]: | |
outputs = self.deberta( | |
input_ids=input_ids, | |
attention_mask=attention_mask, | |
token_type_ids=token_type_ids, | |
position_ids=position_ids, | |
inputs_embeds=inputs_embeds, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
training=training, | |
) | |
return outputs | |
class TFDebertaForMaskedLM(TFDebertaPreTrainedModel, TFMaskedLanguageModelingLoss): | |
def __init__(self, config: DebertaConfig, *inputs, **kwargs): | |
super().__init__(config, *inputs, **kwargs) | |
if config.is_decoder: | |
logger.warning( | |
"If you want to use `TFDebertaForMaskedLM` make sure `config.is_decoder=False` for " | |
"bi-directional self-attention." | |
) | |
self.deberta = TFDebertaMainLayer(config, name="deberta") | |
self.mlm = TFDebertaOnlyMLMHead(config, input_embeddings=self.deberta.embeddings, name="cls") | |
def get_lm_head(self) -> tf.keras.layers.Layer: | |
return self.mlm.predictions | |
def call( | |
self, | |
input_ids: TFModelInputType | None = None, | |
attention_mask: np.ndarray | tf.Tensor | None = None, | |
token_type_ids: np.ndarray | tf.Tensor | None = None, | |
position_ids: np.ndarray | tf.Tensor | None = None, | |
inputs_embeds: np.ndarray | tf.Tensor | None = None, | |
output_attentions: Optional[bool] = None, | |
output_hidden_states: Optional[bool] = None, | |
return_dict: Optional[bool] = None, | |
labels: np.ndarray | tf.Tensor | None = None, | |
training: Optional[bool] = False, | |
) -> Union[TFMaskedLMOutput, Tuple[tf.Tensor]]: | |
r""" | |
labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size, sequence_length)`, *optional*): | |
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., | |
config.vocab_size]` (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]` | |
""" | |
outputs = self.deberta( | |
input_ids=input_ids, | |
attention_mask=attention_mask, | |
token_type_ids=token_type_ids, | |
position_ids=position_ids, | |
inputs_embeds=inputs_embeds, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
training=training, | |
) | |
sequence_output = outputs[0] | |
prediction_scores = self.mlm(sequence_output=sequence_output, training=training) | |
loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=prediction_scores) | |
if not return_dict: | |
output = (prediction_scores,) + outputs[2:] | |
return ((loss,) + output) if loss is not None else output | |
return TFMaskedLMOutput( | |
loss=loss, | |
logits=prediction_scores, | |
hidden_states=outputs.hidden_states, | |
attentions=outputs.attentions, | |
) | |
class TFDebertaForSequenceClassification(TFDebertaPreTrainedModel, TFSequenceClassificationLoss): | |
def __init__(self, config: DebertaConfig, *inputs, **kwargs): | |
super().__init__(config, *inputs, **kwargs) | |
self.num_labels = config.num_labels | |
self.deberta = TFDebertaMainLayer(config, name="deberta") | |
self.pooler = TFDebertaContextPooler(config, name="pooler") | |
drop_out = getattr(config, "cls_dropout", None) | |
drop_out = self.config.hidden_dropout_prob if drop_out is None else drop_out | |
self.dropout = TFDebertaStableDropout(drop_out, name="cls_dropout") | |
self.classifier = tf.keras.layers.Dense( | |
units=config.num_labels, | |
kernel_initializer=get_initializer(config.initializer_range), | |
name="classifier", | |
) | |
def call( | |
self, | |
input_ids: TFModelInputType | None = None, | |
attention_mask: np.ndarray | tf.Tensor | None = None, | |
token_type_ids: np.ndarray | tf.Tensor | None = None, | |
position_ids: np.ndarray | tf.Tensor | None = None, | |
inputs_embeds: np.ndarray | tf.Tensor | None = None, | |
output_attentions: Optional[bool] = None, | |
output_hidden_states: Optional[bool] = None, | |
return_dict: Optional[bool] = None, | |
labels: np.ndarray | tf.Tensor | None = None, | |
training: Optional[bool] = False, | |
) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]: | |
r""" | |
labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size,)`, *optional*): | |
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., | |
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If | |
`config.num_labels > 1` a classification loss is computed (Cross-Entropy). | |
""" | |
outputs = self.deberta( | |
input_ids=input_ids, | |
attention_mask=attention_mask, | |
token_type_ids=token_type_ids, | |
position_ids=position_ids, | |
inputs_embeds=inputs_embeds, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
training=training, | |
) | |
sequence_output = outputs[0] | |
pooled_output = self.pooler(sequence_output, training=training) | |
pooled_output = self.dropout(pooled_output, training=training) | |
logits = self.classifier(pooled_output) | |
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 TFSequenceClassifierOutput( | |
loss=loss, | |
logits=logits, | |
hidden_states=outputs.hidden_states, | |
attentions=outputs.attentions, | |
) | |
class TFDebertaForTokenClassification(TFDebertaPreTrainedModel, TFTokenClassificationLoss): | |
def __init__(self, config: DebertaConfig, *inputs, **kwargs): | |
super().__init__(config, *inputs, **kwargs) | |
self.num_labels = config.num_labels | |
self.deberta = TFDebertaMainLayer(config, name="deberta") | |
self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob) | |
self.classifier = tf.keras.layers.Dense( | |
units=config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier" | |
) | |
def call( | |
self, | |
input_ids: TFModelInputType | None = None, | |
attention_mask: np.ndarray | tf.Tensor | None = None, | |
token_type_ids: np.ndarray | tf.Tensor | None = None, | |
position_ids: np.ndarray | tf.Tensor | None = None, | |
inputs_embeds: np.ndarray | tf.Tensor | None = None, | |
output_attentions: Optional[bool] = None, | |
output_hidden_states: Optional[bool] = None, | |
return_dict: Optional[bool] = None, | |
labels: np.ndarray | tf.Tensor | None = None, | |
training: Optional[bool] = False, | |
) -> Union[TFTokenClassifierOutput, Tuple[tf.Tensor]]: | |
r""" | |
labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size, sequence_length)`, *optional*): | |
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. | |
""" | |
outputs = self.deberta( | |
input_ids=input_ids, | |
attention_mask=attention_mask, | |
token_type_ids=token_type_ids, | |
position_ids=position_ids, | |
inputs_embeds=inputs_embeds, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
training=training, | |
) | |
sequence_output = outputs[0] | |
sequence_output = self.dropout(sequence_output, training=training) | |
logits = self.classifier(inputs=sequence_output) | |
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 TFTokenClassifierOutput( | |
loss=loss, | |
logits=logits, | |
hidden_states=outputs.hidden_states, | |
attentions=outputs.attentions, | |
) | |
class TFDebertaForQuestionAnswering(TFDebertaPreTrainedModel, TFQuestionAnsweringLoss): | |
def __init__(self, config: DebertaConfig, *inputs, **kwargs): | |
super().__init__(config, *inputs, **kwargs) | |
self.num_labels = config.num_labels | |
self.deberta = TFDebertaMainLayer(config, name="deberta") | |
self.qa_outputs = tf.keras.layers.Dense( | |
units=config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs" | |
) | |
def call( | |
self, | |
input_ids: TFModelInputType | None = None, | |
attention_mask: np.ndarray | tf.Tensor | None = None, | |
token_type_ids: np.ndarray | tf.Tensor | None = None, | |
position_ids: np.ndarray | tf.Tensor | None = None, | |
inputs_embeds: np.ndarray | tf.Tensor | None = None, | |
output_attentions: Optional[bool] = None, | |
output_hidden_states: Optional[bool] = None, | |
return_dict: Optional[bool] = None, | |
start_positions: np.ndarray | tf.Tensor | None = None, | |
end_positions: np.ndarray | tf.Tensor | None = None, | |
training: Optional[bool] = False, | |
) -> Union[TFQuestionAnsweringModelOutput, Tuple[tf.Tensor]]: | |
r""" | |
start_positions (`tf.Tensor` or `np.ndarray` of shape `(batch_size,)`, *optional*): | |
Labels for position (index) of the start of the labelled span for computing the token classification loss. | |
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. | |
end_positions (`tf.Tensor` or `np.ndarray` of shape `(batch_size,)`, *optional*): | |
Labels for position (index) of the end of the labelled span for computing the token classification loss. | |
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. | |
""" | |
outputs = self.deberta( | |
input_ids=input_ids, | |
attention_mask=attention_mask, | |
token_type_ids=token_type_ids, | |
position_ids=position_ids, | |
inputs_embeds=inputs_embeds, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
training=training, | |
) | |
sequence_output = outputs[0] | |
logits = self.qa_outputs(inputs=sequence_output) | |
start_logits, end_logits = tf.split(value=logits, num_or_size_splits=2, axis=-1) | |
start_logits = tf.squeeze(input=start_logits, axis=-1) | |
end_logits = tf.squeeze(input=end_logits, axis=-1) | |
loss = None | |
if start_positions is not None and end_positions is not None: | |
labels = {"start_position": start_positions} | |
labels["end_position"] = end_positions | |
loss = self.hf_compute_loss(labels=labels, logits=(start_logits, end_logits)) | |
if not return_dict: | |
output = (start_logits, end_logits) + outputs[2:] | |
return ((loss,) + output) if loss is not None else output | |
return TFQuestionAnsweringModelOutput( | |
loss=loss, | |
start_logits=start_logits, | |
end_logits=end_logits, | |
hidden_states=outputs.hidden_states, | |
attentions=outputs.attentions, | |
) | |