# Copyright (c) 2023 PaddlePaddle Authors. 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.
from collections.abc import Sequence
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddlenlp.transformers.model_utils import PretrainedModel, register_base_model
from ...utils.converter import StateDictNameMapping
from ...utils.env import CONFIG_NAME
from ..activations import ACT2FN
from ..model_outputs import (
BaseModelOutput,
MaskedLMOutput,
MultipleChoiceModelOutput,
QuestionAnsweringModelOutput,
SequenceClassifierOutput,
TokenClassifierOutput,
)
from .configuration import (
DEBERTA_PRETRAINED_INIT_CONFIGURATION,
DEBERTA_PRETRAINED_RESOURCE_FILES_MAP,
DebertaConfig,
)
__all__ = [
"DebertaModel",
"DebertaForSequenceClassification",
"DebertaForQuestionAnswering",
"DebertaForTokenClassification",
"DebertaPreTrainedModel",
"DebertaForMultipleChoice",
]
class DropoutContext(object):
def __init__(self):
self.dropout = 0
self.mask = None
self.scale = 1
self.reuse_mask = True
def get_mask(input, local_context):
if not isinstance(local_context, DropoutContext):
dropout = local_context
mask = None
else:
dropout = local_context.dropout
dropout *= local_context.scale
mask = local_context.mask if local_context.reuse_mask else None
if dropout > 0 and mask is None:
# mask = (1 - torch.empty_like(input).bernoulli_(1 - dropout)).to(torch.bool)
probability_matrix = paddle.full(paddle.empty_like(input).shape, 1 - dropout)
mask = (1 - paddle.bernoulli(probability_matrix)).cast("bool")
if isinstance(local_context, DropoutContext):
if local_context.mask is None:
local_context.mask = mask
return mask, dropout
class XDropout(paddle.autograd.PyLayer):
"""Optimized dropout function to save computation and memory by using mask operation instead of multiplication."""
@staticmethod
def forward(ctx, input, local_ctx):
mask, dropout = get_mask(input, local_ctx)
ctx.scale = 1.0 / (1 - dropout)
if dropout > 0:
ctx.save_for_backward(mask)
return input.masked_fill(mask, 0) * ctx.scale
else:
return input
@staticmethod
def backward(ctx, grad_output):
if ctx.scale > 1:
(mask,) = ctx.saved_tensor()
return grad_output.masked_fill(mask, 0) * ctx.scale, None
else:
return grad_output, None
class StableDropout(nn.Layer):
"""
Optimized dropout module for stabilizing the training
Args:
drop_prob (float): the dropout probabilities
"""
def __init__(self, drop_prob):
super().__init__()
self.drop_prob = drop_prob
self.count = 0
self.context_stack = None
def forward(self, x):
"""
Call the module
Args:
x (`paddle.Tensor`): The input tensor to apply dropout
"""
if self.training and self.drop_prob > 0:
return XDropout.apply(x, self.get_context())
return x
def clear_context(self):
self.count = 0
self.context_stack = None
def init_context(self, reuse_mask=True, scale=1):
if self.context_stack is None:
self.context_stack = []
self.count = 0
for c in self.context_stack:
c.reuse_mask = reuse_mask
c.scale = scale
def get_context(self):
if self.context_stack is not None:
if self.count >= len(self.context_stack):
self.context_stack.append(DropoutContext())
ctx = self.context_stack[self.count]
ctx.dropout = self.drop_prob
self.count += 1
return ctx
else:
return self.drop_prob
def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length):
"""
Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
are ignored. This is modified from fairseq's `utils.make_positions`.
Args:
x: paddle.Tensor x:
Returns: paddle.Tensor
"""
if past_key_values_length is None:
past_key_values_length = 0
# The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
mask = (input_ids != padding_idx).cast("int64")
incremental_indices = (paddle.cumsum(mask, axis=1) + past_key_values_length) * mask
return incremental_indices + padding_idx
def softmax_with_mask(x, mask, axis):
rmask = paddle.logical_not(mask.astype("bool"))
y = paddle.full(x.shape, -float("inf"), x.dtype)
return F.softmax(paddle.where(rmask, y, x), axis=axis)
class DebertaEmbeddings(nn.Layer):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config):
super().__init__()
pad_token_id = getattr(config, "pad_token_id", 0)
self.position_biased_input = getattr(config, "position_biased_input", True)
self.embedding_size = getattr(config, "embedding_size", config.hidden_size)
if not self.position_biased_input:
self.position_embeddings = None
else:
self.position_embeddings = nn.Embedding(config.max_position_embeddings, self.embedding_size)
self.word_embeddings = nn.Embedding(config.vocab_size, self.embedding_size, padding_idx=pad_token_id)
if config.type_vocab_size > 0:
self.token_type_embeddings = nn.Embedding(config.type_vocab_size, self.embedding_size)
if self.embedding_size != config.hidden_size:
self.embed_proj = nn.Linear(self.embedding_size, config.hidden_size, bias_attr=False)
self.LayerNorm = DebertaLayerNorm(config.hidden_size, config.layer_norm_eps)
self.dropout = StableDropout(config.hidden_dropout_prob)
self.config = config
def forward(self, input_ids=None, token_type_ids=None, position_ids=None, mask=None, inputs_embeds=None):
if input_ids is not None:
input_shape = input_ids.shape
else:
input_shape = inputs_embeds.shape[:-1]
seq_length = input_shape[1]
if position_ids is None:
position_ids = paddle.arange(seq_length, dtype="int64")
position_ids = position_ids.unsqueeze(0).expand(input_shape)
if token_type_ids is None:
token_type_ids = paddle.zeros(input_shape, dtype="int64")
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
if self.position_embeddings is not None:
position_embeds = self.position_embeddings(position_ids)
else:
position_embeds = paddle.zeros_like(inputs_embeds)
embeddings = inputs_embeds
if self.position_biased_input:
embeddings = embeddings + position_embeds
if self.config.type_vocab_size > 0:
token_type_embeds = self.token_type_embeddings(token_type_ids)
embeddings = embeddings + token_type_embeds
if self.config.embedding_size != self.config.hidden_size:
embeddings = self.embed_proj(embeddings)
embeddings = self.LayerNorm(embeddings)
if mask is not None:
if mask.dim() != embeddings.dim():
if mask.dim() == 4:
mask = mask.squeeze(1).squeeze(1)
mask = mask.unsqueeze(2)
embeddings = embeddings * mask.astype(embeddings.dtype)
embeddings = self.dropout(embeddings)
return embeddings
class DebertaLayerNorm(nn.Layer):
"""LayerNorm module in the TF style (epsilon inside the square root)."""
def __init__(self, size, eps=1e-12):
super().__init__()
self.weight = paddle.create_parameter(
shape=[size], default_initializer=nn.initializer.Constant(1.0), dtype="float32"
)
self.add_parameter("weight", self.weight)
self.bias = paddle.create_parameter(
shape=[size], default_initializer=nn.initializer.Constant(0.0), dtype="float32"
)
self.add_parameter("bias", self.bias)
self.variance_epsilon = eps
def forward(self, hidden_states):
mean = hidden_states.mean(-1, keepdim=True)
variance = (hidden_states - mean).pow(2).mean(-1, keepdim=True)
hidden_states = (hidden_states - mean) / paddle.sqrt(variance + self.variance_epsilon)
y = self.weight * hidden_states + self.bias
return y
class DebertaSelfOutput(nn.Layer):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = DebertaLayerNorm(config.hidden_size, config.layer_norm_eps)
self.dropout = StableDropout(config.hidden_dropout_prob)
def forward(self, hidden_states, input_tensor):
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
def build_relative_position(query_size, key_size):
q_ids = paddle.arange(query_size, dtype="int64")
k_ids = paddle.arange(key_size, dtype="int64")
rel_pos_ids = q_ids[:, None] - paddle.tile(k_ids[None], [query_size, 1])
rel_pos_ids = rel_pos_ids.unsqueeze(0)
return rel_pos_ids
def c2p_dynamic_expand(c2p_pos, query_layer, relative_pos):
return paddle.expand(
c2p_pos, [query_layer.shape[0], query_layer.shape[1], query_layer.shape[2], relative_pos.shape[-1]]
)
def p2c_dynamic_expand(c2p_pos, query_layer, key_layer):
return paddle.expand(
c2p_pos, [query_layer.shape[0], query_layer.shape[1], key_layer.shape[-2], key_layer.shape[-2]]
)
def pos_dynamic_expand(pos_index, p2c_att, key_layer):
return paddle.expand(pos_index, p2c_att.shape[:2] + (pos_index.shape[-2], key_layer.shape[-2]))
class DisentangledSelfAttention(nn.Layer):
def __init__(self, config):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
"The hidden size (%d) is not a multiple of the number of attention "
"heads (%d)" % (config.hidden_size, 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 = nn.Linear(config.hidden_size, self.all_head_size * 3, bias_attr=False)
self.q_bias = paddle.create_parameter(
shape=[self.all_head_size], default_initializer=nn.initializer.Constant(0.0), dtype="float32"
)
self.v_bias = paddle.create_parameter(
shape=[self.all_head_size], default_initializer=nn.initializer.Constant(0.0), dtype="float32"
)
self.pos_att_type = config.pos_att_type if config.pos_att_type is not None else []
# To transform c2p|p2c" into ["c2p","p2c"]
if isinstance(self.pos_att_type, str):
self.pos_att_type = self.pos_att_type.split("|")
self.relative_attention = getattr(config, "relative_attention", True)
self.talking_head = getattr(config, "talking_head", False)
if self.talking_head:
self.head_logits_proj = nn.Linear(config.num_attention_heads, config.num_attention_heads, bias_attr=False)
self.head_weights_proj = nn.Linear(config.num_attention_heads, config.num_attention_heads, bias_attr=False)
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 = nn.Dropout(config.hidden_dropout_prob)
if "c2p" in self.pos_att_type:
self.pos_proj = nn.Linear(config.hidden_size, self.all_head_size, bias_attr=False)
if "p2c" in self.pos_att_type:
self.pos_q_proj = nn.Linear(config.hidden_size, self.all_head_size)
self.dropout = StableDropout(config.attention_probs_dropout_prob)
def transpose_for_scores(self, x):
new_x_shape = x.shape[:-1] + [self.num_attention_heads, -1]
x = paddle.reshape(x, new_x_shape)
return paddle.transpose(x, perm=[0, 2, 1, 3])
def forward(
self,
hidden_states,
attention_mask,
output_attentions=False,
query_states=None,
relative_pos=None,
rel_embeddings=None,
):
if query_states is None:
query_states = self.in_proj(hidden_states)
query_states = self.transpose_for_scores(query_states)
query_layer, key_layer, value_layer = paddle.chunk(query_states, 3, axis=-1)
else:
def linear(w, b, x):
if b is not None:
return paddle.matmul(x, w, transpose_y=True) + b
else:
return paddle.matmul(x, w, transpose_y=True)
ws = paddle.chunk(self.in_proj.weight, self.num_attention_heads * 3, axis=0)
qkvw = [paddle.concat([ws[i * 3 + k] for i in range(self.num_attention_heads)], axis=0) for k in range(3)]
qkvb = [None] * 3
q = linear(qkvw[0], qkvb[0], query_states.astype(qkvw[0].dtype))
k, v = [linear(qkvw[i], qkvb[i], hidden_states.astype(qkvw[i].dtype)) for i in range(1, 3)]
query_layer, key_layer, value_layer = [self.transpose_for_scores(x) for x in [q, k, 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 = paddle.sqrt(paddle.to_tensor(query_layer.shape[-1], dtype="float32") * scale_factor)
query_layer = query_layer / scale
attention_scores = paddle.matmul(query_layer, key_layer.transpose([0, 1, 3, 2]))
if self.relative_attention:
rel_embeddings = self.pos_dropout(rel_embeddings)
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
# bxhxlxd
if self.talking_head:
attention_scores = self.head_logits_proj(paddle.transpose(attention_scores, [0, 2, 3, 1]))
attention_scores = paddle.transpose(attention_scores, [0, 3, 1, 2])
attention_probs = softmax_with_mask(attention_scores, attention_mask, -1)
attention_probs = self.dropout(attention_probs)
if self.talking_head:
attention_probs = self.head_weights_proj(paddle.transpose(attention_probs, [0, 2, 3, 1]))
attention_probs = paddle.transpose(attention_probs, [0, 3, 1, 2])
context_layer = paddle.matmul(attention_probs, value_layer)
context_layer = paddle.transpose(context_layer, [0, 2, 1, 3])
context_layer = paddle.reshape(context_layer, context_layer.shape[:-2] + [-1])
if output_attentions:
return (context_layer, attention_probs)
else:
return context_layer
def disentangled_att_bias(self, query_layer, key_layer, relative_pos, rel_embeddings, scale_factor):
if relative_pos is None:
q = query_layer.shape[-2]
relative_pos = build_relative_position(q, key_layer.shape[-2])
if relative_pos.ndim == 2:
relative_pos = relative_pos.unsqueeze(0).unsqueeze(0)
elif relative_pos.ndim == 3:
relative_pos = relative_pos.unsqueeze(1)
# bxhxqxk
elif relative_pos.ndim != 4:
raise ValueError(f"Relative position ids must be of dim 2 or 3 or 4. {relative_pos.ndim}")
att_span = min(max(query_layer.shape[-2], key_layer.shape[-2]), self.max_relative_positions)
relative_pos = relative_pos.astype("int64")
rel_embeddings = rel_embeddings[
self.max_relative_positions - att_span : self.max_relative_positions + att_span, :
]
rel_embeddings = paddle.unsqueeze(rel_embeddings, axis=0)
score = 0
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 = paddle.matmul(query_layer, pos_key_layer.transpose([0, 1, 3, 2]))
c2p_pos = paddle.clip(relative_pos + att_span, 0, att_span * 2 - 1)
c2p_att = paddle.take_along_axis(
c2p_att, axis=-1, indices=c2p_dynamic_expand(c2p_pos, query_layer, relative_pos)
)
score += c2p_att
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 /= paddle.sqrt(paddle.to_tensor(pos_query_layer.shape[-1], dtype="float32") * scale_factor)
if query_layer.shape[-2] != key_layer.shape[-2]:
r_pos = build_relative_position(key_layer.shape[-2], key_layer.shape[-2])
else:
r_pos = relative_pos
p2c_pos = paddle.clip(-r_pos + att_span, 0, att_span * 2 - 1)
p2c_att = paddle.matmul(key_layer, pos_query_layer.transpose([0, 1, 3, 2]).astype(key_layer.dtype))
p2c_att = paddle.take_along_axis(
p2c_att, axis=-1, indices=p2c_dynamic_expand(p2c_pos, query_layer, key_layer)
).transpose([0, 1, 3, 2])
if query_layer.shape[-2] != key_layer.shape[-2]:
pos_index = relative_pos[:, :, :, 0].unsqueeze(-1)
p2c_att = paddle.gather(p2c_att, axis=-2, index=pos_dynamic_expand(pos_index, p2c_att, key_layer))
score += p2c_att
return score
class DebertaAttention(nn.Layer):
def __init__(self, config):
super().__init__()
self.self = DisentangledSelfAttention(config)
self.output = DebertaSelfOutput(config)
self.config = config
def forward(
self,
hidden_states,
attention_mask,
output_attentions=False,
query_states=None,
relative_pos=None,
rel_embeddings=None,
):
self_output = self.self(
hidden_states,
attention_mask,
output_attentions,
query_states=query_states,
relative_pos=relative_pos,
rel_embeddings=rel_embeddings,
)
if output_attentions:
self_output, att_matrix = self_output
if query_states is None:
query_states = hidden_states
attention_output = self.output(self_output, query_states)
if output_attentions:
return (attention_output, att_matrix)
else:
return attention_output
class DebertaIntermediate(nn.Layer):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
self.intermediate_act_fn = ACT2FN[config.hidden_act]
def forward(self, hidden_states):
hidden_states = self.dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
class DebertaOutput(nn.Layer):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = DebertaLayerNorm(config.hidden_size, config.layer_norm_eps)
self.dropout = StableDropout(config.hidden_dropout_prob)
def forward(self, hidden_states, input_tensor):
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
class DebertaLayer(nn.Layer):
def __init__(self, config):
super().__init__()
self.attention = DebertaAttention(config)
self.intermediate = DebertaIntermediate(config)
self.output = DebertaOutput(config)
def forward(
self,
hidden_states,
attention_mask,
query_states=None,
relative_pos=None,
rel_embeddings=None,
output_attentions=False,
):
attention_output = self.attention(
hidden_states,
attention_mask,
output_attentions=output_attentions,
query_states=query_states,
relative_pos=relative_pos,
rel_embeddings=rel_embeddings,
)
if output_attentions:
attention_output, att_matrix = attention_output
intermediate_output = self.intermediate(attention_output)
layer_output = self.output(intermediate_output, attention_output)
if output_attentions:
return (layer_output, att_matrix)
else:
return layer_output
class DebertaEncoder(paddle.nn.Layer):
"""Modified BertEncoder with relative position bias support"""
def __init__(self, config):
super().__init__()
self.layer = paddle.nn.LayerList([DebertaLayer(config) for _ in range(config.num_hidden_layers)])
self.relative_attention = getattr(config, "relative_attention", False)
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.rel_embeddings = nn.Embedding(self.max_relative_positions * 2, config.hidden_size)
self.gradient_checkpointing = False
def get_rel_embedding(self):
rel_embeddings = self.rel_embeddings.weight if self.relative_attention else None
return rel_embeddings
def get_attention_mask(self, attention_mask):
if attention_mask.dim() <= 2:
extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
attention_mask = extended_attention_mask * extended_attention_mask.squeeze(-2).unsqueeze(-1)
attention_mask = attention_mask.astype("float32")
elif attention_mask.dim() == 3:
attention_mask = attention_mask.unsqueeze(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 = query_states.shape[-2] if query_states is not None else hidden_states.shape[-2]
relative_pos = build_relative_position(q, hidden_states.shape[-2])
return relative_pos
def forward(
self,
hidden_states,
attention_mask,
output_hidden_states=True,
output_attentions=False,
query_states=None,
relative_pos=None,
return_dict=None,
):
attention_mask = self.get_attention_mask(attention_mask)
relative_pos = self.get_rel_pos(hidden_states, query_states, relative_pos)
all_hidden_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
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,)
if self.gradient_checkpointing and self.training:
def create_custom_forward(module):
def custom_forward(*inputs):
return module(*inputs, output_attentions)
return custom_forward
hidden_states = paddle.utils.checkpoint.checkpoint(
create_custom_forward(layer_module),
next_kv,
attention_mask,
query_states,
relative_pos,
rel_embeddings,
)
else:
hidden_states = layer_module(
next_kv,
attention_mask,
query_states=query_states,
relative_pos=relative_pos,
rel_embeddings=rel_embeddings,
output_attentions=output_attentions,
)
if output_attentions:
hidden_states, att_m = hidden_states
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 + (att_m,)
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 BaseModelOutput(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_attentions,
)
[docs]class DebertaPreTrainedModel(PretrainedModel):
"""
An abstract class for pretrained BERT models. It provides BERT related
`model_config_file`, `resource_files_names`, `pretrained_resource_files_map`,
`pretrained_init_configuration`, `base_model_prefix` for downloading and
loading pretrained models.
See :class:`~paddlenlp.transformers.model_utils.PretrainedModel` for more details.
"""
model_config_file = CONFIG_NAME
config_class = DebertaConfig
resource_files_names = {"model_state": "model_state.pdparams"}
base_model_prefix = "deberta"
pretrained_init_configuration = DEBERTA_PRETRAINED_INIT_CONFIGURATION
pretrained_resource_files_map = DEBERTA_PRETRAINED_RESOURCE_FILES_MAP
@classmethod
def _get_name_mappings(cls, config):
mappings = []
model_mappings = [
["embeddings.word_embeddings.weight", "embeddings.word_embeddings.weight"],
["embeddings.LayerNorm.weight", "embeddings.LayerNorm.weight"],
["embeddings.LayerNorm.bias", "embeddings.LayerNorm.bias"],
["embeddings.position_embeddings.weight", "embeddings.position_embeddings.weight"],
["embeddings.token_type_embeddings.weight", "embeddings.token_type_embeddings.weight"],
["encoder.rel_embeddings.weight", "encoder.rel_embeddings.weight"],
]
for layer_index in range(config.num_hidden_layers):
layer_mappings = [
[
f"encoder.layer.{layer_index}.attention.self.q_bias",
f"encoder.layer.{layer_index}.attention.self.q_bias",
],
[
f"encoder.layer.{layer_index}.attention.self.v_bias",
f"encoder.layer.{layer_index}.attention.self.v_bias",
],
[
f"encoder.layer.{layer_index}.attention.self.in_proj.weight",
f"encoder.layer.{layer_index}.attention.self.in_proj.weight",
"transpose",
],
[
f"encoder.layer.{layer_index}.attention.self.pos_proj.weight",
f"encoder.layer.{layer_index}.attention.self.pos_proj.weight",
"transpose",
],
[
f"encoder.layer.{layer_index}.attention.self.pos_q_proj.weight",
f"encoder.layer.{layer_index}.attention.self.pos_q_proj.weight",
"transpose",
],
[
f"encoder.layer.{layer_index}.attention.self.pos_q_proj.bias",
f"encoder.layer.{layer_index}.attention.self.pos_q_proj.bias",
],
[
f"encoder.layer.{layer_index}.attention.output.dense.weight",
f"encoder.layer.{layer_index}.attention.output.dense.weight",
"transpose",
],
[
f"encoder.layer.{layer_index}.attention.output.dense.bias",
f"encoder.layer.{layer_index}.attention.output.dense.bias",
],
[
f"encoder.layer.{layer_index}.attention.output.LayerNorm.weight",
f"encoder.layer.{layer_index}.attention.output.LayerNorm.weight",
],
[
f"encoder.layer.{layer_index}.attention.output.LayerNorm.bias",
f"encoder.layer.{layer_index}.attention.output.LayerNorm.bias",
],
[
f"encoder.layer.{layer_index}.intermediate.dense.weight",
f"encoder.layer.{layer_index}.intermediate.dense.weight",
"transpose",
],
[
f"encoder.layer.{layer_index}.intermediate.dense.bias",
f"encoder.layer.{layer_index}.intermediate.dense.bias",
],
[
f"encoder.layer.{layer_index}.output.dense.weight",
f"encoder.layer.{layer_index}.output.dense.weight",
"transpose",
],
[f"encoder.layer.{layer_index}.output.dense.bias", f"encoder.layer.{layer_index}.output.dense.bias"],
[
f"encoder.layer.{layer_index}.output.LayerNorm.weight",
f"encoder.layer.{layer_index}.output.LayerNorm.weight",
],
[
f"encoder.layer.{layer_index}.output.LayerNorm.bias",
f"encoder.layer.{layer_index}.output.LayerNorm.bias",
],
]
model_mappings.extend(layer_mappings)
# adapt for hf-internal-testing/tiny-random-DebertaModel
if config.architectures is not None and "DebertaModel" in config.architectures:
pass
else:
for mapping in model_mappings:
mapping[0] = "deberta." + mapping[0]
mapping[1] = "deberta." + mapping[1]
mappings = [StateDictNameMapping(*mapping, index=index) for index, mapping in enumerate(model_mappings)]
return mappings
[docs] def init_weights(self, layer):
"""Initialization hook"""
if isinstance(layer, (nn.Linear, nn.Embedding)):
# In the dygraph mode, use the `set_value` to reset the parameter directly,
# and reset the `state_dict` to update parameter in static mode.
if isinstance(layer.weight, paddle.Tensor):
layer.weight.set_value(
paddle.tensor.normal(
mean=0.0,
std=self.config.initializer_range,
shape=layer.weight.shape,
)
)
elif isinstance(layer, nn.LayerNorm):
layer._epsilon = self.config.layer_norm_eps
[docs]@register_base_model
class DebertaModel(DebertaPreTrainedModel):
def __init__(self, config: DebertaConfig):
super(DebertaModel, self).__init__(config)
self.config = config
self.embeddings = DebertaEmbeddings(config)
self.encoder = DebertaEncoder(config)
self.z_steps = getattr(config, "z_steps", 0)
[docs] def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
inputs_embeds=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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 = input_ids.shape
elif inputs_embeds is not None:
input_shape = inputs_embeds.shape[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if attention_mask is None:
attention_mask = paddle.ones(input_shape, dtype="int64")
if token_type_ids is None:
token_type_ids = paddle.zeros(input_shape, dtype="int64")
embedding_output = self.embeddings(
input_ids=input_ids,
token_type_ids=token_type_ids,
position_ids=position_ids,
mask=attention_mask,
inputs_embeds=inputs_embeds,
)
encoder_outputs = self.encoder(
embedding_output,
attention_mask,
output_hidden_states=True,
output_attentions=output_attentions,
return_dict=return_dict,
)
if not return_dict:
encoded_layers = encoder_outputs[1]
else:
encoded_layers = encoder_outputs.hidden_states
if self.z_steps > 1:
hidden_states = encoded_layers[-2]
layers = [self.encoder.layer[-1] for _ in range(self.z_steps)]
query_states = encoded_layers[-1]
rel_embeddings = self.encoder.get_rel_embedding()
attention_mask = self.encoder.get_attention_mask(attention_mask)
rel_pos = self.encoder.get_rel_pos(embedding_output)
for layer in layers[1:]:
query_states = layer(
hidden_states,
attention_mask,
output_attentions=False,
query_states=query_states,
relative_pos=rel_pos,
rel_embeddings=rel_embeddings,
)
encoded_layers.append(query_states)
sequence_output = encoded_layers[-1]
if not return_dict:
return (sequence_output,) + encoder_outputs[(1 if output_hidden_states else 2) :]
return BaseModelOutput(
last_hidden_state=sequence_output,
hidden_states=encoder_outputs.hidden_states if output_hidden_states else None,
attentions=encoder_outputs.attentions,
)
class DebertaPredictionHeadTransform(nn.Layer):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
if isinstance(config.hidden_act, str):
self.transform_act_fn = ACT2FN[config.hidden_act]
else:
self.transform_act_fn = config.hidden_act
self.LayerNorm = nn.LayerNorm(config.hidden_size, epsilon=config.layer_norm_eps)
def forward(self, hidden_states):
hidden_states = self.dense(hidden_states)
hidden_states = self.transform_act_fn(hidden_states)
hidden_states = self.LayerNorm(hidden_states)
return hidden_states
class DebertaLMPredictionHead(nn.Layer):
def __init__(self, config):
super().__init__()
self.transform = DebertaPredictionHeadTransform(config)
self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias_attr=False)
self.bias = paddle.create_parameter(
shape=[config.vocab_size], default_initializer=nn.initializer.Constant(0.0), dtype="float32"
)
self.decoder.bias = self.bias
def forward(self, hidden_states):
hidden_states = self.transform(hidden_states)
hidden_states = self.decoder(hidden_states)
return hidden_states
class DebertaOnlyMLMHead(nn.Layer):
def __init__(self, config):
super().__init__()
self.predictions = DebertaLMPredictionHead(config)
def forward(self, sequence_output):
prediction_scores = self.predictions(sequence_output)
return prediction_scores
class DebertaForMaskedLM(DebertaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.deberta = DebertaModel(config)
self.cls = DebertaOnlyMLMHead(config)
self.post_init()
def get_output_embeddings(self):
return self.cls.predictions.decoder
def set_output_embeddings(self, new_embeddings):
self.cls.predictions.decoder = new_embeddings
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.deberta(
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,
)
sequence_output = outputs[0]
prediction_scores = self.cls(sequence_output)
masked_lm_loss = None
if labels is not None:
loss_fct = nn.CrossEntropyLoss()
masked_lm_loss = loss_fct(prediction_scores.reshape(-1, self.config.vocab_size), labels.reshape(-1))
if not return_dict:
output = (prediction_scores,) + outputs[2:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return MaskedLMOutput(
loss=masked_lm_loss,
logits=prediction_scores,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
class ContextPooler(nn.Layer):
def __init__(self, config):
super().__init__()
hidden_size = config.pooler_hidden_size if config.pooler_hidden_size is not None else config.hidden_size
self.dense = nn.Linear(config.hidden_size, hidden_size)
self.dropout = StableDropout(config.pooler_dropout)
self.config = config
def forward(self, hidden_states):
# 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)
pooled_output = self.dense(context_token)
pooled_output = F.gelu(pooled_output)
return pooled_output
@property
def output_dim(self):
return self.config.hidden_size
[docs]class DebertaForSequenceClassification(DebertaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.deberta = DebertaModel(config)
self.pooler = ContextPooler(config)
output_dim = self.pooler.output_dim if self.pooler is not None else config.hidden_size
self.classifier = nn.Linear(output_dim, config.num_labels)
drop_out = getattr(config, "cls_dropout", None)
drop_out = self.config.hidden_dropout_prob if drop_out is None else drop_out
self.dropout = StableDropout(drop_out)
[docs] def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.deberta(
input_ids,
token_type_ids=token_type_ids,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = self.pooler(outputs[0])
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
loss = None
if labels is not None:
if self.num_labels == 1:
loss_fct = paddle.nn.MSELoss()
loss = loss_fct(logits, labels)
elif labels.dtype == paddle.int64 or labels.dtype == paddle.int32:
loss_fct = paddle.nn.CrossEntropyLoss()
loss = loss_fct(logits.reshape((-1, self.num_labels)), labels.reshape((-1,)))
else:
loss_fct = paddle.nn.BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else (output[0] if len(output) == 1 else output)
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
[docs]class DebertaForTokenClassification(DebertaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.deberta = DebertaModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
[docs] def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.deberta(
input_ids,
token_type_ids=token_type_ids,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
sequence_output = self.dropout(sequence_output)
logits = self.classifier(sequence_output)
loss = None
if labels is not None:
loss_fct = nn.CrossEntropyLoss()
loss = loss_fct(logits.reshape((-1, self.num_labels)), labels.reshape((-1,)))
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else (output[0] if len(output) == 1 else output)
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
[docs]class DebertaForQuestionAnswering(DebertaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.deberta = DebertaModel(config)
self.qa_outputs = nn.Linear(config.hidden_size, 2)
[docs] def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
inputs_embeds=None,
start_positions=None,
end_positions=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.deberta(
input_ids,
token_type_ids=token_type_ids,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits = self.qa_outputs(sequence_output)
logits = paddle.transpose(logits, perm=[2, 0, 1])
start_logits, end_logits = paddle.unstack(x=logits, axis=0)
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if start_positions.ndim > 1:
start_positions = start_positions.squeeze(-1)
if start_positions.ndim > 1:
end_positions = end_positions.squeeze(-1)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = paddle.shape(start_logits)[1]
start_positions = start_positions.clip(0, ignored_index)
end_positions = end_positions.clip(0, ignored_index)
loss_fct = paddle.nn.CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(start_logits, start_positions)
end_loss = loss_fct(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2
if not return_dict:
output = (start_logits, end_logits) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return QuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
