1618 lines
69 KiB
Python
1618 lines
69 KiB
Python
# MIT License
|
|
#
|
|
# Copyright (c) 2020 The Google AI Language Team Authors, The HuggingFace Inc. team and github/lonePatient
|
|
#
|
|
# Permission is hereby granted, free of charge, to any person obtaining a copy
|
|
# of this software and associated documentation files (the "Software"), to deal
|
|
# in the Software without restriction, including without limitation the rights
|
|
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
|
# copies of the Software, and to permit persons to whom the Software is
|
|
# furnished to do so, subject to the following conditions:
|
|
#
|
|
# The above copyright notice and this permission notice shall be included in all
|
|
# copies or substantial portions of the Software.
|
|
#
|
|
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
|
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
|
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
|
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
|
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
|
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
|
# SOFTWARE.
|
|
|
|
import math
|
|
import os
|
|
import warnings
|
|
from dataclasses import dataclass
|
|
from typing import Optional, Tuple, Union
|
|
|
|
import torch
|
|
from torch import nn
|
|
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
|
|
|
|
from ...activations import ACT2FN
|
|
from ...modeling_outputs import (
|
|
BaseModelOutput,
|
|
BaseModelOutputWithPooling,
|
|
MaskedLMOutput,
|
|
MultipleChoiceModelOutput,
|
|
NextSentencePredictorOutput,
|
|
QuestionAnsweringModelOutput,
|
|
SequenceClassifierOutput,
|
|
TokenClassifierOutput,
|
|
)
|
|
from ...modeling_utils import PreTrainedModel
|
|
from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
|
|
from ...utils import (
|
|
ModelOutput,
|
|
add_code_sample_docstrings,
|
|
add_start_docstrings,
|
|
add_start_docstrings_to_model_forward,
|
|
logging,
|
|
replace_return_docstrings,
|
|
)
|
|
from .configuration_mobilebert import MobileBertConfig
|
|
|
|
|
|
logger = logging.get_logger(__name__)
|
|
|
|
_CHECKPOINT_FOR_DOC = "google/mobilebert-uncased"
|
|
_CONFIG_FOR_DOC = "MobileBertConfig"
|
|
|
|
# TokenClassification docstring
|
|
_CHECKPOINT_FOR_TOKEN_CLASSIFICATION = "mrm8488/mobilebert-finetuned-ner"
|
|
_TOKEN_CLASS_EXPECTED_OUTPUT = "['I-ORG', 'I-ORG', 'O', 'O', 'O', 'O', 'O', 'I-LOC', 'O', 'I-LOC', 'I-LOC']"
|
|
_TOKEN_CLASS_EXPECTED_LOSS = 0.03
|
|
|
|
# QuestionAnswering docstring
|
|
_CHECKPOINT_FOR_QA = "csarron/mobilebert-uncased-squad-v2"
|
|
_QA_EXPECTED_OUTPUT = "'a nice puppet'"
|
|
_QA_EXPECTED_LOSS = 3.98
|
|
_QA_TARGET_START_INDEX = 12
|
|
_QA_TARGET_END_INDEX = 13
|
|
|
|
# SequenceClassification docstring
|
|
_CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION = "lordtt13/emo-mobilebert"
|
|
_SEQ_CLASS_EXPECTED_OUTPUT = "'others'"
|
|
_SEQ_CLASS_EXPECTED_LOSS = "4.72"
|
|
|
|
|
|
from ..deprecated._archive_maps import MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402
|
|
|
|
|
|
def load_tf_weights_in_mobilebert(model, config, tf_checkpoint_path):
|
|
"""Load tf checkpoints in a pytorch model."""
|
|
try:
|
|
import re
|
|
|
|
import numpy as np
|
|
import tensorflow as tf
|
|
except ImportError:
|
|
logger.error(
|
|
"Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
|
|
"https://www.tensorflow.org/install/ for installation instructions."
|
|
)
|
|
raise
|
|
tf_path = os.path.abspath(tf_checkpoint_path)
|
|
logger.info(f"Converting TensorFlow checkpoint from {tf_path}")
|
|
# Load weights from TF model
|
|
init_vars = tf.train.list_variables(tf_path)
|
|
names = []
|
|
arrays = []
|
|
for name, shape in init_vars:
|
|
logger.info(f"Loading TF weight {name} with shape {shape}")
|
|
array = tf.train.load_variable(tf_path, name)
|
|
names.append(name)
|
|
arrays.append(array)
|
|
|
|
for name, array in zip(names, arrays):
|
|
name = name.replace("ffn_layer", "ffn")
|
|
name = name.replace("FakeLayerNorm", "LayerNorm")
|
|
name = name.replace("extra_output_weights", "dense/kernel")
|
|
name = name.replace("bert", "mobilebert")
|
|
name = name.split("/")
|
|
# adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
|
|
# which are not required for using pretrained model
|
|
if any(
|
|
n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"]
|
|
for n in name
|
|
):
|
|
logger.info(f"Skipping {'/'.join(name)}")
|
|
continue
|
|
pointer = model
|
|
for m_name in name:
|
|
if re.fullmatch(r"[A-Za-z]+_\d+", m_name):
|
|
scope_names = re.split(r"_(\d+)", m_name)
|
|
else:
|
|
scope_names = [m_name]
|
|
if scope_names[0] == "kernel" or scope_names[0] == "gamma":
|
|
pointer = getattr(pointer, "weight")
|
|
elif scope_names[0] == "output_bias" or scope_names[0] == "beta":
|
|
pointer = getattr(pointer, "bias")
|
|
elif scope_names[0] == "output_weights":
|
|
pointer = getattr(pointer, "weight")
|
|
elif scope_names[0] == "squad":
|
|
pointer = getattr(pointer, "classifier")
|
|
else:
|
|
try:
|
|
pointer = getattr(pointer, scope_names[0])
|
|
except AttributeError:
|
|
logger.info(f"Skipping {'/'.join(name)}")
|
|
continue
|
|
if len(scope_names) >= 2:
|
|
num = int(scope_names[1])
|
|
pointer = pointer[num]
|
|
if m_name[-11:] == "_embeddings":
|
|
pointer = getattr(pointer, "weight")
|
|
elif m_name == "kernel":
|
|
array = np.transpose(array)
|
|
try:
|
|
assert (
|
|
pointer.shape == array.shape
|
|
), f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched"
|
|
except AssertionError as e:
|
|
e.args += (pointer.shape, array.shape)
|
|
raise
|
|
logger.info(f"Initialize PyTorch weight {name}")
|
|
pointer.data = torch.from_numpy(array)
|
|
return model
|
|
|
|
|
|
class NoNorm(nn.Module):
|
|
def __init__(self, feat_size, eps=None):
|
|
super().__init__()
|
|
self.bias = nn.Parameter(torch.zeros(feat_size))
|
|
self.weight = nn.Parameter(torch.ones(feat_size))
|
|
|
|
def forward(self, input_tensor: torch.Tensor) -> torch.Tensor:
|
|
return input_tensor * self.weight + self.bias
|
|
|
|
|
|
NORM2FN = {"layer_norm": nn.LayerNorm, "no_norm": NoNorm}
|
|
|
|
|
|
class MobileBertEmbeddings(nn.Module):
|
|
"""Construct the embeddings from word, position and token_type embeddings."""
|
|
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.trigram_input = config.trigram_input
|
|
self.embedding_size = config.embedding_size
|
|
self.hidden_size = config.hidden_size
|
|
|
|
self.word_embeddings = nn.Embedding(config.vocab_size, config.embedding_size, padding_idx=config.pad_token_id)
|
|
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
|
|
self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
|
|
|
|
embed_dim_multiplier = 3 if self.trigram_input else 1
|
|
embedded_input_size = self.embedding_size * embed_dim_multiplier
|
|
self.embedding_transformation = nn.Linear(embedded_input_size, config.hidden_size)
|
|
|
|
self.LayerNorm = NORM2FN[config.normalization_type](config.hidden_size)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
|
|
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
|
|
self.register_buffer(
|
|
"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
) -> torch.Tensor:
|
|
if input_ids is not None:
|
|
input_shape = input_ids.size()
|
|
else:
|
|
input_shape = inputs_embeds.size()[:-1]
|
|
|
|
seq_length = input_shape[1]
|
|
|
|
if position_ids is None:
|
|
position_ids = self.position_ids[:, :seq_length]
|
|
|
|
if token_type_ids is None:
|
|
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.word_embeddings(input_ids)
|
|
|
|
if self.trigram_input:
|
|
# From the paper MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited
|
|
# Devices (https://arxiv.org/abs/2004.02984)
|
|
#
|
|
# The embedding table in BERT models accounts for a substantial proportion of model size. To compress
|
|
# the embedding layer, we reduce the embedding dimension to 128 in MobileBERT.
|
|
# Then, we apply a 1D convolution with kernel size 3 on the raw token embedding to produce a 512
|
|
# dimensional output.
|
|
inputs_embeds = torch.cat(
|
|
[
|
|
nn.functional.pad(inputs_embeds[:, 1:], [0, 0, 0, 1, 0, 0], value=0.0),
|
|
inputs_embeds,
|
|
nn.functional.pad(inputs_embeds[:, :-1], [0, 0, 1, 0, 0, 0], value=0.0),
|
|
],
|
|
dim=2,
|
|
)
|
|
if self.trigram_input or self.embedding_size != self.hidden_size:
|
|
inputs_embeds = self.embedding_transformation(inputs_embeds)
|
|
|
|
# Add positional embeddings and token type embeddings, then layer
|
|
# normalize and perform dropout.
|
|
position_embeddings = self.position_embeddings(position_ids)
|
|
token_type_embeddings = self.token_type_embeddings(token_type_ids)
|
|
embeddings = inputs_embeds + position_embeddings + token_type_embeddings
|
|
embeddings = self.LayerNorm(embeddings)
|
|
embeddings = self.dropout(embeddings)
|
|
return embeddings
|
|
|
|
|
|
class MobileBertSelfAttention(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.num_attention_heads = config.num_attention_heads
|
|
self.attention_head_size = int(config.true_hidden_size / config.num_attention_heads)
|
|
self.all_head_size = self.num_attention_heads * self.attention_head_size
|
|
|
|
self.query = nn.Linear(config.true_hidden_size, self.all_head_size)
|
|
self.key = nn.Linear(config.true_hidden_size, self.all_head_size)
|
|
self.value = nn.Linear(
|
|
config.true_hidden_size if config.use_bottleneck_attention else config.hidden_size, self.all_head_size
|
|
)
|
|
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
|
|
|
|
def transpose_for_scores(self, x):
|
|
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
|
|
x = x.view(new_x_shape)
|
|
return x.permute(0, 2, 1, 3)
|
|
|
|
def forward(
|
|
self,
|
|
query_tensor: torch.Tensor,
|
|
key_tensor: torch.Tensor,
|
|
value_tensor: torch.Tensor,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
) -> Tuple[torch.Tensor]:
|
|
mixed_query_layer = self.query(query_tensor)
|
|
mixed_key_layer = self.key(key_tensor)
|
|
mixed_value_layer = self.value(value_tensor)
|
|
|
|
query_layer = self.transpose_for_scores(mixed_query_layer)
|
|
key_layer = self.transpose_for_scores(mixed_key_layer)
|
|
value_layer = self.transpose_for_scores(mixed_value_layer)
|
|
|
|
# Take the dot product between "query" and "key" to get the raw attention scores.
|
|
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
|
|
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
|
|
if attention_mask is not None:
|
|
# Apply the attention mask is (precomputed for all layers in BertModel forward() function)
|
|
attention_scores = attention_scores + attention_mask
|
|
# Normalize the attention scores to probabilities.
|
|
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
|
|
# This is actually dropping out entire tokens to attend to, which might
|
|
# seem a bit unusual, but is taken from the original Transformer paper.
|
|
attention_probs = self.dropout(attention_probs)
|
|
# Mask heads if we want to
|
|
if head_mask is not None:
|
|
attention_probs = attention_probs * head_mask
|
|
context_layer = torch.matmul(attention_probs, value_layer)
|
|
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
|
|
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
|
|
context_layer = context_layer.view(new_context_layer_shape)
|
|
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
|
|
return outputs
|
|
|
|
|
|
class MobileBertSelfOutput(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.use_bottleneck = config.use_bottleneck
|
|
self.dense = nn.Linear(config.true_hidden_size, config.true_hidden_size)
|
|
self.LayerNorm = NORM2FN[config.normalization_type](config.true_hidden_size, eps=config.layer_norm_eps)
|
|
if not self.use_bottleneck:
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
|
|
def forward(self, hidden_states: torch.Tensor, residual_tensor: torch.Tensor) -> torch.Tensor:
|
|
layer_outputs = self.dense(hidden_states)
|
|
if not self.use_bottleneck:
|
|
layer_outputs = self.dropout(layer_outputs)
|
|
layer_outputs = self.LayerNorm(layer_outputs + residual_tensor)
|
|
return layer_outputs
|
|
|
|
|
|
class MobileBertAttention(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.self = MobileBertSelfAttention(config)
|
|
self.output = MobileBertSelfOutput(config)
|
|
self.pruned_heads = set()
|
|
|
|
def prune_heads(self, heads):
|
|
if len(heads) == 0:
|
|
return
|
|
heads, index = find_pruneable_heads_and_indices(
|
|
heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
|
|
)
|
|
|
|
# Prune linear layers
|
|
self.self.query = prune_linear_layer(self.self.query, index)
|
|
self.self.key = prune_linear_layer(self.self.key, index)
|
|
self.self.value = prune_linear_layer(self.self.value, index)
|
|
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
|
|
|
|
# Update hyper params and store pruned heads
|
|
self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
|
|
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
|
|
self.pruned_heads = self.pruned_heads.union(heads)
|
|
|
|
def forward(
|
|
self,
|
|
query_tensor: torch.Tensor,
|
|
key_tensor: torch.Tensor,
|
|
value_tensor: torch.Tensor,
|
|
layer_input: torch.Tensor,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
) -> Tuple[torch.Tensor]:
|
|
self_outputs = self.self(
|
|
query_tensor,
|
|
key_tensor,
|
|
value_tensor,
|
|
attention_mask,
|
|
head_mask,
|
|
output_attentions,
|
|
)
|
|
# Run a linear projection of `hidden_size` then add a residual
|
|
# with `layer_input`.
|
|
attention_output = self.output(self_outputs[0], layer_input)
|
|
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
|
|
return outputs
|
|
|
|
|
|
class MobileBertIntermediate(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.true_hidden_size, config.intermediate_size)
|
|
if isinstance(config.hidden_act, str):
|
|
self.intermediate_act_fn = ACT2FN[config.hidden_act]
|
|
else:
|
|
self.intermediate_act_fn = config.hidden_act
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.intermediate_act_fn(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
class OutputBottleneck(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.true_hidden_size, config.hidden_size)
|
|
self.LayerNorm = NORM2FN[config.normalization_type](config.hidden_size, eps=config.layer_norm_eps)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
|
|
def forward(self, hidden_states: torch.Tensor, residual_tensor: torch.Tensor) -> torch.Tensor:
|
|
layer_outputs = self.dense(hidden_states)
|
|
layer_outputs = self.dropout(layer_outputs)
|
|
layer_outputs = self.LayerNorm(layer_outputs + residual_tensor)
|
|
return layer_outputs
|
|
|
|
|
|
class MobileBertOutput(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.use_bottleneck = config.use_bottleneck
|
|
self.dense = nn.Linear(config.intermediate_size, config.true_hidden_size)
|
|
self.LayerNorm = NORM2FN[config.normalization_type](config.true_hidden_size)
|
|
if not self.use_bottleneck:
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
else:
|
|
self.bottleneck = OutputBottleneck(config)
|
|
|
|
def forward(
|
|
self, intermediate_states: torch.Tensor, residual_tensor_1: torch.Tensor, residual_tensor_2: torch.Tensor
|
|
) -> torch.Tensor:
|
|
layer_output = self.dense(intermediate_states)
|
|
if not self.use_bottleneck:
|
|
layer_output = self.dropout(layer_output)
|
|
layer_output = self.LayerNorm(layer_output + residual_tensor_1)
|
|
else:
|
|
layer_output = self.LayerNorm(layer_output + residual_tensor_1)
|
|
layer_output = self.bottleneck(layer_output, residual_tensor_2)
|
|
return layer_output
|
|
|
|
|
|
class BottleneckLayer(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.intra_bottleneck_size)
|
|
self.LayerNorm = NORM2FN[config.normalization_type](config.intra_bottleneck_size, eps=config.layer_norm_eps)
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
layer_input = self.dense(hidden_states)
|
|
layer_input = self.LayerNorm(layer_input)
|
|
return layer_input
|
|
|
|
|
|
class Bottleneck(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.key_query_shared_bottleneck = config.key_query_shared_bottleneck
|
|
self.use_bottleneck_attention = config.use_bottleneck_attention
|
|
self.input = BottleneckLayer(config)
|
|
if self.key_query_shared_bottleneck:
|
|
self.attention = BottleneckLayer(config)
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> Tuple[torch.Tensor]:
|
|
# This method can return three different tuples of values. These different values make use of bottlenecks,
|
|
# which are linear layers used to project the hidden states to a lower-dimensional vector, reducing memory
|
|
# usage. These linear layer have weights that are learned during training.
|
|
#
|
|
# If `config.use_bottleneck_attention`, it will return the result of the bottleneck layer four times for the
|
|
# key, query, value, and "layer input" to be used by the attention layer.
|
|
# This bottleneck is used to project the hidden. This last layer input will be used as a residual tensor
|
|
# in the attention self output, after the attention scores have been computed.
|
|
#
|
|
# If not `config.use_bottleneck_attention` and `config.key_query_shared_bottleneck`, this will return
|
|
# four values, three of which have been passed through a bottleneck: the query and key, passed through the same
|
|
# bottleneck, and the residual layer to be applied in the attention self output, through another bottleneck.
|
|
#
|
|
# Finally, in the last case, the values for the query, key and values are the hidden states without bottleneck,
|
|
# and the residual layer will be this value passed through a bottleneck.
|
|
|
|
bottlenecked_hidden_states = self.input(hidden_states)
|
|
if self.use_bottleneck_attention:
|
|
return (bottlenecked_hidden_states,) * 4
|
|
elif self.key_query_shared_bottleneck:
|
|
shared_attention_input = self.attention(hidden_states)
|
|
return (shared_attention_input, shared_attention_input, hidden_states, bottlenecked_hidden_states)
|
|
else:
|
|
return (hidden_states, hidden_states, hidden_states, bottlenecked_hidden_states)
|
|
|
|
|
|
class FFNOutput(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.intermediate_size, config.true_hidden_size)
|
|
self.LayerNorm = NORM2FN[config.normalization_type](config.true_hidden_size, eps=config.layer_norm_eps)
|
|
|
|
def forward(self, hidden_states: torch.Tensor, residual_tensor: torch.Tensor) -> torch.Tensor:
|
|
layer_outputs = self.dense(hidden_states)
|
|
layer_outputs = self.LayerNorm(layer_outputs + residual_tensor)
|
|
return layer_outputs
|
|
|
|
|
|
class FFNLayer(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.intermediate = MobileBertIntermediate(config)
|
|
self.output = FFNOutput(config)
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
intermediate_output = self.intermediate(hidden_states)
|
|
layer_outputs = self.output(intermediate_output, hidden_states)
|
|
return layer_outputs
|
|
|
|
|
|
class MobileBertLayer(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.use_bottleneck = config.use_bottleneck
|
|
self.num_feedforward_networks = config.num_feedforward_networks
|
|
|
|
self.attention = MobileBertAttention(config)
|
|
self.intermediate = MobileBertIntermediate(config)
|
|
self.output = MobileBertOutput(config)
|
|
if self.use_bottleneck:
|
|
self.bottleneck = Bottleneck(config)
|
|
if config.num_feedforward_networks > 1:
|
|
self.ffn = nn.ModuleList([FFNLayer(config) for _ in range(config.num_feedforward_networks - 1)])
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
) -> Tuple[torch.Tensor]:
|
|
if self.use_bottleneck:
|
|
query_tensor, key_tensor, value_tensor, layer_input = self.bottleneck(hidden_states)
|
|
else:
|
|
query_tensor, key_tensor, value_tensor, layer_input = [hidden_states] * 4
|
|
|
|
self_attention_outputs = self.attention(
|
|
query_tensor,
|
|
key_tensor,
|
|
value_tensor,
|
|
layer_input,
|
|
attention_mask,
|
|
head_mask,
|
|
output_attentions=output_attentions,
|
|
)
|
|
attention_output = self_attention_outputs[0]
|
|
s = (attention_output,)
|
|
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
|
|
|
|
if self.num_feedforward_networks != 1:
|
|
for i, ffn_module in enumerate(self.ffn):
|
|
attention_output = ffn_module(attention_output)
|
|
s += (attention_output,)
|
|
|
|
intermediate_output = self.intermediate(attention_output)
|
|
layer_output = self.output(intermediate_output, attention_output, hidden_states)
|
|
outputs = (
|
|
(layer_output,)
|
|
+ outputs
|
|
+ (
|
|
torch.tensor(1000),
|
|
query_tensor,
|
|
key_tensor,
|
|
value_tensor,
|
|
layer_input,
|
|
attention_output,
|
|
intermediate_output,
|
|
)
|
|
+ s
|
|
)
|
|
return outputs
|
|
|
|
|
|
class MobileBertEncoder(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.layer = nn.ModuleList([MobileBertLayer(config) for _ in range(config.num_hidden_layers)])
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
output_hidden_states: Optional[bool] = False,
|
|
return_dict: Optional[bool] = True,
|
|
) -> Union[Tuple, BaseModelOutput]:
|
|
all_hidden_states = () if output_hidden_states else None
|
|
all_attentions = () if output_attentions else None
|
|
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,
|
|
attention_mask,
|
|
head_mask[i],
|
|
output_attentions,
|
|
)
|
|
hidden_states = layer_outputs[0]
|
|
|
|
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 BaseModelOutput(
|
|
last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
|
|
)
|
|
|
|
|
|
class MobileBertPooler(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.do_activate = config.classifier_activation
|
|
if self.do_activate:
|
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
# We "pool" the model by simply taking the hidden state corresponding
|
|
# to the first token.
|
|
first_token_tensor = hidden_states[:, 0]
|
|
if not self.do_activate:
|
|
return first_token_tensor
|
|
else:
|
|
pooled_output = self.dense(first_token_tensor)
|
|
pooled_output = torch.tanh(pooled_output)
|
|
return pooled_output
|
|
|
|
|
|
class MobileBertPredictionHeadTransform(nn.Module):
|
|
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 = NORM2FN["layer_norm"](config.hidden_size, eps=config.layer_norm_eps)
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.transform_act_fn(hidden_states)
|
|
hidden_states = self.LayerNorm(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
class MobileBertLMPredictionHead(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.transform = MobileBertPredictionHeadTransform(config)
|
|
# The output weights are the same as the input embeddings, but there is
|
|
# an output-only bias for each token.
|
|
self.dense = nn.Linear(config.vocab_size, config.hidden_size - config.embedding_size, bias=False)
|
|
self.decoder = nn.Linear(config.embedding_size, config.vocab_size, bias=False)
|
|
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
|
|
# Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
|
|
self.decoder.bias = self.bias
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
hidden_states = self.transform(hidden_states)
|
|
hidden_states = hidden_states.matmul(torch.cat([self.decoder.weight.t(), self.dense.weight], dim=0))
|
|
hidden_states += self.decoder.bias
|
|
return hidden_states
|
|
|
|
|
|
class MobileBertOnlyMLMHead(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.predictions = MobileBertLMPredictionHead(config)
|
|
|
|
def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
|
|
prediction_scores = self.predictions(sequence_output)
|
|
return prediction_scores
|
|
|
|
|
|
class MobileBertPreTrainingHeads(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.predictions = MobileBertLMPredictionHead(config)
|
|
self.seq_relationship = nn.Linear(config.hidden_size, 2)
|
|
|
|
def forward(self, sequence_output: torch.Tensor, pooled_output: torch.Tensor) -> Tuple[torch.Tensor]:
|
|
prediction_scores = self.predictions(sequence_output)
|
|
seq_relationship_score = self.seq_relationship(pooled_output)
|
|
return prediction_scores, seq_relationship_score
|
|
|
|
|
|
class MobileBertPreTrainedModel(PreTrainedModel):
|
|
"""
|
|
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
|
|
models.
|
|
"""
|
|
|
|
config_class = MobileBertConfig
|
|
load_tf_weights = load_tf_weights_in_mobilebert
|
|
base_model_prefix = "mobilebert"
|
|
|
|
def _init_weights(self, module):
|
|
"""Initialize the weights"""
|
|
if isinstance(module, nn.Linear):
|
|
# Slightly different from the TF version which uses truncated_normal for initialization
|
|
# cf https://github.com/pytorch/pytorch/pull/5617
|
|
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
|
if module.bias is not None:
|
|
module.bias.data.zero_()
|
|
elif isinstance(module, nn.Embedding):
|
|
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
|
if module.padding_idx is not None:
|
|
module.weight.data[module.padding_idx].zero_()
|
|
elif isinstance(module, (nn.LayerNorm, NoNorm)):
|
|
module.bias.data.zero_()
|
|
module.weight.data.fill_(1.0)
|
|
|
|
|
|
@dataclass
|
|
class MobileBertForPreTrainingOutput(ModelOutput):
|
|
"""
|
|
Output type of [`MobileBertForPreTraining`].
|
|
|
|
Args:
|
|
loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
|
|
Total loss as the sum of the masked language modeling loss and the next sequence prediction
|
|
(classification) loss.
|
|
prediction_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
|
|
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
|
|
seq_relationship_logits (`torch.FloatTensor` of shape `(batch_size, 2)`):
|
|
Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
|
|
before SoftMax).
|
|
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
|
|
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
|
|
shape `(batch_size, sequence_length, hidden_size)`.
|
|
|
|
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
|
|
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
|
|
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
|
|
sequence_length)`.
|
|
|
|
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
|
|
heads.
|
|
"""
|
|
|
|
loss: Optional[torch.FloatTensor] = None
|
|
prediction_logits: torch.FloatTensor = None
|
|
seq_relationship_logits: torch.FloatTensor = None
|
|
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
|
|
attentions: Optional[Tuple[torch.FloatTensor]] = None
|
|
|
|
|
|
MOBILEBERT_START_DOCSTRING = r"""
|
|
|
|
This model inherits from [`PreTrainedModel`]. 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 PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
|
|
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
|
|
and behavior.
|
|
|
|
Parameters:
|
|
config ([`MobileBertConfig`]): 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.
|
|
"""
|
|
|
|
MOBILEBERT_INPUTS_DOCSTRING = r"""
|
|
Args:
|
|
input_ids (`torch.LongTensor` 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 (`torch.FloatTensor` 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 (`torch.LongTensor` 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 (`torch.LongTensor` 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)
|
|
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
|
|
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 indicates the head is **not masked**,
|
|
- 0 indicates the head is **masked**.
|
|
|
|
inputs_embeds (`torch.FloatTensor` 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.
|
|
"""
|
|
|
|
|
|
@add_start_docstrings(
|
|
"The bare MobileBert Model transformer outputting raw hidden-states without any specific head on top.",
|
|
MOBILEBERT_START_DOCSTRING,
|
|
)
|
|
class MobileBertModel(MobileBertPreTrainedModel):
|
|
"""
|
|
https://arxiv.org/pdf/2004.02984.pdf
|
|
"""
|
|
|
|
def __init__(self, config, add_pooling_layer=True):
|
|
super().__init__(config)
|
|
self.config = config
|
|
self.embeddings = MobileBertEmbeddings(config)
|
|
self.encoder = MobileBertEncoder(config)
|
|
|
|
self.pooler = MobileBertPooler(config) if add_pooling_layer else None
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.embeddings.word_embeddings
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.embeddings.word_embeddings = value
|
|
|
|
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
|
|
"""
|
|
for layer, heads in heads_to_prune.items():
|
|
self.encoder.layer[layer].attention.prune_heads(heads)
|
|
|
|
@add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
output_type=BaseModelOutputWithPooling,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, BaseModelOutputWithPooling]:
|
|
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:
|
|
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
|
|
input_shape = input_ids.size()
|
|
elif inputs_embeds is not None:
|
|
input_shape = inputs_embeds.size()[:-1]
|
|
else:
|
|
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
|
|
|
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
|
|
|
if attention_mask is None:
|
|
attention_mask = torch.ones(input_shape, device=device)
|
|
if token_type_ids is None:
|
|
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
|
|
|
|
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
|
|
# ourselves in which case we just need to make it broadcastable to all heads.
|
|
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
|
|
|
|
# Prepare head mask if needed
|
|
# 1.0 in head_mask indicate we keep the head
|
|
# attention_probs has shape bsz x n_heads x N x N
|
|
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
|
|
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
|
|
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
|
|
|
|
embedding_output = self.embeddings(
|
|
input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds
|
|
)
|
|
encoder_outputs = self.encoder(
|
|
embedding_output,
|
|
attention_mask=extended_attention_mask,
|
|
head_mask=head_mask,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
sequence_output = encoder_outputs[0]
|
|
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
|
|
|
|
if not return_dict:
|
|
return (sequence_output, pooled_output) + encoder_outputs[1:]
|
|
|
|
return BaseModelOutputWithPooling(
|
|
last_hidden_state=sequence_output,
|
|
pooler_output=pooled_output,
|
|
hidden_states=encoder_outputs.hidden_states,
|
|
attentions=encoder_outputs.attentions,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
MobileBert Model with two heads on top as done during the pretraining: a `masked language modeling` head and a
|
|
`next sentence prediction (classification)` head.
|
|
""",
|
|
MOBILEBERT_START_DOCSTRING,
|
|
)
|
|
class MobileBertForPreTraining(MobileBertPreTrainedModel):
|
|
_tied_weights_keys = ["cls.predictions.decoder.weight", "cls.predictions.decoder.bias"]
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.mobilebert = MobileBertModel(config)
|
|
self.cls = MobileBertPreTrainingHeads(config)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_output_embeddings(self):
|
|
return self.cls.predictions.decoder
|
|
|
|
def set_output_embeddings(self, new_embeddigs):
|
|
self.cls.predictions.decoder = new_embeddigs
|
|
|
|
def resize_token_embeddings(self, new_num_tokens: Optional[int] = None) -> nn.Embedding:
|
|
# resize dense output embedings at first
|
|
self.cls.predictions.dense = self._get_resized_lm_head(
|
|
self.cls.predictions.dense, new_num_tokens=new_num_tokens, transposed=True
|
|
)
|
|
|
|
return super().resize_token_embeddings(new_num_tokens=new_num_tokens)
|
|
|
|
@add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@replace_return_docstrings(output_type=MobileBertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
next_sentence_label: Optional[torch.LongTensor] = None,
|
|
output_attentions: Optional[torch.FloatTensor] = None,
|
|
output_hidden_states: Optional[torch.FloatTensor] = None,
|
|
return_dict: Optional[torch.FloatTensor] = None,
|
|
) -> Union[Tuple, MobileBertForPreTrainingOutput]:
|
|
r"""
|
|
labels (`torch.LongTensor` 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]`
|
|
next_sentence_label (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair
|
|
(see `input_ids` docstring) Indices should be in `[0, 1]`:
|
|
|
|
- 0 indicates sequence B is a continuation of sequence A,
|
|
- 1 indicates sequence B is a random sequence.
|
|
|
|
Returns:
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, MobileBertForPreTraining
|
|
>>> import torch
|
|
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("google/mobilebert-uncased")
|
|
>>> model = MobileBertForPreTraining.from_pretrained("google/mobilebert-uncased")
|
|
|
|
>>> input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)
|
|
>>> # Batch size 1
|
|
>>> outputs = model(input_ids)
|
|
|
|
>>> prediction_logits = outputs.prediction_logits
|
|
>>> seq_relationship_logits = outputs.seq_relationship_logits
|
|
```"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.mobilebert(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
sequence_output, pooled_output = outputs[:2]
|
|
prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output)
|
|
|
|
total_loss = None
|
|
if labels is not None and next_sentence_label is not None:
|
|
loss_fct = CrossEntropyLoss()
|
|
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
|
|
next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1))
|
|
total_loss = masked_lm_loss + next_sentence_loss
|
|
|
|
if not return_dict:
|
|
output = (prediction_scores, seq_relationship_score) + outputs[2:]
|
|
return ((total_loss,) + output) if total_loss is not None else output
|
|
|
|
return MobileBertForPreTrainingOutput(
|
|
loss=total_loss,
|
|
prediction_logits=prediction_scores,
|
|
seq_relationship_logits=seq_relationship_score,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@add_start_docstrings("""MobileBert Model with a `language modeling` head on top.""", MOBILEBERT_START_DOCSTRING)
|
|
class MobileBertForMaskedLM(MobileBertPreTrainedModel):
|
|
_tied_weights_keys = ["cls.predictions.decoder.weight", "cls.predictions.decoder.bias"]
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.mobilebert = MobileBertModel(config, add_pooling_layer=False)
|
|
self.cls = MobileBertOnlyMLMHead(config)
|
|
self.config = config
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_output_embeddings(self):
|
|
return self.cls.predictions.decoder
|
|
|
|
def set_output_embeddings(self, new_embeddigs):
|
|
self.cls.predictions.decoder = new_embeddigs
|
|
|
|
def resize_token_embeddings(self, new_num_tokens: Optional[int] = None) -> nn.Embedding:
|
|
# resize dense output embedings at first
|
|
self.cls.predictions.dense = self._get_resized_lm_head(
|
|
self.cls.predictions.dense, new_num_tokens=new_num_tokens, transposed=True
|
|
)
|
|
return super().resize_token_embeddings(new_num_tokens=new_num_tokens)
|
|
|
|
@add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
output_type=MaskedLMOutput,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
expected_output="'paris'",
|
|
expected_loss=0.57,
|
|
)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, MaskedLMOutput]:
|
|
r"""
|
|
labels (`torch.LongTensor` 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]`
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.mobilebert(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
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 = CrossEntropyLoss() # -100 index = padding token
|
|
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-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 MobileBertOnlyNSPHead(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.seq_relationship = nn.Linear(config.hidden_size, 2)
|
|
|
|
def forward(self, pooled_output: torch.Tensor) -> torch.Tensor:
|
|
seq_relationship_score = self.seq_relationship(pooled_output)
|
|
return seq_relationship_score
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""MobileBert Model with a `next sentence prediction (classification)` head on top.""",
|
|
MOBILEBERT_START_DOCSTRING,
|
|
)
|
|
class MobileBertForNextSentencePrediction(MobileBertPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
self.mobilebert = MobileBertModel(config)
|
|
self.cls = MobileBertOnlyNSPHead(config)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@replace_return_docstrings(output_type=NextSentencePredictorOutput, config_class=_CONFIG_FOR_DOC)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
**kwargs,
|
|
) -> Union[Tuple, NextSentencePredictorOutput]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair
|
|
(see `input_ids` docstring) Indices should be in `[0, 1]`.
|
|
|
|
- 0 indicates sequence B is a continuation of sequence A,
|
|
- 1 indicates sequence B is a random sequence.
|
|
|
|
Returns:
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, MobileBertForNextSentencePrediction
|
|
>>> import torch
|
|
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("google/mobilebert-uncased")
|
|
>>> model = MobileBertForNextSentencePrediction.from_pretrained("google/mobilebert-uncased")
|
|
|
|
>>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
|
|
>>> next_sentence = "The sky is blue due to the shorter wavelength of blue light."
|
|
>>> encoding = tokenizer(prompt, next_sentence, return_tensors="pt")
|
|
|
|
>>> outputs = model(**encoding, labels=torch.LongTensor([1]))
|
|
>>> loss = outputs.loss
|
|
>>> logits = outputs.logits
|
|
```"""
|
|
|
|
if "next_sentence_label" in kwargs:
|
|
warnings.warn(
|
|
"The `next_sentence_label` argument is deprecated and will be removed in a future version, use"
|
|
" `labels` instead.",
|
|
FutureWarning,
|
|
)
|
|
labels = kwargs.pop("next_sentence_label")
|
|
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.mobilebert(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
pooled_output = outputs[1]
|
|
seq_relationship_score = self.cls(pooled_output)
|
|
|
|
next_sentence_loss = None
|
|
if labels is not None:
|
|
loss_fct = CrossEntropyLoss()
|
|
next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), labels.view(-1))
|
|
|
|
if not return_dict:
|
|
output = (seq_relationship_score,) + outputs[2:]
|
|
return ((next_sentence_loss,) + output) if next_sentence_loss is not None else output
|
|
|
|
return NextSentencePredictorOutput(
|
|
loss=next_sentence_loss,
|
|
logits=seq_relationship_score,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
MobileBert Model transformer with a sequence classification/regression head on top (a linear layer on top of the
|
|
pooled output) e.g. for GLUE tasks.
|
|
""",
|
|
MOBILEBERT_START_DOCSTRING,
|
|
)
|
|
# Copied from transformers.models.bert.modeling_bert.BertForSequenceClassification with Bert->MobileBert all-casing
|
|
class MobileBertForSequenceClassification(MobileBertPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
self.config = config
|
|
|
|
self.mobilebert = MobileBertModel(config)
|
|
classifier_dropout = (
|
|
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
|
|
)
|
|
self.dropout = nn.Dropout(classifier_dropout)
|
|
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION,
|
|
output_type=SequenceClassifierOutput,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
expected_output=_SEQ_CLASS_EXPECTED_OUTPUT,
|
|
expected_loss=_SEQ_CLASS_EXPECTED_LOSS,
|
|
)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
labels: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
|
|
r"""
|
|
labels (`torch.LongTensor` 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).
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.mobilebert(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
pooled_output = outputs[1]
|
|
|
|
pooled_output = self.dropout(pooled_output)
|
|
logits = self.classifier(pooled_output)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
if self.config.problem_type is None:
|
|
if self.num_labels == 1:
|
|
self.config.problem_type = "regression"
|
|
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
|
|
self.config.problem_type = "single_label_classification"
|
|
else:
|
|
self.config.problem_type = "multi_label_classification"
|
|
|
|
if self.config.problem_type == "regression":
|
|
loss_fct = MSELoss()
|
|
if self.num_labels == 1:
|
|
loss = loss_fct(logits.squeeze(), labels.squeeze())
|
|
else:
|
|
loss = loss_fct(logits, labels)
|
|
elif self.config.problem_type == "single_label_classification":
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
|
|
elif self.config.problem_type == "multi_label_classification":
|
|
loss_fct = BCEWithLogitsLoss()
|
|
loss = loss_fct(logits, labels)
|
|
if not return_dict:
|
|
output = (logits,) + outputs[2:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return SequenceClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
MobileBert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a
|
|
linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
|
|
""",
|
|
MOBILEBERT_START_DOCSTRING,
|
|
)
|
|
# Copied from transformers.models.bert.modeling_bert.BertForQuestionAnswering with Bert->MobileBert all-casing
|
|
class MobileBertForQuestionAnswering(MobileBertPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
|
|
self.mobilebert = MobileBertModel(config, add_pooling_layer=False)
|
|
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_QA,
|
|
output_type=QuestionAnsweringModelOutput,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
qa_target_start_index=_QA_TARGET_START_INDEX,
|
|
qa_target_end_index=_QA_TARGET_END_INDEX,
|
|
expected_output=_QA_EXPECTED_OUTPUT,
|
|
expected_loss=_QA_EXPECTED_LOSS,
|
|
)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
start_positions: Optional[torch.Tensor] = None,
|
|
end_positions: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]:
|
|
r"""
|
|
start_positions (`torch.LongTensor` 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 (`torch.LongTensor` 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.
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.mobilebert(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
sequence_output = outputs[0]
|
|
|
|
logits = self.qa_outputs(sequence_output)
|
|
start_logits, end_logits = logits.split(1, dim=-1)
|
|
start_logits = start_logits.squeeze(-1).contiguous()
|
|
end_logits = end_logits.squeeze(-1).contiguous()
|
|
|
|
total_loss = None
|
|
if start_positions is not None and end_positions is not None:
|
|
# If we are on multi-GPU, split add a dimension
|
|
if len(start_positions.size()) > 1:
|
|
start_positions = start_positions.squeeze(-1)
|
|
if len(end_positions.size()) > 1:
|
|
end_positions = end_positions.squeeze(-1)
|
|
# sometimes the start/end positions are outside our model inputs, we ignore these terms
|
|
ignored_index = start_logits.size(1)
|
|
start_positions = start_positions.clamp(0, ignored_index)
|
|
end_positions = end_positions.clamp(0, ignored_index)
|
|
|
|
loss_fct = 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,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
MobileBert Model with a multiple choice classification head on top (a linear layer on top of the pooled output and
|
|
a softmax) e.g. for RocStories/SWAG tasks.
|
|
""",
|
|
MOBILEBERT_START_DOCSTRING,
|
|
)
|
|
# Copied from transformers.models.bert.modeling_bert.BertForMultipleChoice with Bert->MobileBert all-casing
|
|
class MobileBertForMultipleChoice(MobileBertPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
self.mobilebert = MobileBertModel(config)
|
|
classifier_dropout = (
|
|
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
|
|
)
|
|
self.dropout = nn.Dropout(classifier_dropout)
|
|
self.classifier = nn.Linear(config.hidden_size, 1)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@add_start_docstrings_to_model_forward(
|
|
MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")
|
|
)
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
output_type=MultipleChoiceModelOutput,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
labels: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple[torch.Tensor], MultipleChoiceModelOutput]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
|
|
num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
|
|
`input_ids` above)
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
|
|
|
|
input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
|
|
attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
|
|
token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
|
|
position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
|
|
inputs_embeds = (
|
|
inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
|
|
if inputs_embeds is not None
|
|
else None
|
|
)
|
|
|
|
outputs = self.mobilebert(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
pooled_output = outputs[1]
|
|
|
|
pooled_output = self.dropout(pooled_output)
|
|
logits = self.classifier(pooled_output)
|
|
reshaped_logits = logits.view(-1, num_choices)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(reshaped_logits, labels)
|
|
|
|
if not return_dict:
|
|
output = (reshaped_logits,) + outputs[2:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return MultipleChoiceModelOutput(
|
|
loss=loss,
|
|
logits=reshaped_logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
MobileBert Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g.
|
|
for Named-Entity-Recognition (NER) tasks.
|
|
""",
|
|
MOBILEBERT_START_DOCSTRING,
|
|
)
|
|
# Copied from transformers.models.bert.modeling_bert.BertForTokenClassification with Bert->MobileBert all-casing
|
|
class MobileBertForTokenClassification(MobileBertPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
|
|
self.mobilebert = MobileBertModel(config, add_pooling_layer=False)
|
|
classifier_dropout = (
|
|
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
|
|
)
|
|
self.dropout = nn.Dropout(classifier_dropout)
|
|
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_TOKEN_CLASSIFICATION,
|
|
output_type=TokenClassifierOutput,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
expected_output=_TOKEN_CLASS_EXPECTED_OUTPUT,
|
|
expected_loss=_TOKEN_CLASS_EXPECTED_LOSS,
|
|
)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
labels: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.mobilebert(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
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 = CrossEntropyLoss()
|
|
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
|
|
|
|
if not return_dict:
|
|
output = (logits,) + outputs[2:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return TokenClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|