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from transformers.file_utils import cached_path, hf_bucket_url
from importlib.machinery import SourceFileLoader
import os
from transformers import EncoderDecoderModel, AutoConfig, AutoModel, EncoderDecoderConfig, RobertaForCausalLM, \
RobertaModel
from transformers.modeling_utils import PreTrainedModel, logging
import torch
from torch.nn import CrossEntropyLoss, Parameter
from transformers.modeling_outputs import Seq2SeqLMOutput, CausalLMOutputWithCrossAttentions, \
ModelOutput
from attentions import ScaledDotProductAttention, MultiHeadAttention
from collections import namedtuple
from typing import Dict, Any, Optional, Tuple
from dataclasses import dataclass
import random
from model_config_handling import EncoderDecoderSpokenNormConfig, DecoderSpokenNormConfig, PretrainedConfig
cache_dir = './cache'
model_name = 'nguyenvulebinh/envibert'
if not os.path.exists(cache_dir):
os.makedirs(cache_dir)
logger = logging.get_logger(__name__)
@dataclass
class SpokenNormOutput(ModelOutput):
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
logits_spoken_tagging: torch.FloatTensor = None
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
encoder_last_hidden_state: Optional[torch.FloatTensor] = None
encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
def collect_spoken_phrases_features(encoder_hidden_states, word_src_lengths, spoken_label):
list_features = []
list_features_mask = []
max_length = word_src_lengths.max()
feature_pad = torch.zeros_like(encoder_hidden_states[0, :1, :])
for hidden_state, word_length, list_idx in zip(encoder_hidden_states, word_src_lengths, spoken_label):
for idx in list_idx:
if idx > 0:
start = sum(word_length[:idx])
end = start + word_length[idx]
remain_length = max_length - word_length[idx]
list_features_mask.append(torch.cat([torch.ones_like(spoken_label[0, 0]).expand(word_length[idx]),
torch.zeros_like(
spoken_label[0, 0].expand(remain_length))]).unsqueeze(0))
spoken_phrases_feature = hidden_state[start: end]
list_features.append(torch.cat([spoken_phrases_feature,
feature_pad.expand(remain_length, feature_pad.size(-1))]).unsqueeze(0))
return torch.cat(list_features), torch.cat(list_features_mask)
def collect_spoken_phrases_labels(decoder_input_ids, labels, labels_bias, word_tgt_lengths, spoken_idx):
list_decoder_input_ids = []
list_labels = []
list_labels_bias = []
max_length = word_tgt_lengths.max()
init_decoder_ids = torch.tensor([0], device=labels.device, dtype=labels.dtype)
pad_decoder_ids = torch.tensor([1], device=labels.device, dtype=labels.dtype)
eos_decoder_ids = torch.tensor([2], device=labels.device, dtype=labels.dtype)
none_labels_bias = torch.tensor([0], device=labels.device, dtype=labels.dtype)
ignore_labels_bias = torch.tensor([-100], device=labels.device, dtype=labels.dtype)
for decoder_inputs, decoder_label, decoder_label_bias, word_length, list_idx in zip(decoder_input_ids,
labels, labels_bias,
word_tgt_lengths, spoken_idx):
for idx in list_idx:
if idx > 0:
start = sum(word_length[:idx - 1])
end = start + word_length[idx - 1]
remain_length = max_length - word_length[idx - 1]
remain_decoder_input_ids = max_length - len(decoder_inputs[start + 1:end + 1])
list_decoder_input_ids.append(torch.cat([init_decoder_ids,
decoder_inputs[start + 1:end + 1],
pad_decoder_ids.expand(remain_decoder_input_ids)]).unsqueeze(0))
list_labels.append(torch.cat([decoder_label[start:end],
eos_decoder_ids,
ignore_labels_bias.expand(remain_length)]).unsqueeze(0))
list_labels_bias.append(torch.cat([decoder_label_bias[start:end],
none_labels_bias,
ignore_labels_bias.expand(remain_length)]).unsqueeze(0))
decoder_input_ids = torch.cat(list_decoder_input_ids)
labels = torch.cat(list_labels)
labels_bias = torch.cat(list_labels_bias)
return decoder_input_ids, labels, labels_bias
class EncoderDecoderSpokenNorm(EncoderDecoderModel):
config_class = EncoderDecoderSpokenNormConfig
def __init__(
self,
config: Optional[PretrainedConfig] = None,
encoder: Optional[PreTrainedModel] = None,
decoder: Optional[PreTrainedModel] = None,
):
if config is None and (encoder is None or decoder is None):
raise ValueError("Either a configuration or an encoder and a decoder has to be provided.")
if config is None:
config = EncoderDecoderConfig.from_encoder_decoder_configs(encoder.config, decoder.config)
else:
if not isinstance(config, self.config_class):
raise ValueError(f"Config: {config} has to be of type {self.config_class}")
if config.decoder.cross_attention_hidden_size is not None:
if config.decoder.cross_attention_hidden_size != config.encoder.hidden_size:
raise ValueError(
"If `cross_attention_hidden_size` is specified in the decoder's configuration, "
"it has to be equal to the encoder's `hidden_size`. "
f"Got {config.decoder.cross_attention_hidden_size} for `config.decoder.cross_attention_hidden_size` "
f"and {config.encoder.hidden_size} for `config.encoder.hidden_size`."
)
# initialize with config
super().__init__(config)
if encoder is None:
from transformers.models.auto.modeling_auto import AutoModel
encoder = AutoModel.from_config(config.encoder)
if decoder is None:
# from transformers.models.auto.modeling_auto import AutoModelForCausalLM
decoder = DecoderSpokenNorm._from_config(config.decoder)
self.encoder = encoder
self.decoder = decoder
if self.encoder.config.to_dict() != self.config.encoder.to_dict():
logger.warning(
f"Config of the encoder: {self.encoder.__class__} is overwritten by shared encoder config: {self.config.encoder}"
)
if self.decoder.config.to_dict() != self.config.decoder.to_dict():
logger.warning(
f"Config of the decoder: {self.decoder.__class__} is overwritten by shared decoder config: {self.config.decoder}"
)
# make sure that the individual model's config refers to the shared config
# so that the updates to the config will be synced
self.encoder.config = self.config.encoder
self.decoder.config = self.config.decoder
# encoder outputs might need to be projected to different dimension for decoder
if (
self.encoder.config.hidden_size != self.decoder.config.hidden_size
and self.decoder.config.cross_attention_hidden_size is None
):
self.enc_to_dec_proj = torch.nn.Linear(self.encoder.config.hidden_size, self.decoder.config.hidden_size)
if self.encoder.get_output_embeddings() is not None:
raise ValueError(
f"The encoder {self.encoder} should not have a LM Head. Please use a model without LM Head"
)
# spoken tagging
self.dropout = torch.nn.Dropout(0.3)
# 0: "O", 1: "B", 2: "I"
self.spoken_tagging_classifier = torch.nn.Linear(config.encoder.hidden_size, 3)
# tie encoder, decoder weights if config set accordingly
self.tie_weights()
@classmethod
def from_encoder_decoder_pretrained(
cls,
encoder_pretrained_model_name_or_path: str = None,
decoder_pretrained_model_name_or_path: str = None,
*model_args,
**kwargs
) -> PreTrainedModel:
kwargs_encoder = {
argument[len("encoder_"):]: value for argument, value in kwargs.items() if argument.startswith("encoder_")
}
kwargs_decoder = {
argument[len("decoder_"):]: value for argument, value in kwargs.items() if argument.startswith("decoder_")
}
# remove encoder, decoder kwargs from kwargs
for key in kwargs_encoder.keys():
del kwargs["encoder_" + key]
for key in kwargs_decoder.keys():
del kwargs["decoder_" + key]
# Load and initialize the encoder and decoder
# The distinction between encoder and decoder at the model level is made
# by the value of the flag `is_decoder` that we need to set correctly.
encoder = kwargs_encoder.pop("model", None)
if encoder is None:
if encoder_pretrained_model_name_or_path is None:
raise ValueError(
"If `encoder_model` is not defined as an argument, a `encoder_pretrained_model_name_or_path` has "
"to be defined."
)
if "config" not in kwargs_encoder:
encoder_config = AutoConfig.from_pretrained(encoder_pretrained_model_name_or_path)
if encoder_config.is_decoder is True or encoder_config.add_cross_attention is True:
logger.info(
f"Initializing {encoder_pretrained_model_name_or_path} as a encoder model "
"from a decoder model. Cross-attention and casual mask are disabled."
)
encoder_config.is_decoder = False
encoder_config.add_cross_attention = False
kwargs_encoder["config"] = encoder_config
encoder = AutoModel.from_pretrained(encoder_pretrained_model_name_or_path, *model_args,
**kwargs_encoder)
decoder = kwargs_decoder.pop("model", None)
if decoder is None:
if decoder_pretrained_model_name_or_path is None:
raise ValueError(
"If `decoder_model` is not defined as an argument, a `decoder_pretrained_model_name_or_path` has "
"to be defined."
)
if "config" not in kwargs_decoder:
decoder_config = DecoderSpokenNormConfig.from_pretrained(decoder_pretrained_model_name_or_path)
if decoder_config.is_decoder is False or decoder_config.add_cross_attention is False:
logger.info(
f"Initializing {decoder_pretrained_model_name_or_path} as a decoder model. "
f"Cross attention layers are added to {decoder_pretrained_model_name_or_path} "
f"and randomly initialized if {decoder_pretrained_model_name_or_path}'s architecture allows for "
"cross attention layers."
)
decoder_config.is_decoder = True
decoder_config.add_cross_attention = True
kwargs_decoder["config"] = decoder_config
if kwargs_decoder["config"].is_decoder is False or kwargs_decoder["config"].add_cross_attention is False:
logger.warning(
f"Decoder model {decoder_pretrained_model_name_or_path} is not initialized as a decoder. "
f"In order to initialize {decoder_pretrained_model_name_or_path} as a decoder, "
"make sure that the attributes `is_decoder` and `add_cross_attention` of `decoder_config` "
"passed to `.from_encoder_decoder_pretrained(...)` are set to `True` or do not pass a "
"`decoder_config` to `.from_encoder_decoder_pretrained(...)`"
)
decoder = DecoderSpokenNorm.from_pretrained(decoder_pretrained_model_name_or_path, **kwargs_decoder)
# instantiate config with corresponding kwargs
config = EncoderDecoderSpokenNormConfig.from_encoder_decoder_configs(encoder.config, decoder.config, **kwargs)
return cls(encoder=encoder, decoder=decoder, config=config)
def get_encoder(self):
def forward(input_ids=None,
attention_mask=None,
bias_input_ids=None,
bias_attention_mask=None,
return_dict=True,
output_attentions=False,
output_hidden_states=False,
word_src_lengths=None,
spoken_idx=None,
**kwargs_encoder):
encoder_outputs = self.encoder(
input_ids=input_ids,
attention_mask=attention_mask,
inputs_embeds=None,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
**kwargs_encoder,
)
encoder_outputs.word_src_lengths = word_src_lengths
encoder_outputs.spoken_tagging_output = self.spoken_tagging_classifier(self.dropout(encoder_outputs[0]))
if spoken_idx is not None:
encoder_outputs.spoken_idx = spoken_idx
else:
pass
encoder_bias_outputs = self.forward_bias(bias_input_ids,
bias_attention_mask,
output_attentions=output_attentions,
return_dict=return_dict,
output_hidden_states=output_hidden_states,
**kwargs_encoder)
# d = {
# "encoder_bias_outputs": None,
# "bias_attention_mask": None,
# "last_hidden_state": None,
# "pooler_output": None
#
# }
# encoder_bias_outputs = namedtuple('Struct', d.keys())(*d.values())
# if bias_input_ids is not None:
# encoder_bias_outputs = self.encoder(
# input_ids=bias_input_ids,
# attention_mask=bias_attention_mask,
# inputs_embeds=None,
# output_attentions=output_attentions,
# output_hidden_states=output_hidden_states,
# return_dict=return_dict,
# **kwargs_encoder,
# )
# encoder_bias_outputs.bias_attention_mask = bias_attention_mask
return encoder_outputs, encoder_bias_outputs
return forward
def forward_bias(self,
bias_input_ids,
bias_attention_mask,
output_attentions=False,
return_dict=True,
output_hidden_states=False,
**kwargs_encoder):
d = {
"encoder_bias_outputs": None,
"bias_attention_mask": None,
"last_hidden_state": None,
"pooler_output": None
}
encoder_bias_outputs = namedtuple('Struct', d.keys())(*d.values())
if bias_input_ids is not None:
encoder_bias_outputs = self.encoder(
input_ids=bias_input_ids,
attention_mask=bias_attention_mask,
inputs_embeds=None,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
**kwargs_encoder,
)
encoder_bias_outputs.bias_attention_mask = bias_attention_mask
return encoder_bias_outputs
def _prepare_encoder_decoder_kwargs_for_generation(
self, input_ids: torch.LongTensor, model_kwargs, model_input_name
) -> Dict[str, Any]:
if "encoder_outputs" not in model_kwargs:
# retrieve encoder hidden states
encoder = self.get_encoder()
encoder_kwargs = {
argument: value
for argument, value in model_kwargs.items()
if not (argument.startswith("decoder_") or argument.startswith("cross_attn"))
}
encoder_outputs, encoder_bias_outputs = encoder(input_ids, return_dict=True, **encoder_kwargs)
model_kwargs["encoder_outputs"]: ModelOutput = encoder_outputs
model_kwargs["encoder_bias_outputs"]: ModelOutput = encoder_bias_outputs
return model_kwargs
def _prepare_decoder_input_ids_for_generation(
self,
batch_size: int,
decoder_start_token_id: int = None,
bos_token_id: int = None,
model_kwargs: Optional[Dict[str, torch.Tensor]] = None,
) -> torch.LongTensor:
if model_kwargs is not None and "decoder_input_ids" in model_kwargs:
return model_kwargs.pop("decoder_input_ids")
else:
decoder_start_token_id = self._get_decoder_start_token_id(decoder_start_token_id, bos_token_id)
num_spoken_phrases = (model_kwargs['encoder_outputs'].spoken_idx >= 0).view(-1).sum()
return torch.ones((num_spoken_phrases, 1), dtype=torch.long, device=self.device) * decoder_start_token_id
def prepare_inputs_for_generation(
self, input_ids, past=None, attention_mask=None, use_cache=None, encoder_outputs=None, **kwargs
):
decoder_inputs = self.decoder.prepare_inputs_for_generation(input_ids, past=past)
decoder_attention_mask = decoder_inputs["attention_mask"] if "attention_mask" in decoder_inputs else None
input_dict = {
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"decoder_input_ids": decoder_inputs["input_ids"],
"encoder_outputs": encoder_outputs,
"encoder_bias_outputs": kwargs["encoder_bias_outputs"],
"past_key_values": decoder_inputs["past_key_values"],
"use_cache": use_cache,
}
return input_dict
def forward(
self,
input_ids=None,
attention_mask=None,
decoder_input_ids=None,
bias_input_ids=None,
bias_attention_mask=None,
labels_bias=None,
decoder_attention_mask=None,
encoder_outputs=None,
encoder_bias_outputs=None,
past_key_values=None,
inputs_embeds=None,
decoder_inputs_embeds=None,
labels=None,
use_cache=None,
spoken_label=None,
word_src_lengths=None,
word_tgt_lengths=None,
spoken_idx=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
inputs_length=None,
outputs=None,
outputs_length=None,
text=None,
**kwargs,
):
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
kwargs_encoder = {argument: value for argument, value in kwargs.items() if not argument.startswith("decoder_")}
kwargs_decoder = {
argument[len("decoder_"):]: value for argument, value in kwargs.items() if argument.startswith("decoder_")
}
spoken_tagging_output = None
if encoder_outputs is None:
encoder_outputs = self.encoder(
input_ids=input_ids,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
**kwargs_encoder,
)
spoken_tagging_output = self.spoken_tagging_classifier(self.dropout(encoder_outputs[0]))
# else:
# word_src_lengths = encoder_outputs.word_src_lengths
# spoken_tagging_output = encoder_outputs.spoken_tagging_output
if encoder_bias_outputs is None:
encoder_bias_outputs = self.encoder(
input_ids=bias_input_ids,
attention_mask=bias_attention_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
**kwargs_encoder,
)
encoder_bias_outputs.bias_attention_mask = bias_attention_mask
encoder_hidden_states = encoder_outputs[0]
# if spoken_idx is None:
# # extract spoken_idx from spoken_tagging_output
# spoken_idx = None
# encoder_hidden_states, attention_mask = collect_spoken_phrases_features(encoder_hidden_states,
# word_src_lengths,
# spoken_idx)
# if labels is not None:
# decoder_input_ids, labels, labels_bias = collect_spoken_phrases_labels(decoder_input_ids,
# labels, labels_bias,
# word_tgt_lengths,
# spoken_idx)
if spoken_idx is not None:
encoder_hidden_states, attention_mask = collect_spoken_phrases_features(encoder_hidden_states,
word_src_lengths,
spoken_idx)
decoder_input_ids, labels, labels_bias = collect_spoken_phrases_labels(decoder_input_ids,
labels, labels_bias,
word_tgt_lengths,
spoken_idx)
# optionally project encoder_hidden_states
if (
self.encoder.config.hidden_size != self.decoder.config.hidden_size
and self.decoder.config.cross_attention_hidden_size is None
):
encoder_hidden_states = self.enc_to_dec_proj(encoder_hidden_states)
# Decode
decoder_outputs = self.decoder(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_bias_pooling=encoder_bias_outputs.pooler_output,
# encoder_bias_hidden_states=encoder_bias_outputs[0],
encoder_bias_hidden_states=encoder_bias_outputs.last_hidden_state,
bias_attention_mask=encoder_bias_outputs.bias_attention_mask,
encoder_attention_mask=attention_mask,
inputs_embeds=decoder_inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
use_cache=use_cache,
past_key_values=past_key_values,
return_dict=return_dict,
labels_bias=labels_bias,
**kwargs_decoder,
)
# Compute loss independent from decoder (as some shift the logits inside them)
loss = None
if labels is not None:
logits = decoder_outputs.logits if return_dict else decoder_outputs[1]
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.reshape(-1, self.decoder.config.vocab_size), labels.view(-1))
loss = loss + decoder_outputs.loss
if spoken_label is not None:
loss_fct = CrossEntropyLoss()
spoken_tagging_loss = loss_fct(spoken_tagging_output.reshape(-1, 3), spoken_label.view(-1))
loss = loss + spoken_tagging_loss
if not return_dict:
if loss is not None:
return (loss,) + decoder_outputs + encoder_outputs
else:
return decoder_outputs + encoder_outputs
return SpokenNormOutput(
loss=loss,
logits=decoder_outputs.logits,
logits_spoken_tagging=spoken_tagging_output,
past_key_values=decoder_outputs.past_key_values,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
cross_attentions=decoder_outputs.cross_attentions,
encoder_last_hidden_state=encoder_outputs.last_hidden_state,
encoder_hidden_states=encoder_outputs.hidden_states,
encoder_attentions=encoder_outputs.attentions,
)
class DecoderSpokenNorm(RobertaForCausalLM):
config_class = DecoderSpokenNormConfig
# Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->Roberta
def __init__(self, config):
super().__init__(config)
self.dense_query_copy = torch.nn.Linear(config.hidden_size, config.hidden_size)
self.mem_no_entry = Parameter(torch.randn(config.hidden_size).unsqueeze(0))
self.bias_attention_layer = MultiHeadAttention(config.hidden_size)
self.copy_attention_layer = MultiHeadAttention(config.hidden_size)
def forward_bias_attention(self, query, values, values_mask):
"""
:param query: batch * output_steps * hidden_state
:param values: batch * output_steps * max_bias_steps * hidden_state
:param values_mask: batch * output_steps * max_bias_steps
:return: batch * output_steps * hidden_state
"""
batch, output_steps, hidden_state = query.size()
_, _, max_bias_steps, _ = values.size()
query = query.view(batch * output_steps, 1, hidden_state)
values = values.view(-1, max_bias_steps, hidden_state)
values_mask = 1 - values_mask.view(-1, max_bias_steps)
result_attention, attention_score = self.bias_attention_layer(query=query,
key=values,
value=values,
mask=values_mask.bool())
result_attention = result_attention.squeeze(1).view(batch, output_steps, hidden_state)
return result_attention
def forward_copy_attention(self, query, values, values_mask):
"""
:param query: batch * output_steps * hidden_state
:param values: batch * max_encoder_steps * hidden_state
:param values_mask: batch * output_steps * max_encoder_steps
:return: batch * output_steps * hidden_state
"""
dot_attn_score = torch.bmm(query, values.transpose(2, 1))
attn_mask = (1 - values_mask.clone().unsqueeze(1)).bool()
dot_attn_score.masked_fill_(attn_mask, -float('inf'))
dot_attn_score = torch.softmax(dot_attn_score, dim=-1)
result_attention = torch.bmm(dot_attn_score, values)
return result_attention
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
encoder_bias_pooling=None,
encoder_bias_hidden_states=None,
bias_attention_mask=None,
inputs_embeds=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
labels=None,
labels_bias=None,
past_key_values=None,
use_cache=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
if labels is not None:
use_cache = False
# attention with input encoded
outputs = self.roberta(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
# Query for bias
sequence_output = outputs[0]
bias_indicate_output = None
# output copy attention
query_copy = torch.relu(self.dense_query_copy(sequence_output))
sequence_atten_copy_output = self.forward_copy_attention(query_copy,
encoder_hidden_states,
encoder_attention_mask)
if encoder_bias_pooling is not None:
# Make bias features
encoder_bias_pooling = torch.cat([self.mem_no_entry, encoder_bias_pooling], dim=0)
mem_no_entry_feature = torch.zeros_like(encoder_bias_hidden_states[0]).unsqueeze(0)
mem_no_entry_mask = torch.ones_like(bias_attention_mask[0]).unsqueeze(0)
encoder_bias_hidden_states = torch.cat([mem_no_entry_feature, encoder_bias_hidden_states], dim=0)
bias_attention_mask = torch.cat([mem_no_entry_mask, bias_attention_mask], dim=0)
# Compute ranking score
b, s, h = sequence_output.size()
bias_ranking_score = sequence_output.view(b * s, h).mm(encoder_bias_pooling.T)
bias_ranking_score = bias_ranking_score.view(b, s, encoder_bias_pooling.size(0))
# teacher force with bias label
if not self.training:
bias_indicate_output = torch.argmax(bias_ranking_score, dim=-1)
else:
if random.random() < 0.5:
bias_indicate_output = labels_bias.clone()
bias_indicate_output[torch.where(bias_indicate_output < 0)] = 0
else:
bias_indicate_output = torch.argmax(bias_ranking_score, dim=-1)
# Bias encoder hidden state
_, max_len, _ = encoder_bias_hidden_states.size()
bias_encoder_hidden_states = torch.index_select(input=encoder_bias_hidden_states,
dim=0,
index=bias_indicate_output.view(b * s)).view(b, s, max_len,
h)
bias_encoder_attention_mask = torch.index_select(input=bias_attention_mask,
dim=0,
index=bias_indicate_output.view(b * s)).view(b, s, max_len)
sequence_atten_bias_output = self.forward_bias_attention(sequence_output,
bias_encoder_hidden_states,
bias_encoder_attention_mask)
# Find output words
prediction_scores = self.lm_head(sequence_output + sequence_atten_bias_output + sequence_atten_copy_output)
else:
prediction_scores = self.lm_head(sequence_output + sequence_atten_copy_output)
# run attention with bias
bias_ranking_loss = None
if labels_bias is not None:
loss_fct = CrossEntropyLoss()
bias_ranking_loss = loss_fct(bias_ranking_score.view(-1, encoder_bias_pooling.size(0)),
labels_bias.view(-1))
if not return_dict:
output = (prediction_scores,) + outputs[2:]
return ((bias_ranking_loss,) + output) if bias_ranking_loss is not None else output
result = CausalLMOutputWithCrossAttentions(
loss=bias_ranking_loss,
logits=prediction_scores,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
cross_attentions=outputs.cross_attentions,
)
result.bias_indicate_output = bias_indicate_output
return result
def download_tokenizer_files():
resources = ['envibert_tokenizer.py', 'dict.txt', 'sentencepiece.bpe.model']
for item in resources:
if not os.path.exists(os.path.join(cache_dir, item)):
tmp_file = hf_bucket_url(model_name, filename=item)
tmp_file = cached_path(tmp_file, cache_dir=cache_dir)
os.rename(tmp_file, os.path.join(cache_dir, item))
def init_tokenizer():
download_tokenizer_files()
tokenizer = SourceFileLoader("envibert.tokenizer",
os.path.join(cache_dir,
'envibert_tokenizer.py')).load_module().RobertaTokenizer(cache_dir)
tokenizer.model_input_names = ["input_ids",
"attention_mask",
"bias_input_ids",
"bias_attention_mask",
"labels"
"labels_bias"]
return tokenizer
def init_model():
download_tokenizer_files()
tokenizer = SourceFileLoader("envibert.tokenizer",
os.path.join(cache_dir,
'envibert_tokenizer.py')).load_module().RobertaTokenizer(cache_dir)
tokenizer.model_input_names = ["input_ids",
"attention_mask",
"bias_input_ids",
"bias_attention_mask",
"labels"
"labels_bias"]
# set encoder decoder tying to True
roberta_shared = EncoderDecoderSpokenNorm.from_encoder_decoder_pretrained(model_name,
model_name,
tie_encoder_decoder=False)
# set special tokens
roberta_shared.config.decoder_start_token_id = tokenizer.bos_token_id
roberta_shared.config.eos_token_id = tokenizer.eos_token_id
roberta_shared.config.pad_token_id = tokenizer.pad_token_id
# sensible parameters for beam search
# set decoding params
roberta_shared.config.max_length = 50
roberta_shared.config.early_stopping = True
roberta_shared.config.no_repeat_ngram_size = 3
roberta_shared.config.length_penalty = 2.0
roberta_shared.config.num_beams = 1
roberta_shared.config.vocab_size = roberta_shared.config.encoder.vocab_size
return roberta_shared, tokenizer
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