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model.py
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model.py
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import copy
import traceback
import torch
from torch import nn
from transformers import T5ForConditionalGeneration
from transformers.modeling_outputs import Seq2SeqLMOutput
class T5Base(T5ForConditionalGeneration):
def __init__(self, config, cfg):
super(T5Base, self).__init__(config)
decoder_config = copy.deepcopy(config)
decoder_config.is_decoder = True
decoder_config.is_encoder_decoder = False
# 暂时注释
# self.resp_decoder = type(self.decoder)(decoder_config, self.shared)
# self.resp_lm_head = type(self.lm_head)(config.d_model, config.vocab_size, bias=False)
self.dropout = nn.Dropout(config.dropout_rate)
def initialize_additional_decoder(self):
decoder_config = copy.deepcopy(self.config)
decoder_config.is_decoder = True
decoder_config.is_encoder_decoder = False
# 暂时注释
# self.resp_decoder = type(self.decoder)(decoder_config, self.shared)
# self.resp_lm_head = type(self.lm_head)(self.config.d_model, self.config.vocab_size, bias=False)
#
# self.resp_decoder.load_state_dict(self.decoder.state_dict())
# self.resp_lm_head.load_state_dict(self.lm_head.state_dict())
def initialize_weights(self, modules):
for module in modules:
if isinstance(module, (nn.Linear, nn.Embedding)):
module.weight.data.normal_(mean=0.0, std=0.02)
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
if isinstance(module, nn.Linear) and module.bias is not None:
module.bias.data.zero_()
def predict_span(self, encoder_hidden_states, attention_mask, span_labels=None):
span_loss, pred_spans, span_logits = 0, None, None
return span_loss, pred_spans, span_logits
def prepare_inputs_for_generation(self, input_ids,
past=None, attention_mask=None,
use_cache=None, encoder_outputs=None,
**kwargs):
if past is not None:
input_ids = input_ids[:, -1:]
return {"decoder_input_ids": input_ids,
"past_key_values": past,
"encoder_outputs": encoder_outputs,
"attention_mask": attention_mask,
"use_cache": use_cache,
"decoder_type": kwargs.get("decoder_type")}
def forward(self,
input_ids=None,
attention_mask=None,
decoder_input_ids=None,
encoder_outputs=None,
past_key_values=None,
inputs_embeds=None,
decoder_inputs_embeds=None,
lm_labels=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
encoder_only=None,
decoder_type=None):
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.return_dict
if encoder_outputs is None:
encoder_outputs = self.encoder(input_ids=input_ids,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
return_dict=return_dict)
if return_dict:
encoder_hidden_states = encoder_outputs.last_hidden_state
else:
encoder_hidden_states = encoder_outputs[0]
else:
if isinstance(encoder_outputs, tuple):
encoder_hidden_states = encoder_outputs[0]
else:
encoder_hidden_states = encoder_outputs.last_hidden_state
if encoder_only:
return encoder_outputs
if lm_labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None:
decoder_input_ids = self._shift_right(lm_labels)
# if decoder_type == "resp":
# decoder = self.resp_decoder
# lm_head = self.resp_lm_head
#
# else:
# decoder = self.decoder
# lm_head = self.lm_head
# 尝试一下如果只用一个decoder的效果如何
decoder = self.decoder
lm_head = self.lm_head
if past_key_values is not None:
assert lm_labels is None, "Decoder should not use cached key value states when training"
if decoder_input_ids is not None:
decoder_input_ids = decoder_input_ids[:, -1:]
if decoder_inputs_embeds is not None:
decoder_inputs_embeds = decoder_inputs_embeds[:, -1:]
decoder_outputs = decoder(input_ids=decoder_input_ids,
inputs_embeds=decoder_inputs_embeds,
past_key_values=past_key_values,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=attention_mask,
use_cache=use_cache,
return_dict=return_dict,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states)
sequence_output = decoder_outputs[0]
sequence_output = sequence_output * (self.model_dim ** -0.5)
lm_logits = lm_head(sequence_output)
lm_loss = None
if lm_labels is not None:
lm_loss_fct = nn.CrossEntropyLoss(ignore_index=0)
lm_loss = lm_loss_fct(
lm_logits.view(-1, lm_logits.size(-1)), lm_labels.view(-1))
# for training
if not return_dict:
pred_lm = torch.argmax(lm_logits, dim=-1)
outputs = (lm_loss, pred_lm, encoder_hidden_states)
# for prediction
else:
outputs = Seq2SeqLMOutput(
loss=lm_loss,
logits=lm_logits,
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[1] if len(
encoder_outputs) > 1 else None,
encoder_attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None)
return outputs
def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int):
"""
Shift input ids one token to the right.
"""
shifted_input_ids = input_ids.new_zeros(input_ids.shape)
shifted_input_ids[:, 1:] = input_ids[:, :-1].clone()
shifted_input_ids[:, 0] = decoder_start_token_id
assert pad_token_id is not None, "self.models.config.pad_token_id has to be defined."
# replace possible -100 values in labels by `pad_token_id`
shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
return shifted_input_ids