P5_beauty_base / modeling_p5.py
makitanikaze's picture
Upload modeling_p5.py
8fce2bb
from dataclasses import dataclass
from transformers.models.t5.modeling_t5 import (
T5Stack, T5Block, T5LayerNorm, T5LayerSelfAttention, T5LayerFF, T5LayerCrossAttention,
T5PreTrainedModel, T5ForConditionalGeneration
)
import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple
import copy
from transformers.modeling_outputs import ModelOutput, BaseModelOutput, BaseModelOutputWithPast, BaseModelOutputWithPastAndCrossAttentions, Seq2SeqLMOutput, Seq2SeqModelOutput
from transformers.modeling_utils import PreTrainedModel, find_pruneable_heads_and_indices, prune_linear_layer
from transformers.utils import logging
from transformers import BeamScorer, BeamSearchScorer
logger = logging.get_logger(__name__)
# The encoder for input token sequence
class JointEncoder(T5Stack):
def __init__(self, config, embed_tokens=None):
super(T5Stack, self).__init__(config)
self.config = config
self.embed_tokens = embed_tokens
self.is_decoder = self.config.is_decoder
assert self.config.is_decoder is False
self.block = nn.ModuleList(
[T5Block(config, has_relative_attention_bias=(i == 0))
for i in range(config.num_layers)]
)
self.final_layer_norm = T5LayerNorm(
config.d_model, eps=config.layer_norm_epsilon)
self.dropout = nn.Dropout(config.dropout_rate)
## Set maximum 512 whole words in a source text
self.whole_word_embeddings = nn.Embedding(
512, config.d_model ## config.d_model is 768 for base
)
self.init_weights()
self.model_parallel = False
self.device_map = None
def set_input_embeddings(self, new_embeddings):
self.embed_tokens = new_embeddings
def forward(
self,
input_ids=None,
whole_word_ids=None,
attention_mask=None,
inputs_embeds=None,
head_mask=None,
past_key_values=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
if inputs_embeds is None:
assert self.embed_tokens is not None, "You have to initialize the model with valid token embeddings"
inputs_embeds = self.embed_tokens(input_ids) ### embedding step - add HERE ###
if whole_word_ids is not None:
whole_word_embeds = self.whole_word_embeddings(whole_word_ids)
assert whole_word_embeds.shape[-1] == inputs_embeds.shape[-1]
inputs_embeds = inputs_embeds + whole_word_embeds
B, L = inputs_embeds.size()[:-1]
if attention_mask is None:
attention_mask = input_ids.ne(self.config.pad_token_id).to(dtype=inputs_embeds.dtype, device=inputs_embeds.device)
# ourselves in which case we just need to make it broadcastable to all heads.
extended_attention_mask = self.get_extended_attention_mask(
attention_mask,
(B, L),
inputs_embeds.device)
# initialize past_key_values with `None` if past does not exist
if past_key_values is None:
past_key_values = [None] * len(self.block)
# Prepare head mask if needed
head_mask = self.get_head_mask(head_mask, self.config.num_layers)
present_key_value_states = () if use_cache else None
all_hidden_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
all_cross_attentions = () if (output_attentions and self.is_decoder) else None
hidden_states = self.dropout(inputs_embeds)
if self.config.num_layers > 0:
assert self.block[0].layer[0].SelfAttention.has_relative_attention_bias
seq_length = L
q_len = seq_length
k_len = seq_length
# [1, n_heads, Q_len, K_len]
text_position_bias = self.block[0].layer[0].SelfAttention.compute_bias(
L, L)
num_heads = text_position_bias.size(1)
position_bias = text_position_bias.new_zeros(
1, num_heads, seq_length, seq_length)
position_bias[:, :, :L, :L] = text_position_bias
position_bias = position_bias + extended_attention_mask
for i, (layer_module, past_key_value) in enumerate(zip(self.block, past_key_values)):
layer_head_mask = head_mask[i]
layer_outputs = layer_module(
hidden_states,
attention_mask=extended_attention_mask,
position_bias=position_bias,
encoder_hidden_states=None,
encoder_attention_mask=None,
encoder_decoder_position_bias=None,
# head_mask=head_mask[i],
layer_head_mask=layer_head_mask,
past_key_value=past_key_value,
use_cache=use_cache,
output_attentions=output_attentions,
)
# layer_outputs is a tuple with:
# hidden-states, key-value-states, (self-attention weights), (self-attention position bias), (cross-attention weights), (cross-attention position bias)
hidden_states, present_key_value_state = layer_outputs[:2]
# We share the position biases between the layers - the first layer store them
# layer_outputs = hidden-states, key-value-states (self-attention weights),
# (self-attention position bias), (cross-attention weights), (cross-attention position bias)
# position_bias = layer_outputs[2]
# append next layer key value states
if use_cache:
present_key_value_states = present_key_value_states + \
(present_key_value_state,)
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.dropout(hidden_states)
# 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,
present_key_value_states,
all_hidden_states,
all_attentions,
all_cross_attentions,
]
if v is not None
)
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=present_key_value_states,
hidden_states=all_hidden_states,
attentions=all_attentions,
cross_attentions=all_cross_attentions,
)
class P5(T5ForConditionalGeneration):
_keys_to_ignore_on_load_missing = [
r"encoder\.embed_tokens\.weight",
r"decoder\.embed_tokens\.weight",
r"lm_head\.weight",
]
_keys_to_ignore_on_load_unexpected = [
r"decoder\.block\.0\.layer\.1\.EncDecAttention\.relative_attention_bias\.weight",
]
def __init__(self, config):
super(T5ForConditionalGeneration, self).__init__(config)
self.config = config
self.model_dim = config.d_model
self.shared = nn.Embedding(config.vocab_size, config.d_model)
encoder_config = copy.deepcopy(config)
encoder_config.is_decoder = False
encoder_config.use_cache = False
encoder_config.is_encoder_decoder = False
self.encoder = JointEncoder(encoder_config, self.shared)
decoder_config = copy.deepcopy(config)
decoder_config.is_decoder = True
decoder_config.is_encoder_decoder = False
self.decoder = T5Stack(decoder_config, self.shared)
self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)
self.init_weights()
self.model_parallel = False
self.device_map = None
def set_input_embeddings(self, new_embeddings):
self.shared = new_embeddings
self.encoder.set_input_embeddings(new_embeddings)
self.decoder.set_input_embeddings(new_embeddings)
def extend_vocab(self, vocab_size):
new_shared = nn.Embedding(vocab_size, self.config.d_model)
old_weight = self.shared.weight.data.detach().clone()
old_vocab_size = old_weight.size(0)
new_shared.weight.data[:old_vocab_size, :] = old_weight
self.shared = new_shared
new_lm_head = nn.Linear(self.config.d_model, vocab_size, bias=False)
old_weight = self.lm_head.weight.data.detach().clone()
old_vocab_size = old_weight.size(0)
new_lm_head.weight.data[:old_vocab_size, :] = old_weight
self.lm_head = new_lm_head
self.encoder.embed_tokens = self.shared
self.decoder.embed_tokens = self.shared
self.lm_head.weight = self.shared.weight
self.config.vocab_size = vocab_size
self.encoder.config.vocab_size = vocab_size
self.decoder.config.vocab_size = vocab_size
def forward(
self,
input_ids=None,
whole_word_ids=None,
attention_mask=None,
encoder_outputs=None,
decoder_input_ids=None,
decoder_attention_mask=None,
past_key_values=None,
use_cache=None,
labels=None,
inputs_embeds=None,
decoder_inputs_embeds=None,
head_mask=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
reduce_loss=False,
return_hidden_state=False,
**kwargs,
):
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.use_return_dict
if encoder_outputs is None:
encoder_outputs = self.encoder(
input_ids=input_ids,
whole_word_ids=whole_word_ids,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
encoder_outputs = BaseModelOutput(
last_hidden_state=encoder_outputs[0],
hidden_states=encoder_outputs[1] if len(
encoder_outputs) > 1 else None,
attentions=encoder_outputs[2] if len(
encoder_outputs) > 2 else None,
)
hidden_states = encoder_outputs[0]
if labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None:
# get decoder inputs from shifting lm labels to the right
decoder_input_ids = self._shift_right(labels)
# If decoding with past key value states, only the last tokens
# should be given as an input
if past_key_values is not None:
assert 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:]
if attention_mask is None:
attention_mask = input_ids.ne(self.config.pad_token_id).to(dtype=hidden_states.dtype, device=hidden_states.device)
encoder_attention_mask = attention_mask
# Decode
decoder_outputs = self.decoder(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
inputs_embeds=decoder_inputs_embeds,
past_key_values=past_key_values,
encoder_hidden_states=hidden_states,
encoder_attention_mask=encoder_attention_mask,
head_mask=head_mask,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = decoder_outputs[0]
assert self.config.tie_word_embeddings is True
if self.config.tie_word_embeddings:
sequence_output = sequence_output * (self.model_dim ** -0.5)
if return_hidden_state:
return sequence_output
lm_logits = self.lm_head(sequence_output)
loss = None
if labels is not None:
if reduce_loss:
loss_fct = CrossEntropyLoss(ignore_index=-100)
else:
loss_fct = CrossEntropyLoss(ignore_index=-100, reduction='none')
loss = loss_fct(
lm_logits.view(-1, lm_logits.size(-1)),
labels.view(-1))
return P5Seq2SeqLMOutput(
loss=loss,
logits=lm_logits,
past_key_values=decoder_outputs.past_key_values,
decoder_last_hidden_state=decoder_outputs.last_hidden_state,
decoder_hidden_states=decoder_outputs.hidden_states,
)
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:]
output = {
"decoder_input_ids": input_ids,
"past_key_values": past,
"encoder_outputs": encoder_outputs,
"attention_mask": attention_mask,
"use_cache": use_cache,
}
return output
@staticmethod
def _expand_inputs_for_generation(
input_ids: torch.LongTensor,
expand_size: int = 1,
is_encoder_decoder: bool = False,
attention_mask: torch.LongTensor = None,
encoder_outputs: ModelOutput = None,
**model_kwargs
) -> Tuple[torch.LongTensor, Dict[str, Any]]:
expanded_return_idx = (
torch.arange(input_ids.shape[0]).view(-1, 1).repeat(1,
expand_size).view(-1).to(input_ids.device)
)
input_ids = input_ids.index_select(0, expanded_return_idx)
if "token_type_ids" in model_kwargs:
token_type_ids = model_kwargs["token_type_ids"]
model_kwargs["token_type_ids"] = token_type_ids.index_select(
0, expanded_return_idx)
if attention_mask is not None:
model_kwargs["attention_mask"] = attention_mask.index_select(
0, expanded_return_idx)
if is_encoder_decoder:
assert encoder_outputs is not None
encoder_outputs["last_hidden_state"] = encoder_outputs.last_hidden_state.index_select(
0, expanded_return_idx
)
model_kwargs["encoder_outputs"] = encoder_outputs
return input_ids, model_kwargs
@dataclass
class P5Seq2SeqLMOutput(ModelOutput):
"""
Base class for sequence-to-sequence language models outputs.
Args:
loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
Languaged modeling loss.
logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
past_key_values (:obj:`List[torch.FloatTensor]`, `optional`, returned when ``use_cache=True`` is passed or when ``config.use_cache=True``):
List of :obj:`torch.FloatTensor` of length :obj:`config.n_layers`, with each tensor of shape
:obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`).
Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see ``past_key_values`` input) to speed up sequential decoding.
decoder_hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
decoder_attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
:obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
encoder_last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
encoder_attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
:obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
past_key_values: Optional[List[torch.FloatTensor]] = None
decoder_last_hidden_state: Optional[Tuple[torch.FloatTensor]] = None
decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
decoder_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