Spaces:
Runtime error
Runtime error
File size: 11,560 Bytes
fc5ecba 9d196bb fc5ecba |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 |
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils.rnn import pad_sequence, pad_packed_sequence, pack_padded_sequence
from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline, set_seed, GPT2Tokenizer, GPT2Model, GPT2LMHeadModel, GPT2Config, GPT2ForSequenceClassification, GPT2LMHeadModel, MarianTokenizer
from fudge.constants import *
from fudge.util import pad_mask
from fudge.clickbait_classifier import BertClickbaitClassifier, ClickbaitConfig
class Model(nn.Module):
def __init__(self, args, gpt_pad_id, vocab_size, rhyme_group_size=None, glove_embeddings=None, verbose=True):
super(Model, self).__init__()
# self.topic = args.task == 'topic'
self.formality = args.task == 'formality'
self.iambic = args.task == 'iambic'
self.rhyme = args.task == 'rhyme'
self.newline = args.task == 'newline'
self.clickbait = args.task == 'clickbait'
# if self.topic:
# self.gpt_embed = nn.Embedding(gpt_pad_id + 1, HIDDEN_DIM, padding_idx=gpt_pad_id) # these are subwords, not words
# if glove_embeddings is None:
# if verbose:
# print('initializing word embeddings from scratch')
# self.word_embed = nn.Embedding(vocab_size, GLOVE_DIM, padding_idx=0)
# else:
# if verbose:
# print('initializing word embeddings from glove')
# self.word_embed = nn.Embedding.from_pretrained(glove_embeddings, padding_idx=0)
# self.rnn = nn.LSTM(HIDDEN_DIM, RNN_DIM, num_layers=3, bidirectional=True)
# self.attention_linear = nn.Linear(HIDDEN_DIM, HIDDEN_DIM)
# large_hidden_dim = HIDDEN_DIM
# self.embed_key_linear = nn.Linear(large_hidden_dim, HIDDEN_DIM)
# self.attention_value_linear = nn.Linear(HIDDEN_DIM, HIDDEN_DIM)
# self.out_embed_linear = nn.Linear(HIDDEN_DIM, HIDDEN_DIM)
# self.out_linear = nn.Linear(HIDDEN_DIM, HIDDEN_DIM)
# self.out_linear2 = nn.Linear(HIDDEN_DIM + large_hidden_dim, HIDDEN_DIM)
# self.out_linear3 = nn.Linear(HIDDEN_DIM, 1)
# self.nonlinear = nn.ReLU()
# elif self.formality:
if self.formality:
self.marian_embed = nn.Embedding(gpt_pad_id + 1, HIDDEN_DIM, padding_idx=0) # 0 in marian is ''
self.rnn = nn.LSTM(HIDDEN_DIM, HIDDEN_DIM, num_layers=3, bidirectional=False, dropout=0.5) # want it to be causal so we can learn all positions
self.out_linear = nn.Linear(HIDDEN_DIM, 1)
elif self.iambic:
self.gpt_embed = nn.Embedding(gpt_pad_id + 1, HIDDEN_DIM, padding_idx=gpt_pad_id)
self.rnn = nn.LSTM(HIDDEN_DIM, HIDDEN_DIM, num_layers=3, bidirectional=False, dropout=0) # want it to be causal so we can learn all positions
self.out_linear = nn.Linear(HIDDEN_DIM, 1)
elif self.rhyme:
self.gpt_embed = nn.Embedding(gpt_pad_id + 1, HIDDEN_DIM, padding_idx=gpt_pad_id) # these are subwords, not words
self.word_embed = nn.Embedding(rhyme_group_size+1, GLOVE_DIM, padding_idx=0) # this embedding for future words will actually embed the rhyme group idx
self.rnn = nn.LSTM(HIDDEN_DIM, RNN_DIM, num_layers=3, bidirectional=True)
self.attention_linear = nn.Linear(HIDDEN_DIM, HIDDEN_DIM)
large_hidden_dim = HIDDEN_DIM + COUNT_SYLLABLE_DIM
self.embed_key_linear = nn.Linear(large_hidden_dim, HIDDEN_DIM)
self.attention_value_linear = nn.Linear(HIDDEN_DIM, HIDDEN_DIM)
self.out_embed_linear = nn.Linear(HIDDEN_DIM, HIDDEN_DIM)
self.out_linear = nn.Linear(HIDDEN_DIM, HIDDEN_DIM)
self.out_linear2 = nn.Linear(HIDDEN_DIM + large_hidden_dim, HIDDEN_DIM)
self.out_linear3 = nn.Linear(HIDDEN_DIM, 1)
self.count_syllable_embed = nn.Embedding(MAX_COUNT_SYLLABLE_DIST+1, COUNT_SYLLABLE_DIM)
self.nonlinear = nn.ReLU()
elif self.newline:
self.gpt_embed = nn.Embedding(gpt_pad_id + 1, HIDDEN_DIM, padding_idx=gpt_pad_id) # these are subwords, not words
self.rnn = nn.LSTM(HIDDEN_DIM, HIDDEN_DIM, num_layers=3, bidirectional=False)
self.count_syllable_embed = nn.Embedding(MAX_COUNT_SYLLABLE_DIST+1, COUNT_SYLLABLE_DIM)
self.out_linear = nn.Linear(HIDDEN_DIM + COUNT_SYLLABLE_DIM, HIDDEN_DIM)
self.out_linear2 = nn.Linear(HIDDEN_DIM, HIDDEN_DIM)
self.out_linear3 = nn.Linear(HIDDEN_DIM, 1)
self.nonlinear = nn.ReLU()
elif self.clickbait:
# mpnet_config = ClickbaitConfig(
# model_type="mpnet",
# pretrained_model="sentence-transformers/all-mpnet-base-v2",
# num_labels=1,
# dropout=0.2,
# inner_dim1=256,
# inner_dim2=32,
# max_length=25,
# load_pretrained=True,
# freeze_bert=False,
# )
#TODO add a checkpoint to Classifier
# print('add a checkpoint to Classifier')
checkpoint = args.checkpoint #'ckpt/clickbait_classifier/checkpoint-1464'
# self.classifier = BertClickbaitClassifier(config=mpnet_config).to(torch.device(args.device))
self.classifier = BertClickbaitClassifier.from_pretrained(checkpoint).to(torch.device(args.device))
else:
raise NotImplementedError # TODO honestly this can/should be refactored into different models
def forward(self, inputs, lengths=None, future_words=None, log_probs=None, syllables_to_go=None, future_word_num_syllables=None, rhyme_group_index=None, run_classifier=False, attention_mask=None):
"""
inputs: token ids, batch x seq, right-padded with 0s
lengths: lengths of inputs; batch
future_words: batch x N words to check if not predict next token, else batch
log_probs: N
syllables_to_go: batch
"""
# if self.topic:
# inputs = self.gpt_embed(inputs) # batch x seq x 300
# inputs = pack_padded_sequence(inputs.permute(1, 0, 2), lengths.cpu(), enforce_sorted=False)
# rnn_output, _ = self.rnn(inputs)
# rnn_output, _ = pad_packed_sequence(rnn_output)
# rnn_output = rnn_output.permute(1, 0, 2) # batch x seq x 300
# hidden = rnn_output
# attention_mask = pad_mask(lengths).permute(1, 0) # batch x seq
# embed = self.word_embed(future_words) # batch x N x 300
# embed_query = self.embed_key_linear(embed)
# attention_tensor = self.attention_linear(hidden).unsqueeze(2) * embed_query.unsqueeze(1) # batch x seq x N x 300
# attention_weights = F.softmax(attention_tensor.sum(dim=3), dim=1) # batch x seq x N
# attention_weights = attention_weights * attention_mask.unsqueeze(2)
# hidden = self.attention_value_linear(hidden)
# weighted_hidden = (hidden.unsqueeze(2) * attention_weights.unsqueeze(3)).sum(dim=1) # batch x seq x N x 768 -> batch x N x 768
# unnormalized_scores = (self.out_linear(weighted_hidden) * self.out_embed_linear(embed)) # batch x N x 300
# unnormalized_scores = torch.cat([unnormalized_scores, embed], dim=2)
# unnormalized_scores = self.nonlinear(self.out_linear2(self.nonlinear(unnormalized_scores)))
# unnormalized_scores = self.out_linear3(unnormalized_scores)
# scores = unnormalized_scores.squeeze(2) - log_probs.unsqueeze(0)
# return scores # batch x N of normalized scores or batch x
# elif self.formality:
if self.formality:
inputs = self.marian_embed(inputs)
inputs = pack_padded_sequence(inputs.permute(1, 0, 2), lengths.cpu(), enforce_sorted=False)
rnn_output, _ = self.rnn(inputs)
rnn_output, _ = pad_packed_sequence(rnn_output)
rnn_output = rnn_output.permute(1, 0, 2) # batch x seq x 300
return self.out_linear(rnn_output).squeeze(2)
elif self.iambic:
inputs = self.gpt_embed(inputs)
inputs = pack_padded_sequence(inputs.permute(1, 0, 2), lengths.cpu(), enforce_sorted=False)
rnn_output, _ = self.rnn(inputs)
rnn_output, _ = pad_packed_sequence(rnn_output)
rnn_output = rnn_output.permute(1, 0, 2) # batch x seq x 300
return self.out_linear(rnn_output).squeeze(2)
elif self.rhyme:
inputs = self.gpt_embed(inputs) # batch x seq x 300
inputs = pack_padded_sequence(inputs.permute(1, 0, 2), lengths.cpu(), enforce_sorted=False)
rnn_output, _ = self.rnn(inputs)
rnn_output, _ = pad_packed_sequence(rnn_output)
rnn_output = rnn_output.permute(1, 0, 2) # batch x seq x 300
hidden = rnn_output
attention_mask = pad_mask(lengths).permute(1, 0) # batch x seq
embed = self.word_embed(future_words) # batch x N x 300
embedded_syllables_to_go = self.count_syllable_embed(syllables_to_go).unsqueeze(1).expand(-1, embed.shape[1], -1) # batch x N x 100
auxiliary_embed = embedded_syllables_to_go
embed_query = self.embed_key_linear(torch.cat([embed, auxiliary_embed], dim=2))
attention_tensor = self.attention_linear(hidden).unsqueeze(2) * embed_query.unsqueeze(1) # batch x seq x N x 300
attention_weights = F.softmax(attention_tensor.sum(dim=3), dim=1) # batch x seq x N
attention_weights = attention_weights * attention_mask.unsqueeze(2)
hidden = self.attention_value_linear(hidden)
weighted_hidden = (hidden.unsqueeze(2) * attention_weights.unsqueeze(3)).sum(dim=1) # batch x seq x N x 768 -> batch x N x 768
unnormalized_scores = (self.out_linear(weighted_hidden) * self.out_embed_linear(embed)) # batch x N x 300
unnormalized_scores = torch.cat([unnormalized_scores, embed, auxiliary_embed], dim=2)
unnormalized_scores = self.nonlinear(self.out_linear2(self.nonlinear(unnormalized_scores)))
unnormalized_scores = self.out_linear3(unnormalized_scores)
scores = unnormalized_scores.squeeze(2) - log_probs.unsqueeze(0)
return scores # batch x N of normalized scores or batch x
elif self.newline:
inputs = self.gpt_embed(inputs) # batch x seq x 300
inputs = pack_padded_sequence(inputs.permute(1, 0, 2), lengths.cpu(), enforce_sorted=False)
rnn_output, _ = self.rnn(inputs)
rnn_output, _ = pad_packed_sequence(rnn_output)
rnn_output = rnn_output.permute(1, 0, 2) # batch x seq x 300
hidden = torch.cat([rnn_output, self.count_syllable_embed(syllables_to_go).unsqueeze(1).expand(-1, rnn_output.shape[1], -1)], dim=2)
return self.out_linear3(self.nonlinear(self.out_linear2(self.nonlinear(self.out_linear(hidden))))).squeeze(2)
elif self.clickbait:
input_ids = torch.tensor(inputs)
classifer_output = self.classifier(input_ids = input_ids, attention_mask = attention_mask).logits
classifer_output = classifer_output[None,:,:] # batch x seq x 300
# return self.out_linear(rnn_output).squeeze(2)
return classifer_output.squeeze(2)
else:
raise NotImplementedError
|