Spaces:
Runtime error
Runtime error
File size: 13,874 Bytes
c5ca37a |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 |
import math
import torch
import torch.nn as nn
from .utils import log_sum_exp
import pdb
import sys
sys.path.append('../../')
from pytorch_transformers.modeling_bert import BertEmbeddings
import torch.nn.functional as F
class ARAE(nn.Module):
def __init__(self, encoder, decoder, tokenizer_encoder, tokenizer_decoder, args): #
super(ARAE, self).__init__()
self.encoder = encoder
self.decoder = decoder
self.tokenizer_encoder = tokenizer_encoder
self.tokenizer_decoder = tokenizer_decoder
self.args = args
self.nz = args.latent_size
self.bos_token_id_list = self.tokenizer_decoder.encode(self.tokenizer_decoder.bos_token)
self.pad_token_id = self.tokenizer_decoder.encode(self.tokenizer_decoder.pad_token)[0]
# connector: from Bert hidden units to the latent space
self.linear = nn.Linear(encoder.config.hidden_size, self.nz, bias=False)
# # Standard Normal prior
# loc = torch.zeros(self.nz, device=args.device)
# scale = torch.ones(self.nz, device=args.device)
# self.prior = torch.distributions.normal.Normal(loc, scale)
self.label_embedding = nn.Embedding(args.label_size, self.nz, padding_idx=0) # use the same size as latent_z so as to use the same decoder.linear()
self.latent_generator = nn.Linear(self.nz, self.nz)
self.latent_classifier = nn.Linear(self.nz, args.label_size if args.label_size > 2 else 1)
self.latent_discriminator = nn.Linear(self.nz, 1)
self.gpt_embeddings = nn.Embedding(self.decoder.config.vocab_size, self.decoder.config.n_embd)
self.gpt_embeddings.weight.data = decoder.transformer.wte.weight.data
self.conv1 = nn.Conv1d(self.encoder.config.hidden_size, self.encoder.config.hidden_size, 3)
self.classifier = nn.Linear(self.encoder.config.hidden_size, 1 if args.label_size <= 2 else args.label_size)
self.CrossEntropyLoss = torch.nn.CrossEntropyLoss()
self.BCEWithLogitsLoss = torch.nn.BCEWithLogitsLoss()
def forward(self, input_seq_ids, tgt_seq_ids, cond_labels, attention_mask=None):
# inputs: (B, seq_len)
# labels: (B, seq_len)
# cond_labels: (B), conditional labels.
ones_label = torch.ones_like(cond_labels).to(dtype=torch.float32)
zeros_label = torch.zeros_like(cond_labels).to(dtype=torch.float32)
random_noise = torch.nn.init.normal_(torch.empty(input_seq_ids.size(0), self.nz)).to(device=input_seq_ids.device, dtype=torch.float32)
# Encode inputs
outputs = self.encoder(input_seq_ids, attention_mask=attention_mask)
pooled_hidden_fea = outputs[1] # (B, dim_h)
# Encode z
latent_z = self.linear(pooled_hidden_fea) # (B, nz)
# Generate z
gen_z = self.latent_generator(random_noise) # (B, nz)
# Latent discriminator
prob_encode_z_dis = self.latent_discriminator(latent_z).squeeze(1).float() # (B)
prob_gen_z_dis = self.latent_discriminator(gen_z).squeeze(1).float() # (B)
# Train latent discriminator
loss_lsd = self.BCEWithLogitsLoss(prob_gen_z_dis, zeros_label) + self.BCEWithLogitsLoss(prob_encode_z_dis, ones_label)
acc_encode_z_dis = ((prob_encode_z_dis >= 0).float() == ones_label).float()
acc_gen_z_dis = ((prob_gen_z_dis >= 0).float() == zeros_label).float()
# Train sampler adversarially
loss_lsg = self.BCEWithLogitsLoss(prob_gen_z_dis, ones_label)
# Latent classifier
prob_encode_z_cls = self.latent_classifier(latent_z) # (B, n_labels)
if self.args.label_size <= 2:
prob_encode_z_cls = prob_encode_z_cls.squeeze(1) # (B)
# Train latent classifier
loss_lsc = self.BCEWithLogitsLoss(prob_encode_z_cls, cond_labels.float())
acc_encode_z_cls = ((prob_encode_z_cls >= 0).float() == cond_labels.float()).float()
# Train encoder adversarially
loss_encoder = 1 - self.BCEWithLogitsLoss(prob_encode_z_cls, cond_labels.float())
else:
# Train latent classifier
loss_lsc = self.CrossEntropyLoss(prob_encode_z_cls, cond_labels)
acc_encode_z_cls = (torch.argmax(prob_encode_z_cls, dim=-1) == cond_labels).float()
# Train encoder adversarially
loss_encoder = 1 - self.CrossEntropyLoss(prob_encode_z_cls, cond_labels)
# Embed labels
label_emb = self.label_embedding(cond_labels) # (B, hidden_size)
past_label = self.decoder.linear(label_emb) # (B, n_blocks * hidden_size) # todo: use the same linear layer for latent_z for now.
if self.args.label_size <= 2:
sampled_cond_labels = 1 - cond_labels
else:
raise NotImplementedError # todo: currently only implemented for binary labels. need to change for multi-class labels.
sampled_label_emb = self.label_embedding(sampled_cond_labels) # (B, hidden_size)
past_sampled_label = self.decoder.linear(sampled_label_emb) # (B, n_blocks * hidden_size) # todo: use the same linear layer for latent_z for now.
# Generate based on encoded z and gt labels. (reconstruction)
past_z = self.decoder.linear(latent_z) # (B, n_blocks * hidden_size)
gen_past_z = self.decoder.linear(gen_z) # (B, n_blocks * hidden_size)
past = torch.cat([past_z.unsqueeze(1), past_label.unsqueeze(1)], dim=1) # (B, 2, n_blocks * hidden_size)
outputs = self.decoder(input_ids=tgt_seq_ids, past=past, labels=tgt_seq_ids, label_ignore=self.pad_token_id)
loss_rec = outputs[0]
# Train a classifier in the observation space
tgt_emb = self.gpt_embeddings(tgt_seq_ids)
tgt_encode = self.conv1(tgt_emb.transpose(1, 2)) # (B, dim_h, seq_len)
tgt_encode = torch.mean(tgt_encode, dim=-1) # (B, dim_h)
prob_cls = self.classifier(tgt_encode) # (B, n_labels)
if self.args.label_size <= 2:
prob_cls = prob_cls.squeeze(1)
loss_cls = self.BCEWithLogitsLoss(prob_cls, cond_labels.float())
pred_cls = (prob_cls >= 0).to(dtype=torch.long)
else:
loss_cls = self.CrossEntropyLoss(prob_cls, cond_labels)
pred_cls = torch.argmax(prob_cls, dim=-1)
acc_cls = (pred_cls == cond_labels).float()
# Loss
loss = loss_rec + loss_encoder + loss_lsc + loss_lsd + loss_lsg + loss_cls
if not self.training:
# Generate based on encoded z and gt labels
generated = self.sample_sequence_conditional_batch(past=past, context=self.bos_token_id_list)
# Generate based on encoded z and sampled labels (attribute transfer)
at_past = torch.cat([past_z.unsqueeze(1), past_sampled_label.unsqueeze(1)], dim=1) # (B, 2, n_blocks * hidden_size)
at_generated = self.sample_sequence_conditional_batch(past=at_past, context=self.bos_token_id_list) # (B, seq_len)
# Generate based on sampled z and sampled labels. (conditional generation)
cg_past = torch.cat([gen_past_z.unsqueeze(1), past_sampled_label.unsqueeze(1)], dim=1) # (B, 2, n_blocks * hidden_size)
cg_generated = self.sample_sequence_conditional_batch(past=cg_past, context=self.bos_token_id_list) # (B, seq_len)
# classifier on gt generated sentences.
ge_emb = self.gpt_embeddings(generated)
ge_encode = self.conv1(ge_emb.transpose(1, 2)) # (B, dim_h, seq_len)
ge_encode = torch.mean(ge_encode, dim=-1) # (B, dim_h)
prob_ge_cls = self.classifier(ge_encode) # (B, 1)
if self.args.label_size <= 2:
pred_ge_cls = (prob_ge_cls.squeeze(1) >= 0).to(torch.long)
else:
pred_ge_cls = torch.argmax(prob_ge_cls, dim=-1)
acc_ge_cls = (pred_ge_cls == cond_labels).float()
# classifier on attribute transfer generated sentences.
at_emb = self.gpt_embeddings(at_generated)
at_encode = self.conv1(at_emb.transpose(1, 2)) # (B, dim_h, seq_len)
at_encode = torch.mean(at_encode, dim=-1) # (B, dim_h)
prob_at_cls = self.classifier(at_encode) # (B, 1)
if self.args.label_size <= 2:
pred_at_cls = (prob_at_cls.squeeze(1) >= 0).to(torch.long)
else:
pred_at_cls = torch.argmax(prob_at_cls, dim=-1)
acc_at_cls = (pred_at_cls == sampled_cond_labels).float()
# classifier on conditional generated sentences.
cg_emb = self.gpt_embeddings(cg_generated)
cg_encode = self.conv1(cg_emb.transpose(1, 2)) # (B, dim_h, seq_len)
cg_encode = torch.mean(cg_encode, dim=-1) # (B, dim_h)
prob_cg_cls = self.classifier(cg_encode) # (B, 1)
if self.args.label_size <= 2:
pred_cg_cls = (prob_cg_cls.squeeze(1) >= 0).to(torch.long)
else:
pred_cg_cls = torch.argmax(prob_cg_cls, dim=-1)
acc_cg_cls = (pred_cg_cls == sampled_cond_labels).float()
result = {
'sampled_cond_labels': sampled_cond_labels,
'cond_labels': cond_labels,
'tgt_seq_ids': tgt_seq_ids,
'generated': generated,
'at_generated': at_generated,
'cg_generated': cg_generated,
'acc_encode_z_dis': acc_encode_z_dis,
'acc_gen_z_dis': acc_gen_z_dis,
'acc_encode_z_cls': acc_encode_z_cls,
'acc_cls': acc_cls,
'acc_ge_cls': acc_ge_cls,
'acc_at_cls': acc_at_cls,
'acc_cg_cls': acc_cg_cls,
'pred_cls': pred_cls,
'pred_ge_cls': pred_ge_cls,
'pred_at_cls': pred_at_cls,
'pred_cg_cls': pred_cg_cls,
}
return result
loss_dict = {
'loss': loss,
'loss_rec': loss_rec,
'loss_encoder': loss_encoder,
'loss_lsc': loss_lsc,
'loss_lsd': loss_lsd,
'loss_lsg': loss_lsg,
'loss_cls': loss_cls,
}
acc_dict = {
'acc_encode_z_dis': acc_encode_z_dis,
'acc_gen_z_dis': acc_gen_z_dis,
'acc_encode_z_cls': acc_encode_z_cls,
'acc_cls': acc_cls,
}
return loss_dict, acc_dict
def sample_sequence_conditional_batch(self, past, context):
# context: a single id of <BOS>
# past: (B, past_seq_len dim_h)
num_samples = past.size(0)
context = torch.tensor(context, dtype=torch.long, device=past.device)
context = context.unsqueeze(0).repeat(num_samples, 1)
generated = context # (B, 1)
# with torch.no_grad():
while generated.size(-1) < self.args.block_size:
inputs = {'input_ids': generated, 'past': past}
outputs = self.decoder(**inputs) # Note: we could also use 'past' with GPT-2/Transfo-XL/XLNet (cached hidden-states)
lm_logits = outputs[0]
next_tokens_logits = lm_logits[:, -1, :] / self.args.temperature # (B, 1, vocab_size)
filtered_logits = self.top_k_top_p_filtering_batch(next_tokens_logits, top_k=self.args.top_k, top_p=self.args.top_p) # (B, vocab_size)
filtered_logits = F.softmax(filtered_logits, dim=-1)
next_tokens = torch.multinomial(filtered_logits, num_samples=1) # (B, 1)
generated = torch.cat((generated, next_tokens), dim=1) # (B, seq_len+1)
not_finished = next_tokens != self.tokenizer_decoder.encode('<EOS>')[0]
if torch.sum(not_finished) == 0:
break
return generated # (B, seq_len)
def top_k_top_p_filtering_batch(self, logits, top_k=0, top_p=0.0, filter_value=-float('Inf')):
""" Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
Args:
logits: logits distribution shape (vocabulary size)
top_k > 0: keep only top k tokens with highest probability (top-k filtering).
top_p > 0.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).
Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317
"""
# assert logits.dim() == 1 # batch size 1 for now - could be updated for more but the code would be less clear
top_k = min(top_k, logits.size(-1)) # Safety check
if top_k > 0:
# Remove all tokens with a probability less than the last token of the top-k
threshold = torch.topk(logits, top_k, dim=-1)[0][:, -1, None]
logits.masked_fill_(logits < threshold, filter_value) # (B, vocab_size)
if top_p > 0.0:
sorted_logits, sorted_indices = torch.sort(logits, descending=True) # (B, vocab_size)
cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1) # (B, vocab_size)
# Remove tokens with cumulative probability above the threshold
sorted_indices_to_remove = cumulative_probs > top_p
# Shift the indices to the right to keep also the first token above the threshold
sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
sorted_indices_to_remove[..., 0] = 0
indices_to_remove = sorted_indices[sorted_indices_to_remove]
logits.masked_fill_(indices_to_remove, filter_value)
return logits
|