File size: 13,442 Bytes
b19206a |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 |
from transformers import PreTrainedModel, PretrainedConfig, Wav2Vec2ForCTC
import json
import torch
from torch import nn
from torch.nn.utils.rnn import pad_sequence
import math
from typing import Optional
# x: torch.FloatTensor [T, B, D]
# mask: torch.BoolTensor [B, T], where True indicates padding
# returns: torch.LongTensor [B]
def get_lengths(x, mask=None):
if mask is not None:
return (~mask).long().sum(dim=1)
else:
return torch.LongTensor([x.size(0)] * x.size(1)).to(x.device)
# lens: torch.LongTensor [B]
# returns: torch.BoolTensor [B, max_lens], where True indicates padding
def lengths_to_padding_mask(lens):
bsz, max_lens = lens.size(0), torch.max(lens).item()
mask = torch.arange(max_lens).to(lens.device).view(1, max_lens)
mask = mask.expand(bsz, -1) >= lens.view(bsz, 1).expand(-1, max_lens)
return mask
# input_lengths: torch.LongTensor [B]
def get_output_lengths(input_lengths):
conv_feature_layers = "[(512, 10, 5)] + [(512, 3, 2)] * 4 + [(512,2,2)] + [(512,2,2)]"
conv_cfg_list = eval(conv_feature_layers)
def _conv_out_length(input_length, kernel_size, stride):
return torch.floor((input_length - kernel_size) / stride + 1)
for i in range(len(conv_cfg_list)):
input_lengths = _conv_out_length(
input_lengths, conv_cfg_list[i][1], conv_cfg_list[i][2]
)
return input_lengths.to(torch.long)
class ZeroSwotEncoderConfig(PretrainedConfig):
model_type = "zero_swot_encoder"
def __init__(
self,
wav2vec2_model_name_or_path="",
compression_adapter=None,
embed_dim=1024,
**kwargs
):
super().__init__(**kwargs)
self.wav2vec2_model_name_or_path = wav2vec2_model_name_or_path
self.compression_adapter = compression_adapter
self.embed_dim = embed_dim
@classmethod
def from_json_file(cls, json_file):
with open(json_file, "r") as reader:
text = reader.read()
config_dict = json.loads(text)
return cls(**config_dict)
class ZeroSwotEncoderModel(PreTrainedModel):
config_class = ZeroSwotEncoderConfig
model_type = "zero_swot_encoder"
def __init__(self, config):
super().__init__(config)
self.wav2vec2 = Wav2Vec2ForCTC.from_pretrained(config.wav2vec2_model_name_or_path)
self.compression_adapter = CompressionAdapter(config.compression_adapter)
self.speech_embedder = SpeechEmbedder(config.embed_dim)
def forward(self, input_values, attention_mask=None):
input_lens = get_lengths(input_values, ~attention_mask)
# Forward pass through wav2vec2 encoder
x = self.wav2vec2.wav2vec2(input_values, attention_mask)[0] # [B, T, D]
# CTC predictions
preds = self.wav2vec2.lm_head(x).argmax(-1) # [B, T]
# Get output lengths for x
output_lens = get_output_lengths(input_lens)
# Compression
x, mask, _ = self.compression_adapter(x, preds, output_lens) # [B, N, D] with N << T
# BOS and EOS embeddings
x, mask = self.speech_embedder(x, mask) # [B, N+2, D]
return x, ~mask
class SpeechEmbedder(nn.Module):
def __init__(self, embed_dim):
super().__init__()
self.embed_dim = embed_dim
self.bos_emb = nn.Parameter(torch.empty(embed_dim))
self.eos_emb = nn.Parameter(torch.empty(embed_dim))
self.scale = self.embed_dim ** 0.5
def forward(self, x, padding_mask=None):
"""Add special embedding and positional embedding.
Args:
x (FloatTensor): (B, T, C)
padding_mask (ByteTensor): (B, T)
Outputs:
x (FloatTensor): (B, T+2, C)
padding_mask (ByteTensor): (B, T+2)
"""
B = x.size(0)
lengths = get_lengths(x.transpose(0, 1), padding_mask)
assert B == len(lengths)
if padding_mask is not None:
x = x * (1 - padding_mask.unsqueeze(-1).type_as(x))
# prepend bos
x = torch.cat([self.bos_emb.view(1, 1, -1).expand(B, 1, -1), x], dim=1)
lengths += 1
# append padding (zeros) and then convert first padding to eos
x = torch.cat([x, torch.zeros(B, 1, x.size(-1), device=x.device, dtype=x.dtype)], dim=1)
for i in range(B):
x[i, lengths[i], :] = self.eos_emb
lengths += 1
padding_mask = lengths_to_padding_mask(lengths)
x = x * self.scale
return x, padding_mask
class PositionalEmbedding(nn.Module):
def __init__(self, num_embeddings, embedding_dim, padding_idx):
super().__init__()
self.embedding_dim = embedding_dim
self.padding_idx = padding_idx if padding_idx is not None else 0
num_embeddings += padding_idx + 1
self.weights = PositionalEmbedding.get_embedding(
num_embeddings, embedding_dim, padding_idx
)
self.register_buffer("_float_tensor", torch.FloatTensor(1))
self.max_positions = int(1e5)
@staticmethod
def get_embedding(
num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None
):
half_dim = embedding_dim // 2
emb = math.log(10000) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, dtype=torch.float) * -emb)
emb = torch.arange(num_embeddings, dtype=torch.float).unsqueeze(1) * emb.unsqueeze(0)
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(num_embeddings, -1)
if embedding_dim % 2 == 1:
# zero pad
emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1)
if padding_idx is not None:
emb[padding_idx, :] = 0
return emb
def make_positions(self, x, padding_idx: int):
mask = x.ne(padding_idx).int()
return (torch.cumsum(mask, dim=1).type_as(mask) * mask).long() + padding_idx
def forward(self, input):
"""Input is expected to be of size [bsz x seqlen]."""
bsz, seq_len = input.size()
max_pos = self.padding_idx + 1 + seq_len
if self.weights is None or max_pos > self.weights.size(0):
# recompute/expand embeddings if needed
self.weights = PositionalEmbedding.get_embedding(
max_pos, self.embedding_dim, self.padding_idx
)
self.weights = self.weights.to(self._float_tensor)
positions = self.make_positions(input, self.padding_idx)
return (
self.weights.index_select(0, positions.view(-1))
.view(bsz, seq_len, -1)
.detach()
)
class CLSPooling(nn.Module):
def __init__(self, embed_dim, num_transformer_layers, dropout_rate):
super().__init__()
self.cls_token = nn.Parameter(torch.empty(1, 1, embed_dim))
nn.init.normal_(self.cls_token, mean=0.0, std=0.25)
self.transformer = nn.TransformerEncoder(
nn.TransformerEncoderLayer(
embed_dim,
nhead=16 if embed_dim == 1024 else 8,
dim_feedforward=4*embed_dim,
dropout=dropout_rate,
activation="relu",
batch_first=True,
norm_first=True
),
num_layers=num_transformer_layers,
)
self.pos_emb = PositionalEmbedding(512, embed_dim, 1)
self.scale = math.sqrt(embed_dim)
def forward(self, x, lens):
# x: [B, N, D]
# lens: [B]
# prepend cls token
x = torch.cat(
[
self.cls_token.to(dtype=x.dtype, device=x.device).repeat(x.size(0), 1, 1), # B x 1 x D
x
],
dim=1) # [B, N+1, D]
mask = lengths_to_padding_mask(lens+1)
x = x + self.pos_emb(mask.long()) / self.scale
x = self.transformer(x, src_key_padding_mask=mask) # [B, N+1, D]
x = x[:, 0] # [B, D]
return x
class CompressionAdapter(nn.Module):
def __init__(self, cfg):
super().__init__()
self.embed_dim = cfg["embed_dim"]
self.transformer_layers = cfg["transformer_layers"]
self.dropout = cfg["dropout"]
self.blank_idx = cfg["blank_idx"]
self.sep_idx = cfg["sep_idx"]
self.token_pooling_module = CLSPooling(
self.embed_dim, self.transformer_layers, self.dropout
)
def char_compression(self, x, preds, lens):
# x: B x T x D
# preds: B x T
# lens: B
B, T, D = x.size()
device = x.device
dtype = x.dtype
# zero-out the padding
mask = lengths_to_padding_mask(lens) # B x T
x = x.masked_fill(mask.unsqueeze(-1), 0)
preds = preds.masked_fill(mask, self.blank_idx)
# add a vector of -1 to know where each example ends after flattening the batch
preds = torch.cat([-torch.ones(B, 1, device=device, dtype=torch.long), preds], dim=1).view(-1)
x = torch.cat([torch.zeros(B, 1, D, device=device, dtype=dtype), x], dim=1).view(-1, D)
# get points of consecutive preds
preds, counts = preds.unique_consecutive(return_counts=True)
# split in representations of same chars
x = torch.split(x, counts.tolist())
# remove blanks
valid_mask = preds != self.blank_idx
preds = preds[valid_mask]
counts = counts[valid_mask] # [N]
x = [x_i for x_i, v_i in zip(x, valid_mask) if v_i]
# pack into tensor
x = pad_sequence(x, batch_first=True, padding_value=0)
# char pooling
x = torch.sum(x, dim=1) / counts.to(dtype=x.dtype).unsqueeze(1) # [B, N, D] -> [B, D]
# find split points for retrieving the examples
split_points = (preds == -1).nonzero(as_tuple=True)[0]
split_points = torch.cat([split_points, torch.tensor([len(preds)], device=device)])
split_points = (split_points[1:] - split_points[:-1]).tolist()
# split into examples
x = torch.split(x, split_points)
preds = torch.split(preds, split_points)
lens = torch.tensor([len(x_i) for x_i in x], device=device)
# pack into tensors
x = pad_sequence(x, batch_first=True, padding_value=0)
preds = pad_sequence(preds, batch_first=True, padding_value=self.blank_idx)
# remove the parts we add to identify the bounds for each example
x = x[:, 1:]
preds = preds[:, 1:]
lens -= 1
mask = lengths_to_padding_mask(lens)
# account for empty examples (just a sep token)
empty_examples = lens == 0
num_empty_examples = empty_examples.sum()
if num_empty_examples > 0:
mask[empty_examples, 0] = True
lens[empty_examples] = 1
preds[empty_examples, 0] = self.sep_idx
return x, mask, lens, preds, num_empty_examples
def token_compression(self, x, preds, lens):
# x: B x T x D
# preds: B x T
# lens: B
B, T, D = x.size()
device = x.device
dtype = x.dtype
# new lengths after compression
new_lens = preds.eq(self.sep_idx).sum(dim=1)
# unpad and unpack to list of tensors
preds = [preds[i, :lens[i]] for i in range(B)]
x = [x[i, :lens[i]] for i in range(B)]
# make sure every example ends with a separator
num_examples_without_ending_sep = torch.tensor(0, device=device, dtype=torch.long)
for i in range(B):
if preds[i][-1] != self.sep_idx:
preds[i] = torch.cat([preds[i], torch.tensor([self.sep_idx], device=device, dtype=torch.long)])
x[i] = torch.cat([x[i], torch.zeros(1, D, device=device, dtype=dtype)])
new_lens[i] += 1
num_examples_without_ending_sep += 1
# flatten
preds = torch.cat(preds)
x = torch.cat(x)
# split points according to separators
split_points = preds.eq(self.sep_idx).nonzero(as_tuple=True)[0] + 1
split_points = torch.cat([torch.tensor([0], device=device, dtype=torch.long), split_points])
split_points = (split_points[1:] - split_points[:-1]).tolist()
# re-arrange in 3d [total_num_tokens x max(count) x D]
x = torch.split(x, split_points) # Tuple[2d tensor]
counts = torch.tensor([len(x_i) for x_i in x], device=device, dtype=torch.long)
x = pad_sequence(x, batch_first=True, padding_value=0)
# reduce dim 1
x = self.token_pooling_module(x, counts)
# reconstruct the batch
split_points = new_lens.cumsum(dim=0)
split_points = torch.cat([torch.tensor([0], device=device, dtype=torch.long), split_points])
split_points = (split_points[1:] - split_points[:-1]).tolist()
x = torch.split(x, split_points)
x = pad_sequence(x, batch_first=True, padding_value=0) # B x ? x D
mask = lengths_to_padding_mask(new_lens)
return x, mask, new_lens, num_examples_without_ending_sep
def forward(self, x, preds, lens):
x, mask, lens, preds, _ = self.char_compression(x, preds, lens)
x, mask, lens, _ = self.token_compression(x, preds, lens)
return x, mask, lens |