File size: 9,617 Bytes
ff495b4 |
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 |
#
# For licensing see accompanying LICENSE file.
# Copyright (C) 2024 Apple Inc. All Rights Reserved.
#
import math
from typing import Optional, Sequence
import torch
from torch import Tensor, nn
from typing import Dict
import open_clip
from .mobile_clip_transformer import (
PositionalEmbedding,
TransformerEncoder,
get_normalization_layer,
)
class TextTransformer(nn.Module):
def __init__(self, cfg: dict, projection_dim: int, *args, **kwargs) -> None:
super().__init__()
model_dim = cfg["dim"]
no_scale_embedding = cfg.get("no_scale_embedding", False)
no_pos_embedding = cfg.get("no_pos_embedding", False)
embed_dropout = cfg.get("embed_dropout", 0.0)
norm_layer = cfg["norm_layer"]
variant = cfg["model_name"]
self.vocab_size = cfg["vocab_size"]
self.projection_dim = projection_dim
# Token embedding layer
self.embedding_layer = nn.Embedding(
embedding_dim=model_dim, num_embeddings=self.vocab_size
)
self.embed_scale = 1.0 if no_scale_embedding else model_dim**-0.5
# Context length
context_length = cfg["context_length"]
assert (
context_length is not None
), "Context length can't be None. Please set value accordingly."
self.positional_embedding = (
None
if no_pos_embedding
else PositionalEmbedding(
num_embeddings=context_length, embedding_dim=model_dim
)
)
self.embedding_dropout = nn.Dropout(p=embed_dropout)
# Transformer layer
n_transformer_layers = cfg["n_transformer_layers"]
# FFN multipliers for transformer layer
ffn_multipliers = cfg["ffn_multiplier_per_layer"]
if isinstance(ffn_multipliers, (float, int)):
ffn_multipliers = [ffn_multipliers] * n_transformer_layers
if not isinstance(ffn_multipliers, Sequence):
Warning(
"{} expects FFN multipliers as a list, whose length is the same as"
" number of transformer layers. Got: {}".format(
self.__class__.__name__, type(ffn_multipliers)
)
)
elif (
isinstance(ffn_multipliers, Sequence)
and len(ffn_multipliers) != n_transformer_layers
):
Warning(
"We need FFN multiplier for each transformer layer. Got {} ffn"
" multipliers while number of transformer layers = {}".format(
len(ffn_multipliers), n_transformer_layers
)
)
ffn_dims = [
int(math.ceil(model_dim * ffn_mult / 16.0) * 16.0)
for ffn_mult in ffn_multipliers
]
# Heads for transformer layers
mha_heads = cfg["n_heads_per_layer"]
if isinstance(mha_heads, int):
mha_heads = [mha_heads] * n_transformer_layers
if not isinstance(mha_heads, Sequence):
Warning(
"{} expects MHA heads as a list, whose length is the same as number of "
"transformer layers. Got: {}".format(
self.__class__.__name__, type(mha_heads)
)
)
elif isinstance(mha_heads, Sequence) and len(mha_heads) != n_transformer_layers:
Warning(
"{} needs MHA heads for each transformer layer. Got {} mha heads while"
" number of transformer layers = {}".format(
self.__class__.__name__, len(mha_heads), n_transformer_layers
)
)
if variant == "base":
self.transformer = nn.ModuleList(
[
TransformerEncoder(
embed_dim=model_dim,
num_heads=mha_heads[layer_idx],
ffn_latent_dim=ffn_dims[layer_idx],
transformer_norm_layer=norm_layer,
)
for layer_idx in range(n_transformer_layers)
]
)
elif variant == "mct":
raise NotImplementedError
else:
raise ValueError("Unrecognized text encoder variant {}".format(variant))
self.final_layer_norm = get_normalization_layer(
num_features=model_dim, norm_type=norm_layer
)
self.projection_layer = nn.Parameter(
torch.empty(model_dim, self.projection_dim)
)
self.model_dim = model_dim
self.causal_masking = cfg["causal_masking"]
def forward_embedding(self, text_tokens: Tensor) -> Tensor:
"""Return text embedding for all tokens.
Args:
text_tokens: a tensor of token indices. Shape: [batch_size, context_length]
Returns:
A tensor of [batch_size, context_length, hidden_dim].
"""
# [batch_size, context_length] --> [batch_size, context_length, hidden_dim]
token_emb = self.embedding_layer(text_tokens)
seq_len = token_emb.shape[1]
if self.positional_embedding is not None:
token_emb = token_emb + self.positional_embedding(seq_len).to(
token_emb.dtype
)
token_emb = self.embedding_dropout(token_emb)
return token_emb
def build_attention_mask(self, context_length: int, batch_size: int) -> Tensor:
"""Build causal attention mask [batch_size, context_length, context_length]."""
# Build mask with full attention between the tokens
# pytorch uses additive attention mask; fill with -inf
mask = torch.empty(context_length, context_length)
mask.fill_(float("-inf"))
mask.triu_(1) # zero out the lower diagonal
mask = mask.unsqueeze(0) # add dummy batch dimension
mask = mask.expand(batch_size, -1, -1)
return mask
def encode_text(
self,
text_tokens: Tensor,
key_padding_mask: Optional[Tensor] = None,
return_all_tokens: bool = False,
*args,
**kwargs
) -> Tensor:
"""Return text token embeddings.
Args:
text_tokens: a tensor of token indices. Shape: [batch_size, context_length]
key_padding_mask: a tensor of boolean values as the padding mask.
Shape: [batch_size, context_length]
return_all_tokens: a boolean flag to return all tokens, defaults to False
to return only EOT token embedding.
Returns:
A tensor of [batch_size, context_length, hidden_dim] if return_all_tokens is
True, otherwise a tensor of [batch_size, hidden_dim].
"""
# Discrete tokens to continuous embeddings
# [batch_size, context_length] --> [batch_size, context_length, hidden_dim]
token_emb = self.forward_embedding(text_tokens)
# [1, context_length, context_length]
attn_mask = None
if self.causal_masking:
attn_mask = self.build_attention_mask(
context_length=text_tokens.shape[1], batch_size=text_tokens.shape[0]
)
attn_mask = attn_mask.to(device=token_emb.device, dtype=token_emb.dtype)
key_padding_mask = None
for layer in self.transformer:
token_emb = layer(
token_emb,
key_padding_mask=key_padding_mask,
attn_mask=attn_mask,
)
# Apply layer norm
token_emb = self.final_layer_norm(token_emb)
if return_all_tokens:
return token_emb
# Take features from the eot embedding (eot_token is the highest number in each sequence)
token_emb = token_emb[
torch.arange(text_tokens.shape[0]), text_tokens.argmax(dim=-1)
]
token_emb = token_emb @ self.projection_layer
return token_emb
def forward(
self,
text_tokens: Tensor,
key_padding_mask: Optional[Tensor] = None,
return_all_tokens: bool = False,
*args,
**kwargs
) -> Tensor:
# Image-text pair data with single caption
# [B, CL] --> [B, d]
text_tokens = self.encode_text(
text_tokens=text_tokens,
key_padding_mask=key_padding_mask,
return_all_tokens=return_all_tokens,
*args,
**kwargs
)
return text_tokens
class ClipTokenizer(nn.Module):
def __init__(self, cfg, *args, **kwargs):
super().__init__()
self.context_length = cfg["text_cfg"]["context_length"]
model_name = getattr(cfg["text_cfg"], "open_clip_tokenizer", "ViT-B-16")
self.tokenizer = open_clip.get_tokenizer(model_name)
def get_vocab_size(self) -> int:
return len(self.tokenizer.encoder)
def get_encodings(self) -> Dict[str, int]:
return self.tokenizer.encoder
def get_eot_token(self) -> int:
# Tokenizing an empty string returns a list [sot_id, eot_id]
return self.tokenizer("")[1]
def get_sot_token(self) -> int:
# Tokenizing an empty string returns a list [sot_id, eot_id]
return self.tokenizer("")[0]
def forward(self, input_sentence: str, *args, **kwargs) -> Tensor:
# tokenizer returns indices as a string
tokenized_sentence = self.tokenizer(input_sentence, self.context_length)
assert (
tokenized_sentence.shape[-1] == self.context_length
), "Tokenized tensor should be exactly `context_length` long."
return tokenized_sentence
|