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b64c0a4 b23b9d6 b64c0a4 4fdb5a5 b64c0a4 b23b9d6 b64c0a4 b23b9d6 b64c0a4 | 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 | """
BERTose transformer layers.
Transformer blocks adapted for WURCS glycan tokenization.
"""
import torch
import torch.nn as nn
import math
class GlycanBERTConfig:
"""Configuration for the BERTose transformer stack."""
def __init__(
self,
vocab_size: int = 102,
hidden_size: int = 384,
num_hidden_layers: int = 6,
num_attention_heads: int = 6,
intermediate_size: int = 1536,
hidden_dropout_prob: float = 0.1,
attention_probs_dropout_prob: float = 0.1,
max_position_embeddings: int = 512,
layer_norm_eps: float = 1e-12,
pad_token_id: int = 0,
mask_token_id: int = 4,
initializer_range: float = 0.02
):
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.layer_norm_eps = layer_norm_eps
self.pad_token_id = pad_token_id
self.mask_token_id = mask_token_id
self.initializer_range = initializer_range
class GlycanBERTEmbeddings(nn.Module):
"""
Embeddings for glycan tokens including token and positional embeddings.
"""
def __init__(self, config: GlycanBERTConfig):
super().__init__()
self.token_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
# position_ids (1, max_seq_len) is contiguous in memory and exported when serialized
self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
def forward(self, input_ids: torch.Tensor) -> torch.Tensor:
"""
Args:
input_ids: Tensor of shape (batch_size, seq_len)
Returns:
Embeddings of shape (batch_size, seq_len, hidden_size)
"""
batch_size, seq_len = input_ids.shape
# Token embeddings
token_embeds = self.token_embeddings(input_ids)
# Position embeddings
position_ids = self.position_ids[:, :seq_len]
position_embeds = self.position_embeddings(position_ids)
# Combine
embeddings = token_embeds + position_embeds
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
class GlycanBERTAttention(nn.Module):
"""Multi-head self-attention."""
def __init__(self, config: GlycanBERTConfig):
super().__init__()
assert config.hidden_size % config.num_attention_heads == 0
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = config.hidden_size // config.num_attention_heads
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.query = nn.Linear(config.hidden_size, self.all_head_size)
self.key = nn.Linear(config.hidden_size, self.all_head_size)
self.value = nn.Linear(config.hidden_size, self.all_head_size)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
"""Reshape for multi-head attention."""
new_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(*new_shape)
return x.permute(0, 2, 1, 3) # (batch, heads, seq_len, head_size)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor = None
) -> torch.Tensor:
"""
Args:
hidden_states: (batch_size, seq_len, hidden_size)
attention_mask: (batch_size, seq_len) - 1 for valid, 0 for padding
Returns:
Attention output: (batch_size, seq_len, hidden_size)
"""
batch_size, seq_len, _ = hidden_states.shape
# Linear projections
query_layer = self.transpose_for_scores(self.query(hidden_states))
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
# Attention scores
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
# Apply attention mask
if attention_mask is not None:
# Convert mask to additive mask
attention_mask = attention_mask.unsqueeze(1).unsqueeze(2) # (batch, 1, 1, seq_len)
attention_mask = (1.0 - attention_mask) * -10000.0
attention_scores = attention_scores + attention_mask
# Attention probabilities
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
attention_probs = self.dropout(attention_probs)
# Apply attention to values
context_layer = torch.matmul(attention_probs, value_layer)
# Reshape back
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(*new_shape)
return context_layer
class GlycanBERTLayer(nn.Module):
"""Single transformer layer."""
def __init__(self, config: GlycanBERTConfig):
super().__init__()
self.attention = GlycanBERTAttention(config)
self.attention_output = nn.Linear(config.hidden_size, config.hidden_size)
self.attention_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.intermediate = nn.Linear(config.hidden_size, config.intermediate_size)
self.output = nn.Linear(config.intermediate_size, config.hidden_size)
self.output_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor = None
) -> torch.Tensor:
"""
Args:
hidden_states: (batch_size, seq_len, hidden_size)
attention_mask: (batch_size, seq_len)
Returns:
Output: (batch_size, seq_len, hidden_size)
"""
# Self-attention
attention_output = self.attention(hidden_states, attention_mask)
attention_output = self.attention_output(attention_output)
attention_output = self.dropout(attention_output)
# Add & Norm
hidden_states = self.attention_layer_norm(hidden_states + attention_output)
# Feed-forward
intermediate_output = self.intermediate(hidden_states)
intermediate_output = nn.functional.gelu(intermediate_output)
layer_output = self.output(intermediate_output)
layer_output = self.dropout(layer_output)
# Add & Norm
layer_output = self.output_layer_norm(hidden_states + layer_output)
return layer_output
class GlycanBERT(nn.Module):
"""
BERTose transformer stack for masked language modeling.
"""
def __init__(self, config: GlycanBERTConfig):
super().__init__()
self.config = config
# Embeddings
self.embeddings = GlycanBERTEmbeddings(config)
# Transformer layers
self.layers = nn.ModuleList([GlycanBERTLayer(config) for _ in range(config.num_hidden_layers)])
# MLM head
self.mlm_head = nn.Linear(config.hidden_size, config.vocab_size)
# Initialize weights
self.apply(self._init_weights)
def _init_weights(self, module):
"""Initialize weights."""
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
def forward(
self,
input_ids: torch.Tensor,
attention_mask: torch.Tensor = None,
labels: torch.Tensor = None
):
"""
Args:
input_ids: (batch_size, seq_len)
attention_mask: (batch_size, seq_len) - 1 for valid, 0 for padding
labels: (batch_size, seq_len) - token IDs to predict, -100 for positions to ignore
Returns:
If labels provided: (loss, logits)
Else: logits
"""
# Create attention mask if not provided
if attention_mask is None:
attention_mask = (input_ids != self.config.pad_token_id).float()
# Embeddings
hidden_states = self.embeddings(input_ids)
# Transformer layers
for layer in self.layers:
hidden_states = layer(hidden_states, attention_mask)
# MLM prediction
logits = self.mlm_head(hidden_states)
# Calculate loss if labels provided
loss = None
if labels is not None:
loss_fct = nn.CrossEntropyLoss() # -100 is ignored
loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1))
if loss is not None:
return loss, logits
return logits
def get_embeddings(
self,
input_ids: torch.Tensor,
attention_mask: torch.Tensor = None
) -> torch.Tensor:
"""
Get contextualized embeddings (for downstream tasks).
Args:
input_ids: (batch_size, seq_len)
attention_mask: (batch_size, seq_len)
Returns:
Embeddings: (batch_size, seq_len, hidden_size)
"""
if attention_mask is None:
attention_mask = (input_ids != self.config.pad_token_id).float()
hidden_states = self.embeddings(input_ids)
for layer in self.layers:
hidden_states = layer(hidden_states, attention_mask)
return hidden_states
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