File size: 12,546 Bytes
1b7bd7b | 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 | """Dual-attention primitives for decoder language models."""
import math
from typing import Optional
import torch
import torch.nn as nn
from .transformer_components import PositionalInfo
from .transformer_core import MultiHeadAttentionBase
def _activate_scores(scores: torch.Tensor, activation: str) -> torch.Tensor:
if activation == "softmax":
return torch.softmax(scores, dim=-1)
if activation == "identity":
return scores
if activation == "relu":
return torch.relu(scores)
if activation == "tanh":
return torch.tanh(scores)
if activation == "sigmoid":
return torch.sigmoid(scores)
if activation == "gelu":
return torch.nn.functional.gelu(scores)
raise ValueError(f"Unsupported attention activation: {activation}")
class RelationalAttentionBase(MultiHeadAttentionBase):
def __init__(
self,
hidden_dim: int,
symbol_dim: int,
n_heads: int,
total_n_heads: int,
dropout: float = 0.0,
n_relations: Optional[int] = None,
rel_activation: str = "identity",
symmetric_rels: bool = False,
use_relative_positional_symbols: bool = False,
use_bias_qkv: bool = False,
use_bias_out: bool = True,
):
head_dim = hidden_dim // total_n_heads
output_dim = n_heads * head_dim
super().__init__(
query_dim=hidden_dim,
output_dim=output_dim,
key_dim=hidden_dim,
value_dim=symbol_dim,
n_heads=n_heads,
hidden_dim=hidden_dim,
dropout=dropout,
total_n_heads=total_n_heads,
activation="softmax",
use_bias_qkv=use_bias_qkv,
use_bias_out=use_bias_out,
)
self.symbol_dim = symbol_dim
self.rel_activation = rel_activation
self.symmetric_rels = symmetric_rels
self.use_relative_positional_symbols = use_relative_positional_symbols
self.n_relations = n_heads if n_relations is None else n_relations
total_rel_dim = self.head_dim * n_heads
if total_rel_dim % self.n_relations != 0:
raise ValueError(
f"head_dim * n_heads ({total_rel_dim}) must be divisible by n_relations "
f"({self.n_relations})"
)
self.rel_proj_dim = total_rel_dim // self.n_relations
self.rel_scale = 1.0 / math.sqrt(self.rel_proj_dim)
rel_total_dim = self.n_relations * self.rel_proj_dim
self.wq_rel = nn.Linear(hidden_dim, rel_total_dim, bias=False)
self.wk_rel = self.wq_rel if symmetric_rels else nn.Linear(
hidden_dim,
rel_total_dim,
bias=False,
)
nn.init.xavier_uniform_(self.wq_rel.weight)
if self.wk_rel is not self.wq_rel:
nn.init.xavier_uniform_(self.wk_rel.weight)
def _compute_base_attention(
self,
x: torch.Tensor,
mask: Optional[torch.Tensor],
pos_info: Optional[PositionalInfo],
) -> torch.Tensor:
batch_size, seq_len, _ = x.shape
q = self._reshape_for_multihead(self.q_proj(x), batch_size, seq_len)
k = self._reshape_for_multihead(self.k_proj(x), batch_size, seq_len)
scores = self._compute_attn_scores(q, k, pos_info)
return self._apply_activation_and_mask(scores, mask)
def compute_relational_scores(
self,
x: torch.Tensor,
mask: Optional[torch.Tensor],
return_as: str,
) -> torch.Tensor:
batch_size, seq_len, _ = x.shape
q_rel = self.wq_rel(x).view(batch_size, seq_len, self.n_relations, self.rel_proj_dim)
k_rel = self.wk_rel(x).view(batch_size, seq_len, self.n_relations, self.rel_proj_dim)
q_rel = q_rel.transpose(1, 2)
k_rel = k_rel.transpose(1, 2)
relations = torch.matmul(q_rel, k_rel.transpose(-2, -1)) * self.rel_scale
processed_mask = self._process_mask(mask)
if self.rel_activation == "softmax" and processed_mask is not None:
relations.masked_fill_(~processed_mask, torch.finfo(relations.dtype).min)
relations = _activate_scores(relations, self.rel_activation)
if processed_mask is not None:
relations = relations.masked_fill(~processed_mask, 0.0)
if return_as == "vectors":
return relations.permute(0, 2, 3, 1)
if return_as == "scores":
return relations
raise ValueError(f"return_as must be 'vectors' or 'scores', got {return_as}")
def combine_attention_and_relations(
self,
attn_weights: torch.Tensor,
relation_scores: torch.Tensor,
) -> torch.Tensor:
return attn_weights * relation_scores
def _process_symbols_with_attention(
self,
symbols: torch.Tensor,
attn_weights: torch.Tensor,
) -> torch.Tensor:
batch_size, _, seq_len_q, seq_len_k = attn_weights.shape
values = self.v_proj(symbols)
if self.use_relative_positional_symbols:
values = values.view(seq_len_q, seq_len_k, self.n_heads, self.head_dim)
return torch.einsum("bhij,ijhd->bihd", attn_weights, values)
values = values.view(batch_size, seq_len_k, self.n_heads, self.head_dim)
values = values.transpose(1, 2)
output = torch.matmul(attn_weights, values)
return output.transpose(1, 2)
def _apply_output_projection(self, output: torch.Tensor) -> torch.Tensor:
batch_size, seq_len = output.shape[:2]
output = output.contiguous().view(batch_size, seq_len, self.output_dim)
output = self.o_proj(output)
output = self.dropout(output)
return output
def _validate_symbols(self, x: torch.Tensor, symbols: torch.Tensor) -> None:
if self.use_relative_positional_symbols:
seq_len = x.shape[1]
expected_shape = (seq_len, seq_len, self.symbol_dim)
if tuple(symbols.shape) != expected_shape:
raise ValueError(
f"Relative symbols must have shape {expected_shape}, got {tuple(symbols.shape)}"
)
class RelationalAttention(RelationalAttentionBase):
def __init__(
self,
hidden_dim: int,
symbol_dim: int,
n_heads: int,
total_n_heads: int,
n_relations: int,
dropout: float = 0.0,
rel_activation: str = "identity",
symmetric_rels: bool = False,
use_relative_positional_symbols: bool = False,
use_bias_qkv: bool = False,
use_bias_out: bool = True,
):
super().__init__(
hidden_dim=hidden_dim,
symbol_dim=symbol_dim,
n_heads=n_heads,
total_n_heads=total_n_heads,
dropout=dropout,
n_relations=n_relations,
rel_activation=rel_activation,
symmetric_rels=symmetric_rels,
use_relative_positional_symbols=use_relative_positional_symbols,
use_bias_qkv=use_bias_qkv,
use_bias_out=use_bias_out,
)
self.wr_proj = nn.Parameter(torch.empty(n_heads, self.head_dim, n_relations))
nn.init.xavier_uniform_(self.wr_proj)
def forward(
self,
x: torch.Tensor,
symbols: torch.Tensor,
mask: Optional[torch.Tensor],
pos_info: Optional[PositionalInfo],
) -> tuple[torch.Tensor, dict[str, torch.Tensor]]:
self._validate_symbols(x, symbols)
attn_weights = self._compute_base_attention(x, mask, pos_info)
attn_weights = self.attn_dropout(attn_weights)
relation_vectors = self.compute_relational_scores(x, mask, return_as="vectors")
attended_symbols = self._process_symbols_with_attention(symbols, attn_weights)
projected_relations = torch.einsum(
"bhij,bijr,hdr->bihd",
attn_weights,
relation_vectors,
self.wr_proj,
)
output = self._apply_output_projection(attended_symbols + projected_relations)
self.last_attn_weights = attn_weights.detach()
return output, {"attention": attn_weights, "relations": relation_vectors}
class RelationalCrossAttention(MultiHeadAttentionBase):
def __init__(
self,
hidden_dim: int,
symbol_dim: int,
n_heads: int,
total_n_heads: int,
dropout: float = 0.0,
activation: str = "identity",
use_relative_positional_symbols: bool = False,
use_bias_qkv: bool = False,
use_bias_out: bool = True,
):
head_dim = hidden_dim // total_n_heads
super().__init__(
query_dim=hidden_dim,
output_dim=n_heads * head_dim,
key_dim=hidden_dim,
value_dim=symbol_dim,
n_heads=n_heads,
hidden_dim=hidden_dim,
dropout=dropout,
total_n_heads=total_n_heads,
activation=activation,
use_bias_qkv=use_bias_qkv,
use_bias_out=use_bias_out,
)
self.symbol_dim = symbol_dim
self.use_relative_positional_symbols = use_relative_positional_symbols
def forward(
self,
x: torch.Tensor,
symbols: torch.Tensor,
mask: Optional[torch.Tensor],
pos_info: Optional[PositionalInfo],
) -> tuple[torch.Tensor, dict[str, torch.Tensor]]:
if self.use_relative_positional_symbols:
batch_size, seq_len, _ = x.shape
expected_shape = (seq_len, seq_len, self.symbol_dim)
if tuple(symbols.shape) != expected_shape:
raise ValueError(
f"Relative symbols must have shape {expected_shape}, got {tuple(symbols.shape)}"
)
q = self._reshape_for_multihead(self.q_proj(x), batch_size, seq_len)
k = self._reshape_for_multihead(self.k_proj(x), batch_size, seq_len)
values = self.v_proj(symbols).view(seq_len, seq_len, self.n_heads, self.head_dim)
scores = self._compute_attn_scores(q, k, pos_info)
weights = self._apply_activation_and_mask(scores, mask)
weights = self.attn_dropout(weights)
output = torch.einsum("bhij,ijhd->bihd", weights, values)
output = output.contiguous().view(batch_size, seq_len, self.output_dim)
output = self.o_proj(output)
output = self.dropout(output)
self.last_attn_weights = weights.detach()
return output, {"attention": weights}
output, weights = super().forward(
query=x,
key=x,
value=symbols,
mask=mask,
pos_info=pos_info,
)
return output, {"attention": weights}
class DisentangledRelationalCrossAttention(RelationalAttentionBase):
def __init__(
self,
hidden_dim: int,
symbol_dim: int,
n_heads: int,
total_n_heads: int,
dropout: float = 0.0,
rel_activation: str = "identity",
use_relative_positional_symbols: bool = False,
use_bias_qkv: bool = False,
use_bias_out: bool = True,
):
super().__init__(
hidden_dim=hidden_dim,
symbol_dim=symbol_dim,
n_heads=n_heads,
total_n_heads=total_n_heads,
dropout=dropout,
n_relations=None,
rel_activation=rel_activation,
symmetric_rels=False,
use_relative_positional_symbols=use_relative_positional_symbols,
use_bias_qkv=use_bias_qkv,
use_bias_out=use_bias_out,
)
def forward(
self,
x: torch.Tensor,
symbols: torch.Tensor,
mask: Optional[torch.Tensor],
pos_info: Optional[PositionalInfo],
) -> tuple[torch.Tensor, dict[str, torch.Tensor]]:
self._validate_symbols(x, symbols)
attn_weights = self._compute_base_attention(x, mask, pos_info)
relation_scores = self.compute_relational_scores(x, mask, return_as="scores")
combined_weights = self.attn_dropout(
self.combine_attention_and_relations(attn_weights, relation_scores)
)
output = self._process_symbols_with_attention(symbols, combined_weights)
output = self._apply_output_projection(output)
self.last_attn_weights = attn_weights.detach()
return output, {
"attention": attn_weights,
"relations": relation_scores,
"combined": combined_weights,
}
|