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import math
from typing import Optional, Tuple
import torch
import torch.nn as nn
from .transformer_components import PositionalInfo, apply_rotary_emb
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 MultiHeadAttentionBase(nn.Module):
def __init__(
self,
query_dim: int,
output_dim: int,
key_dim: Optional[int] = None,
value_dim: Optional[int] = None,
n_heads: int = 4,
hidden_dim: int = 64,
dropout: float = 0.0,
total_n_heads: Optional[int] = None,
activation: str = "softmax",
use_bias_qkv: bool = False,
use_bias_out: bool = True,
):
super().__init__()
self.query_dim = query_dim
self.key_dim = query_dim if key_dim is None else key_dim
self.value_dim = self.key_dim if value_dim is None else value_dim
self.output_dim = output_dim
self.n_heads = n_heads
self.hidden_dim = hidden_dim
self.total_n_heads = n_heads if total_n_heads is None else total_n_heads
self.activation = activation
self.use_bias_qkv = use_bias_qkv
self.use_bias_out = use_bias_out
if self.total_n_heads <= 0:
raise ValueError(f"total_n_heads must be positive, got {self.total_n_heads}")
if hidden_dim % self.total_n_heads != 0:
raise ValueError(
f"hidden_dim ({hidden_dim}) must be divisible by total_n_heads ({self.total_n_heads})"
)
self.head_dim = hidden_dim // self.total_n_heads
self.scale = 1.0 / math.sqrt(self.head_dim)
projected_dim = n_heads * self.head_dim
self.q_proj = nn.Linear(self.query_dim, projected_dim, bias=use_bias_qkv)
self.k_proj = nn.Linear(self.key_dim, projected_dim, bias=use_bias_qkv)
self.v_proj = nn.Linear(self.value_dim, projected_dim, bias=use_bias_qkv)
self.o_proj = nn.Linear(projected_dim, output_dim, bias=use_bias_out)
self.dropout = nn.Dropout(dropout)
self.attn_dropout = nn.Dropout(dropout)
self.last_attn_weights = None
self._init_weights()
def _init_weights(self) -> None:
gain = 1.0 / math.sqrt(2.0)
for projection in (self.q_proj, self.k_proj, self.v_proj, self.o_proj):
nn.init.xavier_uniform_(projection.weight, gain=gain)
if projection.bias is not None:
nn.init.zeros_(projection.bias)
def _reshape_for_multihead(self, x: torch.Tensor, batch_size: int, seq_len: int) -> torch.Tensor:
x = x.view(batch_size, seq_len, self.n_heads, self.head_dim)
return x.transpose(1, 2)
def _process_mask(self, mask: Optional[torch.Tensor]) -> Optional[torch.Tensor]:
if mask is None:
return None
if mask.dim() == 2:
if mask.shape[0] != mask.shape[1]:
raise ValueError(
"2D decoder attention masks must be square [seq, seq]; "
f"got {tuple(mask.shape)}. Expand padding masks at the LM boundary."
)
return mask.bool().unsqueeze(0).unsqueeze(0)
if mask.dim() == 3:
return mask.bool().unsqueeze(1)
raise ValueError(f"Mask must be 2D or 3D, got {mask.dim()}D")
def _compute_attn_scores(
self,
q: torch.Tensor,
k: torch.Tensor,
pos_info: Optional[PositionalInfo] = None,
) -> torch.Tensor:
if pos_info is not None and pos_info.rope_freqs is not None:
freqs_cos, freqs_sin = pos_info.rope_freqs
q, k = apply_rotary_emb(q, k, freqs_cos, freqs_sin)
return torch.matmul(q, k.transpose(-2, -1)) * self.scale
def _apply_activation_and_mask(self, scores: torch.Tensor, mask: Optional[torch.Tensor]) -> torch.Tensor:
processed_mask = self._process_mask(mask)
if processed_mask is not None and self.activation in {"softmax", "sigmoid", "tanh"}:
scores.masked_fill_(~processed_mask, torch.finfo(scores.dtype).min)
weights = _activate_scores(scores, self.activation)
if processed_mask is not None:
weights = weights.masked_fill(~processed_mask, 0.0)
return weights
def _apply_mask(self, scores: torch.Tensor, mask: Optional[torch.Tensor]) -> torch.Tensor:
return self._apply_activation_and_mask(scores, mask)
def forward(
self,
query: torch.Tensor,
key: Optional[torch.Tensor] = None,
value: Optional[torch.Tensor] = None,
mask: Optional[torch.Tensor] = None,
pos_info: Optional[PositionalInfo] = None,
) -> Tuple[torch.Tensor, torch.Tensor]:
batch_size, seq_len, _ = query.shape
key = query if key is None else key
value = key if value is None else value
key_len = key.shape[1]
if key.shape[0] != batch_size or value.shape[0] != batch_size:
raise ValueError(
f"Batch size mismatch: query={batch_size}, key={key.shape[0]}, value={value.shape[0]}"
)
if key.shape[1] != value.shape[1]:
raise ValueError(
f"Key and value sequence length mismatch: {key.shape[1]} vs {value.shape[1]}"
)
q = self.q_proj(query)
k = self.k_proj(key)
v = self.v_proj(value)
q = self._reshape_for_multihead(q, batch_size, seq_len)
k = self._reshape_for_multihead(k, batch_size, key_len)
v = self._reshape_for_multihead(v, batch_size, key_len)
attn_scores = self._compute_attn_scores(q, k, pos_info)
attn_weights = self._apply_activation_and_mask(attn_scores, mask)
attn_weights = self.attn_dropout(attn_weights)
attn_output = torch.matmul(attn_weights, v)
attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.view(batch_size, seq_len, self.n_heads * self.head_dim)
attn_output = self.o_proj(attn_output)
attn_output = self.dropout(attn_output)
self.last_attn_weights = attn_weights.detach()
return attn_output, attn_weights
class SelfAttention(MultiHeadAttentionBase):
def __init__(
self,
input_dim: int,
n_heads: int = 4,
hidden_dim: int = 64,
dropout: float = 0.0,
supports_relative: bool = False,
total_n_heads: Optional[int] = None,
use_bias_qkv: bool = False,
use_bias_out: bool = True,
):
head_dim = hidden_dim // (n_heads if total_n_heads is None else total_n_heads)
super().__init__(
query_dim=input_dim,
output_dim=n_heads * head_dim,
key_dim=input_dim,
value_dim=input_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.supports_relative = supports_relative
if self.supports_relative:
self.rel_k_proj = nn.Linear(self.head_dim, self.head_dim, bias=False)
self.rel_v_proj = nn.Linear(self.head_dim, self.head_dim, bias=False)
nn.init.xavier_uniform_(self.rel_k_proj.weight)
nn.init.xavier_uniform_(self.rel_v_proj.weight)
def forward(
self,
x: torch.Tensor,
mask: Optional[torch.Tensor] = None,
pos_info: Optional[PositionalInfo] = None,
) -> Tuple[torch.Tensor, torch.Tensor]:
if self.supports_relative and pos_info is not None and pos_info.rel_embeddings is not None:
batch_size, seq_len, _ = x.shape
rel_embeddings = pos_info.rel_embeddings
if rel_embeddings.shape[:2] != (seq_len, seq_len):
raise ValueError(
f"Relative embeddings shape {rel_embeddings.shape} does not match sequence length {seq_len}"
)
if rel_embeddings.shape[2] != self.head_dim:
raise ValueError(
f"Relative embeddings dim {rel_embeddings.shape[2]} does not match head_dim {self.head_dim}"
)
rel_k = self.rel_k_proj(rel_embeddings)
rel_v = self.rel_v_proj(rel_embeddings)
q = self.q_proj(x)
k = self.k_proj(x)
v = self.v_proj(x)
q = self._reshape_for_multihead(q, batch_size, seq_len)
k = self._reshape_for_multihead(k, batch_size, seq_len)
v = self._reshape_for_multihead(v, batch_size, seq_len)
attn_scores = self._compute_attn_scores(q, k, pos_info)
rel_scores = torch.einsum("bhid,ijd->bhij", q, rel_k) * self.scale
attn_scores = attn_scores + rel_scores
attn_weights = self._apply_mask(attn_scores, mask)
attn_weights = self.attn_dropout(attn_weights)
attn_output = torch.matmul(attn_weights, v)
rel_output = torch.einsum("bhij,ijd->bhid", attn_weights, rel_v)
attn_output = attn_output + rel_output
attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.view(batch_size, seq_len, self.n_heads * self.head_dim)
attn_output = self.o_proj(attn_output)
attn_output = self.dropout(attn_output)
self.last_attn_weights = attn_weights.detach()
return attn_output, attn_weights
return super().forward(query=x, mask=mask, pos_info=pos_info)
class CrossAttention(MultiHeadAttentionBase):
def __init__(
self,
input_dim: int,
output_dim: int,
context_dim: Optional[int] = None,
n_heads: int = 8,
hidden_dim: int = 64,
dropout: float = 0.0,
supports_relative: bool = False,
use_bias_qkv: bool = False,
use_bias_out: bool = True,
):
resolved_context_dim = input_dim if context_dim is None else context_dim
super().__init__(
query_dim=input_dim,
key_dim=resolved_context_dim,
value_dim=resolved_context_dim,
output_dim=output_dim,
n_heads=n_heads,
hidden_dim=hidden_dim,
dropout=dropout,
activation="softmax",
use_bias_qkv=use_bias_qkv,
use_bias_out=use_bias_out,
)
self.supports_relative = supports_relative
if supports_relative:
self.rel_k_proj = nn.Linear(self.head_dim, self.head_dim, bias=False)
self.rel_v_proj = nn.Linear(self.head_dim, self.head_dim, bias=False)
nn.init.xavier_uniform_(self.rel_k_proj.weight)
nn.init.xavier_uniform_(self.rel_v_proj.weight)
def forward(
self,
inputs: torch.Tensor,
context: torch.Tensor,
mask: Optional[torch.Tensor] = None,
pos_info: Optional[PositionalInfo] = None,
) -> Tuple[torch.Tensor, torch.Tensor]:
if self.supports_relative and pos_info is not None and pos_info.rel_embeddings is not None:
batch_size, seq_len_q, _ = inputs.shape
_, seq_len_k, _ = context.shape
rel_embeddings = pos_info.rel_embeddings
if rel_embeddings.shape[:2] != (seq_len_q, seq_len_k):
raise ValueError(
f"Relative embeddings shape {rel_embeddings.shape} does not match "
f"sequence lengths {(seq_len_q, seq_len_k)}"
)
if rel_embeddings.shape[2] != self.head_dim:
raise ValueError(
f"Relative embeddings dim {rel_embeddings.shape[2]} does not match head_dim {self.head_dim}"
)
rel_k = self.rel_k_proj(rel_embeddings)
rel_v = self.rel_v_proj(rel_embeddings)
q = self._reshape_for_multihead(self.q_proj(inputs), batch_size, seq_len_q)
k = self._reshape_for_multihead(self.k_proj(context), batch_size, seq_len_k)
v = self._reshape_for_multihead(self.v_proj(context), batch_size, seq_len_k)
attn_scores = self._compute_attn_scores(q, k, pos_info)
attn_scores = attn_scores + torch.einsum("bhid,ijd->bhij", q, rel_k) * self.scale
attn_weights = self._apply_activation_and_mask(attn_scores, mask)
attn_weights = self.attn_dropout(attn_weights)
self.last_attn_weights = attn_weights.detach()
attn_output = torch.matmul(attn_weights, v)
attn_output = attn_output + torch.einsum("bhij,ijd->bhid", attn_weights, rel_v)
attn_output = attn_output.transpose(1, 2).contiguous().view(batch_size, seq_len_q, -1)
attn_output = self.o_proj(attn_output)
attn_output = self.dropout(attn_output)
return attn_output, attn_weights
return super().forward(
query=inputs,
key=context,
value=context,
mask=mask,
pos_info=pos_info,
)
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