1 files changed, 27 insertions, 53 deletions

diff --git a/text_recognizer/networks/transformer/attention.py b/text_recognizer/networks/transformer/attention.py
index fca260d..85f513e 100644
--- a/text_recognizer/networks/transformer/attention.py
+++ b/text_recognizer/networks/transformer/attention.py
@@ -1,5 +1,5 @@
 """Implementes the attention module for the transformer."""
-from typing import Optional, Tuple
+from typing import Optional
 
 import torch
 import torch.nn.functional as F
@@ -8,7 +8,6 @@ from torch import Tensor, einsum, nn
 
 from text_recognizer.networks.transformer.embeddings.rotary import (
     RotaryEmbedding,
-    rotate_half,
 )
 
 
@@ -22,100 +21,75 @@ class Attention(nn.Module):
         causal: bool = False,
         dim_head: int = 64,
         dropout_rate: float = 0.0,
-        rotary_embedding: Optional[RotaryEmbedding] = None,
     ) -> None:
         super().__init__()
-
         self.dim = dim
+        self.scale = self.dim**-0.5
         self.num_heads = num_heads
-        self.causal = causal
         self.dim_head = dim_head
+
+        self.causal = causal
         self.dropout_rate = dropout_rate
-        self.rotary_embedding = rotary_embedding
 
-        self.scale = self.dim**-0.5
-        inner_dim = self.num_heads * self.dim_head
+        # Single key/value head
+        k_dim = dim_head
+        v_dim = dim_head
+
+        out_dim = self.num_heads * self.dim_head
 
-        self.to_q = nn.Linear(self.dim, inner_dim, bias=False)
-        self.to_k = nn.Linear(self.dim, inner_dim, bias=False)
-        self.to_v = nn.Linear(self.dim, inner_dim, bias=False)
+        self.to_q = nn.Linear(self.dim, out_dim, bias=False)
+        self.to_k = nn.Linear(self.dim, k_dim, bias=False)
+        self.to_v = nn.Linear(self.dim, v_dim, bias=False)
 
         self.dropout = nn.Dropout(p=self.dropout_rate)
 
         # Feedforward
-        self.fc = nn.Linear(inner_dim, self.dim)
+        self.fc = nn.Linear(out_dim, self.dim)
 
     def forward(
         self,
         x: Tensor,
         context: Optional[Tensor] = None,
-        input_mask: Optional[Tensor] = None,
-        context_mask: Optional[Tensor] = None,
+        mask: Optional[Tensor] = None,
+        rotary_embedding: Optional[RotaryEmbedding] = None,
     ) -> Tensor:
         """Computes the attention."""
-        b, n, _, device = *x.shape, x.device
+        b, device = x.shape[0], x.device
 
         q = self.to_q(x)
+        q = rearrange(q, "b n (h d) -> b h n d", h=self.num_heads)
         k = self.to_k(context) if context is not None else self.to_k(x)
         v = self.to_v(context) if context is not None else self.to_v(x)
-        q, k, v = map(
-            lambda t: rearrange(t, "b n (h d) -> b h n d", h=self.num_heads), (q, k, v)
-        )
 
-        if self.rotary_embedding is not None:
-            embedding = self.rotary_embedding(q)
-            q, k, v = _apply_rotary_emb(q, k, v, embedding[None, ...])
+        if rotary_embedding is not None:
+            q, k, v = map(lambda t: rotary_embedding.rotate(t), (q, k, v))
 
-        energy = einsum("b h i d, b h j d -> b h i j", q, k) * self.scale
+        energy = einsum("b h i d, b j d -> b h i j", q, k) * self.scale
         mask_value = -torch.finfo(energy.dtype).max
-        energy = apply_input_mask(
-            b, n, k, energy, input_mask, context, context_mask, mask_value, device
-        )
+        energy = apply_input_mask(b, k, energy, mask, mask_value, device)
         if self.causal:
-            energy = apply_causal_mask(energy, input_mask, mask_value, device)
+            energy = apply_causal_mask(energy, mask, mask_value, device)
 
         attn = F.softmax(energy, dim=-1)
         attn = self.dropout(attn)
-        out = einsum("b h i j, b h j d -> b h i d", attn, v)
+        out = einsum("b h i j, b j d -> b h i d", attn, v)
         out = rearrange(out, "b h n d -> b n (h d)")
         out = self.fc(out)
         return out
 
 
-def _apply_rotary_emb(
-    q: Tensor, k: Tensor, v: Tensor, freqs: Tensor
-) -> Tuple[Tensor, Tensor, Tensor]:
-    q, k, v = map(
-        lambda t: (t * freqs.cos()) + (rotate_half(t) * freqs.sin()), (q, k, v)
-    )
-    return q, k, v
-
-
 def apply_input_mask(
     b: int,
-    n: int,
     k: Tensor,
     energy: Tensor,
-    input_mask: Optional[Tensor],
-    context: Optional[Tensor],
-    context_mask: Optional[Tensor],
+    mask: Optional[Tensor],
     mask_value: Tensor,
     device: str,
 ) -> Tensor:
     """Applies an input mask."""
-    if any(x is not None for x in (input_mask, context_mask)):
-        q_mask = (
-            input_mask
-            if input_mask is not None
-            else torch.ones((b, n), device=device).bool()
-        )
-        k_mask = q_mask if context is None else context_mask
-        k_mask = (
-            torch.ones((b, k.shape[-2]), device=device).bool()
-            if k_mask is None
-            else k_mask
-        )
-        q_mask = rearrange(q_mask, "b i -> b () i ()")
+    if mask is not None:
+        k_mask = torch.ones((b, k.shape[-2]), device=device).bool()
+        q_mask = rearrange(mask, "b i -> b () i ()")
         k_mask = rearrange(k_mask, "b j -> b () () j")
         input_mask = q_mask * k_mask