tranformer layer done

1 files changed, 35 insertions, 7 deletions

diff --git a/text_recognizer/networks/transformer/layers.py b/text_recognizer/networks/transformer/layers.py
index 1c951ae..a2fdb1a 100644
--- a/text_recognizer/networks/transformer/layers.py
+++ b/text_recognizer/networks/transformer/layers.py
@@ -10,6 +10,7 @@ from torch import nn, Tensor
 from .attention import Attention
 from .mlp import FeedForward
 from .residual import Residual
+from .rotary_embedding import RotaryEmbedding
 
 
 class AttentionLayers(nn.Module):
@@ -24,17 +25,23 @@ class AttentionLayers(nn.Module):
         norm_fn: Type[nn.Module] = nn.LayerNorm,
         ff_fn: Type[nn.Module] = FeedForward,
         residual_fn: Type[nn.Module] = Residual,
+        rotary_emb: Optional[Type[nn.Module]] = None,
+        rotary_emb_dim: Optional[int] = None,
         causal: bool = False,
         cross_attend: bool = False,
+        pre_norm: bool = True,
     ) -> None:
         super().__init__()
         attn_fn = partial(attn_fn, dim=dim, num_heads=num_heads, **attn_kwargs)
         norm_fn = partial(norm_fn, dim=dim)
         ff_fn = partial(ff_fn, dim=dim, **ff_kwargs)
-        layer_types = self._get_layer_types(cross_attend) * depth
+        self.layer_types = self._get_layer_types(cross_attend) * depth
         self.layers = self._build_network(
-            layer_types, causal, attn_fn, norm_fn, ff_fn, residual_fn
+            causal, attn_fn, norm_fn, ff_fn, residual_fn
         )
+        rotary_emb_dim = max(rotary_emb_dim, 32) if rotary_emb_dim is not None else None
+        self.rotary_emb = RotaryEmbedding(rotary_emb_dim) if rotary_emb else None
+        self.pre_norm = pre_norm
 
     @staticmethod
     def _get_layer_types(cross_attend: bool) -> Tuple:
@@ -43,18 +50,17 @@ class AttentionLayers(nn.Module):
             return "a", "c", "f"
         return "a", "f"
 
-    @staticmethod
     def _build_network(
-        layer_types: Tuple,
+        self,
         causal: bool,
         attn_fn: partial,
         norm_fn: partial,
         ff_fn: partial,
         residual_fn: Type[nn.Module],
     ) -> nn.ModuleList:
-        """Configures transformer layers."""
+        """Configures transformer network."""
         layers = nn.ModuleList([])
-        for layer_type in layer_types:
+        for layer_type in self.layer_types:
             if layer_type == "a":
                 layer = attn_fn(causal=causal)
             elif layer_type == "c":
@@ -74,4 +80,26 @@ class AttentionLayers(nn.Module):
         mask: Optional[Tensor] = None,
         context_mask: Optional[Tensor] = None,
     ) -> Tensor:
-        pass
+        rotary_pos_emb = self.rotary_emb(x) if self.rotary_emb is not None else None
+
+        for i, (layer_type, (norm, block, residual_fn)) in enumerate(zip(self.layer_types, self.layers)):
+            is_last = i == len(self.layers) - 1
+            
+           residual = x 
+
+            if self.pre_norm:
+                x = norm(x)
+
+            if layer_type == "a":
+                out, _ = block(x=x, mask=mask, rotary_pos_emb=rotary_pos_emb)
+            elif layer_type == "c":
+                out, _ = block(x, context=context, mask=mask, context_mask=context_mask)
+            elif layer_type == "f":
+                out = block(x)
+
+            x = residual_fn(out, residual)
+
+            if not self.pre_norm and not is_last:
+                x = norm(x)
+
+        return x