Move mlp to ff

1 files changed, 46 insertions, 0 deletions

diff --git a/text_recognizer/networks/transformer/ff.py b/text_recognizer/networks/transformer/ff.py
new file mode 100644
index 0000000..4028ab3
--- /dev/null
+++ b/text_recognizer/networks/transformer/ff.py
@@ -0,0 +1,46 @@
+"""Feedforward layer in transformer.
+
+Stolen from lucidrains:
+    https://github.com/lucidrains/x-transformers/blob/main/x_transformers/x_transformers.py
+"""
+from typing import Optional
+
+from torch import nn
+from torch import Tensor
+import torch.nn.functional as F
+
+
+class GEGLU(nn.Module):
+    def __init__(self, dim_in: int, dim_out: int) -> None:
+        super().__init__()
+        self.fc = nn.Linear(dim_in, dim_out * 2)
+
+    def forward(self, x: Tensor) -> Tensor:
+        x, gate = self.fc(x).chunk(2, dim=-1)
+        return x * F.gelu(gate)
+
+
+class FeedForward(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        dim_out: Optional[int] = None,
+        expansion_factor: int = 4,
+        glu: bool = True,
+        dropout_rate: float = 0.0,
+    ) -> None:
+        super().__init__()
+        inner_dim = dim * expansion_factor
+        dim_out = dim_out if dim_out is not None else dim
+        in_projection = (
+            nn.Sequential(nn.Linear(dim, inner_dim), nn.GELU())
+            if not glu
+            else GEGLU(dim, inner_dim)
+        )
+
+        self.mlp = nn.Sequential(
+            in_projection, nn.Dropout(dropout_rate), nn.Linear(inner_dim, dim_out)
+        )
+
+    def forward(self, x: Tensor) -> Tensor:
+        return self.mlp(x)