1 files changed, 6 insertions, 9 deletions
diff --git a/text_recognizer/networks/transformer/embeddings/rotary.py b/text_recognizer/networks/transformer/embeddings/rotary.py
index 2f58964..ef2b85d 100644
--- a/text_recognizer/networks/transformer/embeddings/rotary.py
+++ b/text_recognizer/networks/transformer/embeddings/rotary.py
@@ -6,9 +6,6 @@ Stolen from lucidrains:
 Explanation of roatary:
     https://blog.eleuther.ai/rotary-embeddings/
 """
-from typing import Tuple
-
-from einops import rearrange
 import torch
 from torch import nn
 from torch import Tensor
@@ -17,21 +14,21 @@ from torch import Tensor
 class RotaryEmbedding(nn.Module):
     """Rotary positional embedding."""
 
-    def __init__(self, dim: int):
+    def __init__(self, dim: int) -> None:
         super().__init__()
         inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim))
         self.register_buffer("inv_freq", inv_freq)
 
-    def forward(self, x: Tensor, seq_dim: int = 1) -> Tensor:
+    def forward(self, x: Tensor) -> Tensor:
         """Encodes tensor x with rotary embeddings."""
-        t = torch.arange(x.shape[seq_dim], device=x.device).type_as(self.inv_freq)
-        freqs = torch.einsum("i , j -> i j", t, self.inv_freq)
+        freqs = self.inv_freqs
+        freqs = torch.einsum("..., f -> ... f", x.type(freqs.dtype), freqs)
         emb = torch.cat((freqs, freqs), dim=-1)
-        return rearrange(emb, "n d -> () () n d")
+        return emb
 
 
 def rotate_half(x: Tensor) -> Tensor:
-    x = rearrange(x, "... (j d) -> ... j d", j=2)
+    x = x.reshape((x.shape[0], -1, 2, x.shape[-1] // 2))
     x1, x2 = x.unbind(dim=-2)
     return torch.cat((-x2, x1), dim=-1)