1 files changed, 0 insertions, 67 deletions
diff --git a/text_recognizer/networks/transformer/embeddings/rotary.py b/text_recognizer/networks/transformer/embeddings/rotary.py
deleted file mode 100644
index ca0a260..0000000
--- a/text_recognizer/networks/transformer/embeddings/rotary.py
+++ /dev/null
@@ -1,67 +0,0 @@
-"""Roatary embedding.
-
-Stolen from lucidrains:
-    https://github.com/lucidrains/rotary-embedding-torch
-
-Explanation of roatary:
-    https://blog.eleuther.ai/rotary-embeddings/
-"""
-from inspect import isfunction
-
-from einops import rearrange, repeat
-import torch
-from torch import Tensor, nn
-
-
-class RotaryEmbedding(nn.Module):
-    """Rotary positional embedding."""
-
-    def __init__(self, dim: int) -> None:
-        super().__init__()
-        inv_freqs = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim))
-        self.register_buffer("inv_freqs", inv_freqs)
-        self.cache = {}
-
-    def rotate(self, t: Tensor, dim: int = -2) -> Tensor:
-        """Rotate vector."""
-        device, n = t.device, t.shape[dim]
-        freqs = self.forward(lambda: torch.arange(n, device=device), cache_key=n)
-        return apply_rotary_emb(t, freqs)
-
-    def forward(self, t: Tensor, cache_key: int) -> Tensor:
-        """Encodes tensor x with rotary embeddings."""
-        if cache_key in self.cache:
-            return self.cache[cache_key]
-
-        if isfunction(t):
-            t = t()
-
-        freqs = self.inv_freqs
-        freqs = torch.einsum("..., f -> ... f", t.type(freqs.dtype), freqs)
-        freqs = repeat(freqs, "... n -> ... (n r)", r=2)
-        self.cache[cache_key] = freqs
-        return freqs
-
-
-def rotate_half(x: Tensor) -> Tensor:
-    x = rearrange(x, "... (d r) -> ... d r", r=2)
-    x1, x2 = x.unbind(dim=-1)
-    x = torch.stack((-x2, x1), dim=-1)
-    return rearrange(x, "... d r -> ... (d r)")
-
-
-def apply_rotary_emb(t: Tensor, freqs: Tensor, start_index: int = 0) -> Tensor:
-    freqs = freqs.to(t)
-    rot_dim = freqs.shape[-1]
-    end_index = start_index + rot_dim
-    assert rot_dim <= t.shape[-1], (
-        f"feature dimension {t.shape[-1]} is not of sufficient size to rotate"
-        f"in all the positions {rot_dim}"
-    )
-    t_left, t, t_right = (
-        t[..., :start_index],
-        t[..., start_index:end_index],
-        t[..., end_index:],
-    )
-    t = (t * freqs.cos()) + (rotate_half(t) * freqs.sin())
-    return torch.cat((t_left, t, t_right), dim=-1)