optimize(dvae): remove einops (#383)

ChatTTS/model/dvae.py

@@ -1,5 +1,4 @@
 import math
-from einops import rearrange
 from vector_quantize_pytorch import GroupedResidualFSQ
 
 import torch
@@ -66,23 +65,32 @@ def __init__(self,
         self.G = G
         self.R = R
 
-    def _embed(self, x):
+    def _embed(self, x: torch.Tensor):
         if self.transpose:
             x = x.transpose(1,2)
+        """
         x = rearrange(
             x, "b t (g r) -> g b t r", g = self.G, r = self.R,
-        )  
+        )
+        """
+        x.view(-1, self.G, self.R).permute(2, 0, 1, 3)
         feat = self.quantizer.get_output_from_indices(x)
         return feat.transpose(1,2) if self.transpose else feat
-        
+
     def forward(self, x,):
         if self.transpose:
             x = x.transpose(1,2)
         feat, ind = self.quantizer(x)
+        """
         ind = rearrange(
             ind, "g b t r ->b t (g r)",
-        )  
-        embed_onehot = F.one_hot(ind.long(), self.n_ind).to(x.dtype)
+        )
+        """
+        ind = ind.permute(1, 2, 0, 3).contiguous()
+        ind = ind.view(ind.size(0), ind.size(1), -1)
+        embed_onehot_tmp = F.one_hot(ind.long(), self.n_ind)
+        embed_onehot = embed_onehot_tmp.to(x.dtype)
+        del embed_onehot_tmp
         e_mean = torch.mean(embed_onehot, dim=[0,1])
         e_mean = e_mean / (e_mean.sum(dim=1) + self.eps).unsqueeze(1)
         perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + self.eps), dim=1))