update model

model/model.py

@@ -27,15 +27,11 @@ class RMSNorm(torch.nn.Module):
         return output * self.weight
 
 
-def precompute_pos_cis(dim: int, end: int, theta: float = 10000.0, train_len: int = 512):
+def precompute_pos_cis(dim: int, end: int, theta: float = 10000.0):
     freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
     t = torch.arange(end, device=freqs.device)  # type: ignore
     freqs = torch.outer(t, freqs).float()  # type: ignore
     pos_cis = torch.polar(torch.ones_like(freqs), freqs)  # complex64
-    # # 计算缩放因子
-    # scale = train_len / end
-    # # 缩放旋转嵌入，实现线性的长度外推（注释掉不用是因为小模型依赖pos_cis拟合严重，直接做线性外推效果并不好）
-    # pos_cis = pos_cis * scale
     return pos_cis