diff --git a/src_llm/modeling_dolly.py b/src_llm/modeling_dolly.py
index db3da29..ceeae1f 100755
--- a/src_llm/modeling_dolly.py
+++ b/src_llm/modeling_dolly.py
@@ -72,27 +72,25 @@ class DollyMLP(nn.Module):
 class DollyRotaryEmbedding(nn.Module):
     def __init__(self, config: DollyConfig, device=None):
         super().__init__()
-        # BC: "rope_type" was originally "type"
-        if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
-            self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
-        else:
-            self.rope_type = "default"
-        self.max_seq_len_cached = config.max_position_embeddings
+
+        #获取"rope_type"的值，如果没有使用"default"
+        self.rope_type = config.rope_scaling.get("rope_type", "default")
+
         self.original_max_seq_len = config.max_position_embeddings
 
         self.config = config
         self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
-
+        #得到频率倒数
         inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
+        #注册缓存区
         self.register_buffer("inv_freq", inv_freq, persistent=False)
-        self.original_inv_freq = self.inv_freq
+        
 
     @torch.no_grad()
-    @dynamic_rope_update  # power user: used with advanced RoPE types (e.g. dynamic rope)
+    @dynamic_rope_update
     def forward(self, x, position_ids):
         inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
         position_ids_expanded = position_ids[:, None, :].float()
-
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with torch.autocast(device_type=device_type, enabled=False):  # Force float32
             freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
@@ -103,13 +101,14 @@ class DollyRotaryEmbedding(nn.Module):
 
 
 def rotate_half(x):
-    """Rotates half the hidden dims of the input."""
     x1 = x[..., : x.shape[-1] // 2]
     x2 = x[..., x.shape[-1] // 2 :]
     return torch.cat((-x2, x1), dim=-1)
 
-def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
- 
+def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1):
+    '''
+    对q和k使用RoPE
+    '''
     cos = cos.unsqueeze(unsqueeze_dim)
     sin = sin.unsqueeze(unsqueeze_dim)
     q_embed = (q * cos) + (rotate_half(q) * sin)