Remove attention sinks

awq/modules/fused/cache.py

 import torch
 
 class WindowedCache:
-    def __init__(self, cache_v_shape, cache_k_shape, device, attention_sinks=4):
+    def __init__(self, cache_v_shape, cache_k_shape, device):
         """
         The window size is the same as the max_new_tokens. The window will
         automatically roll once max_new_tokens is exceeded.
         """
-        self.attention_sinks = attention_sinks
-
         # [batch_size, n_kv_heads, max_seq_len, head_dim]
         self.v = torch.zeros(cache_v_shape).to(device).half()
         # [batch_size, n_kv_heads, head_dim // pack_factor, max_seq_len, pack_factor]
@@ -25,15 +23,9 @@ class WindowedCache:
         self.k[:batch_size, :, :, start_pos : start_pos + seqlen, :] = keys_store
     
     def roll_kv(self, roll_len, start_pos):
-        """
-        With sink=0, roll_len=3, and [A,B,C,D,E] we get [D,E,F,G,H]
-        With sink=1, roll_len=3, and [A,B,C,D,E] we get [A,E,F,G,H]
-        With sink=2, roll_len=3, and [A,B,C,D,E] we get [A,B,F,G,H]
-        With sink=3, roll_len=3, and [A,B,C,D,E] we get [A,B,C,G,H]
-        """
         # Roll only the necessary part of the cache to the left
-        self.v[:, :, self.attention_sinks:-roll_len+self.attention_sinks, :] = self.v[:, :, roll_len:, :]
-        self.k[:, :, :, self.attention_sinks:-roll_len+self.attention_sinks, :] = self.k[:, :, :, roll_len:, :]
+        self.v[:, :, :-roll_len, :] = self.v[:, :, roll_len:, :]
+        self.k[:, :, :, :-roll_len, :] = self.k[:, :, :, roll_len:, :]
 
         # Zero out the new part
         self.v[:, :, -roll_len:, :] = 0