[remove] unused comments

gptneox_attn_replace.py

@@ -112,9 +112,7 @@ def shift(qkv, num_heads, head_dim):
                 qkv = qkv.transpose(1, 2)
                 # qkv = [bsz, q_len, nh, d]
                 qkv[:, :, num_heads//2:] = qkv[:, :, num_heads//2:].roll(-group_size//2, dims=1)
-                #qkv = qkv.transpose(1, 2)
 
-                # TODO: Changing the q_len to group_size, will require attention mask to be adjusted as well
                 # -> [bsz * n_group, group_s, nh, d)
                 #   -> [bsz * n_group, nh, group_s, d)
                 qkv = qkv.reshape(bsz * num_group, group_size, num_heads, head_dim).transpose(1, 2)
@@ -124,6 +122,7 @@ def shift(qkv, num_heads, head_dim):
             query = shift(query, self.num_attention_heads, self.head_size).contiguous()
             key = shift(key, self.num_attention_heads, self.head_size).contiguous()
             value = shift(value, self.num_attention_heads, self.head_size).contiguous()
+
             attention_mask = attention_mask[:, :, :group_size, :group_size].repeat(num_group, 1, 1, 1)
 
         # Compute attention
@@ -136,7 +135,6 @@ def shift(qkv, num_heads, head_dim):
         if self.training and not use_full:
             attn_output = attn_output.transpose(1, 2).contiguous()
             attn_output = attn_output.reshape(bsz, q_len, self.num_attention_heads, self.head_size)
-            #attn_output = attn_output.transpose(1, 2)
             # [bsz, q_len, nh, hd]
             attn_output[:, :, self.num_attention_heads//2:] = attn_output[:, :, self.num_attention_heads//2:].roll(group_size//2, dims=1)
             attn_output = attn_output.transpose(1, 2)