add annotation for the fusion

paddlenlp/transformers/llama/modeling.py

@@ -774,6 +774,17 @@ def forward(
 
         if self.fuse_attention_qkv:
             mix_layer = self.qkv_proj(hidden_states)
+            # NOTE for GQA attention fusion (compatible with MHA and MQA):
+            # The weight for qkv_proj is in shape like [hidden_size, hidden_size + 2 * num_kv_heads * head_dim].
+            # After the projection, the mix_layer is in shape like [b, s, hidden_size + 2 * num_kv_heads * head_dim].
+            # Reshape the mix_layer into a shape like [b, s, num_kv_heads, (num_groups + 2) * head_dim],
+            # where num_groups = num_q_heads // num_kv_heads.
+            # Split the mix_layer on the last axis into three sections [num_groups * head_dim, head_dim, head_dim]
+            # to represent the q, k and v respectively.
+            # The q is in the shape like [b, s, num_kv_heads, num_groups * head_dim].
+            # The k and v are in the shape like [b, s, num_kv_heads, head_dim].
+            # Under MHA, the q is ready for the following calculation since num_kv_heads == num_q_heads,
+            # But for the GQA or MQA, q should be reshaped into [b, s, num_q_heads, head_dim].
             if self.reshard_layer is not None:
                 if self.sequence_parallel:
                     assert self.seq_length % self.config.sep_parallel_degree == 0