[Docs] Fix mention of MQA/GQA in qkvpacked functions

flash_attn/flash_attn_interface.py

@@ -285,10 +285,8 @@ def flash_attn_qkvpacked_func(qkv, dropout_p=0.0, softmax_scale=None, causal=Fal
     If Q, K, V are already stacked into 1 tensor, this function will be faster than
     calling flash_attn_func on Q, K, V since the backward pass avoids explicit concatenation
     of the gradients of Q, K, V.
-    Supports multi-query and grouped-query attention (MQA/GQA) by passing in KV with fewer heads
-    than Q. Note that the number of heads in KV must be divisible by the number of heads in Q.
-    For example, if Q has 6 heads and K, V have 2 heads, head 0, 1, 2 of Q will attention to head
-    0 of K, V, and head 3, 4, 5 of Q will attention to head 1 of K, V.
+    For multi-query and grouped-query attention (MQA/GQA), please see
+    flash_attn_kvpacked_func and flash_attn_func.
 
     Arguments:
         qkv: (batch_size, seqlen, 3, nheads, headdim)
@@ -381,8 +379,8 @@ def flash_attn_varlen_qkvpacked_func(qkv, cu_seqlens, max_seqlen, dropout_p=0.0,
     If Q, K, V are already stacked into 1 tensor, this function will be faster than
     calling flash_attn_varlen_func on Q, K, V since the backward pass avoids explicit concatenation
     of the gradients of Q, K, V.
-    For example, if Q has 6 heads and K, V have 2 heads, head 0, 1, 2 of Q will attention to head
-    0 of K, V, and head 3, 4, 5 of Q will attention to head 1 of K, V.
+    For multi-query and grouped-query attention (MQA/GQA), please see
+    flash_attn_varlen_kvpacked_func and flash_attn_varlen_func.
 
     Arguments:
         qkv: (total, 3, nheads, headdim), where total = total number of tokens in the batch.