diff --git a/megatron/model/transformer.py b/megatron/model/transformer.py
index d112a7461..42dbdfeeb 100644
--- a/megatron/model/transformer.py
+++ b/megatron/model/transformer.py
@@ -763,51 +763,16 @@ def gqa_project(self, hidden_states, attention_mask, layer_past=None):
         # pass through projection: [sq, b, h] --> [sq, b, ((np + 2 * kvp) * hn)]
         mixed_x_layer, _ = self.query_key_value(hidden_states)
 
-        # First: reshape so we have seqlen, batch, and num. query heads each as separate dims
-        # Final dim is not exactly head dim: the first (head dim) dims are query heads,
-        # The last (head dim * ratio of kv to q heads) each are the "k/v heads"
-        # (right now we treat like we have same num. heads, but smaller head dim)
-
-        # [sq, b, ((np + 2 * kvp) * hn)] --> [sq, b, np, (hn * (1 + 2 * (kvp / np)))]
-        new_qkv_shape = (
-            mixed_x_layer.shape[0],
-            mixed_x_layer.shape[1],
-            self.num_attention_heads_per_partition,
-            int(
-                self.hidden_size_per_attention_head
-                * (
-                    1
-                    + 2
-                    * (
-                        self.num_kv_heads_per_partition
-                        / self.num_attention_heads_per_partition
-                    )
-                )
-            ),
-        )
-        mixed_x_layer = mixed_x_layer.reshape(*new_qkv_shape)
-
-        # Next: split our fake head dim. (last dim) so that the first (head dim) dimensions go to Q,
-        # the last smaller 2 * (head dim * kv to q head ratio) each divided between K and V separately
+        # split the last dim, so that the first (q head * head dim) dimensions go to Q,
+        # the last smaller 2 * (kv head * head dim) each divided between K and V separately
         split_sizes = (
-            self.hidden_size_per_attention_head,
-            int(
-                (
-                    self.num_kv_heads_per_partition
-                    / self.num_attention_heads_per_partition
-                )
-                * self.hidden_size_per_attention_head
-            ),
-            int(
-                (
-                    self.num_kv_heads_per_partition
-                    / self.num_attention_heads_per_partition
-                )
-                * self.hidden_size_per_attention_head
-            ),
+            self.num_attention_heads_per_partition
+            * self.hidden_size_per_attention_head,
+            self.num_kv_heads_per_partition * self.hidden_size_per_attention_head,
+            self.num_kv_heads_per_partition * self.hidden_size_per_attention_head,
         )
 
-        # [sq, b, np, (hn * (1 + 2 * (kvp / np)))] --> 1 x [sq, b, np, hn] , 2 x [sq, b, np, (hn * (kvp / np))]
+        # [sq, b, ((np + 2 * kvp) * hn)] --> 1 x [sq, b, np * hn] , 2 x [sq, b, kvp * hn]
         (query_layer, key_layer, value_layer) = [
             x.contiguous()
             for x in torch.split(
@@ -817,6 +782,17 @@ def gqa_project(self, hidden_states, attention_mask, layer_past=None):
             )
         ]
 
+        # reshape Q to proper output shape (last dim = correct full "real" head size again)
+        # [sq, b, np * hn] --> [sq, b, np, hn]
+        new_query_shape = (
+            query_layer.size(0),
+            query_layer.size(1),
+            self.num_attention_heads_per_partition,
+            self.hidden_size_per_attention_head,
+        )
+
+        query_layer = query_layer.view(*new_query_shape)
+
         # reshape K/V to proper output shape (last dim = correct full "real" head size again)
         # 2 x [sq, b, np, (hn * (kvp / np))] --> 2 x [sq, b, kvp, hn]
         new_kv_shape = (