[ Adaptation Prompt] Fix llama rotary embedding issue with transformers main

src/peft/tuners/adaption_prompt/utils.py

@@ -11,6 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import inspect
 
 import torch
 import torch.nn as nn
@@ -78,6 +79,28 @@ def llama_compute_query_states(model: nn.Module, **kwargs) -> torch.Tensor:
         else:
             # since transformers 4.36, this is a DynamicCache instance
             seq_len += past_key_value.get_seq_length(model.layer_idx)
+
+            # For transformers > 4.37.2 `position_ids` became a required arguments in the
+            # rotary embedding's forward pass. and cos/sin are indexed through the
+            # `rotary_emb` forward pass.
+            if "position_ids" in list(inspect.signature(model.rotary_emb.forward).parameters):
+                if position_ids is None:
+                    kv_seq_len = value_states.shape[-2]
+                    past_seen_tokens = past_key_value.get_usable_length(kv_seq_len, model.layer_idx)
+                    kv_seq_len += past_seen_tokens
+
+                    new_cache_positions = torch.arange(
+                        past_seen_tokens, past_seen_tokens + q_len, device=value_states.device
+                    )
+                    position_ids = new_cache_positions.unsqueeze(0)
+
+                cos, sin = model.rotary_emb(value_states, seq_len=kv_seq_len, position_ids=position_ids)
+
+                # Here cos and sin are are already indexed correctly, therefore to avoid adding
+                # boilerplate in `llama_apply_rotary_pos_emb` we just return here the correct query states
+                # embeddings
+                return (query_states * cos) + (llama_rotate_half(query_states) * sin)
+
     cos, sin = model.rotary_emb(value_states, seq_len=seq_len)
 
     return llama_apply_rotary_pos_emb(query_states, cos, sin, position_ids)