diff --git a/src/transformers/modeling_flash_attention_utils.py b/src/transformers/modeling_flash_attention_utils.py
index 201ea3eff305..356006675a5b 100644
--- a/src/transformers/modeling_flash_attention_utils.py
+++ b/src/transformers/modeling_flash_attention_utils.py
@@ -26,6 +26,7 @@
     is_torch_npu_available,
     logging,
 )
+from .utils.import_utils import is_tracing
 
 
 logger = logging.get_logger(__name__)
@@ -174,6 +175,8 @@ def _unpad_input(hidden_states, attention_mask, unused_mask=None):
     seqlens_in_batch = all_masks.sum(dim=-1, dtype=torch.int32)
     used_seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
     indices = torch.nonzero(all_masks.flatten(), as_tuple=False).flatten()
+    # `.item()` is necessary to work with torch compile as the FA API requires base ints, not tensors.
+    # You might need to set `TORCHDYNAMO_CAPTURE_SCALAR_OUTPUTS=1`.
     max_seqlen_in_batch = seqlens_in_batch.max().item()
     cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
 
@@ -223,8 +226,8 @@ def _get_unpad_data(attention_mask: torch.Tensor) -> tuple[torch.Tensor, torch.T
     """
     seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
     indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
-    # NOTE: Similar to the `.item()` in prepare_fa2_from_position_ids, with torch compile,
-    # this might cause a graph break
+    # `.item()` is necessary to work with torch compile as the FA API requires base ints, not tensors.
+    # You might need to set `TORCHDYNAMO_CAPTURE_SCALAR_OUTPUTS=1`.
     max_seqlen_in_batch = seqlens_in_batch.max().item()
     cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
     return (
@@ -346,11 +349,8 @@ def prepare_fa_kwargs_from_position_ids(position_ids):
     # We should use cu_seq_lens instead of position_ids to get the max length since position_ids is not always increasing
     # for some models (e.g. qwen2-vl).
     max_length_q = cu_seq_lens_q.diff().max()
-    # NOTE: With torch compile, this will cause a graph break if you don't set
-    # `TORCHDYNAMO_CAPTURE_SCALAR_OUTPUTS=1` in the environment or call
-    # `torch._dynamo.config.capture_scalar_outputs = True` before doing the forward pass.
-    # This is a limitation of flash attention API, as the function `flash_attn_varlen_func`
-    # requires `max_length_q`, `max_length_k` to be passed as `int` and not `torch.Tensor`.
+    # `.item()` is necessary to work with torch compile as the FA API requires base ints, not tensors.
+    # You might need to set `TORCHDYNAMO_CAPTURE_SCALAR_OUTPUTS=1`.
     max_length_q = max_length_q.item()
     max_length_k = max_length_q
 
@@ -401,8 +401,11 @@ def _is_packed_sequence(position_ids, batch_size):
         1. Position ids exist
         2. Flattened sequences only are supported
         3. Compile-friendly `not (torch.diff(position_ids, dim=-1) >= 0).all()`, i.e. we have multiple increasing sequences
+
+    NOTE: We disable this feature if torch compile or similar features are used due to dynamic control flows
+          we cannot avoid without losing control over the gradients, e.g. via `torch.cond`.
     """
-    if position_ids is None:
+    if is_tracing(position_ids) or position_ids is None:
         return False
 
     increasing_position_sequences = (
@@ -592,8 +595,10 @@ def _flash_attention_forward(
 
     # We will use `flash_varlen_fn` to prevent cross-example attention and also allow padding free approach under two cases:
     # Case 1. If position ids is provided and the position ids indicate packed sequences, see `_is_packed_sequence`.
+    #         --> not compile friendly, will be ignored if torch compile is used
     # Case 2. Some models pass directly pre-computed `cu_seqlens` so we don't need to infer it from position ids. It is safe to
-    # use `flash_varlen_fn` knowing we already have all necessary the kwargs.
+    #         use `flash_varlen_fn` knowing we already have all necessary the kwargs.
+    #         --> compile friendly, preferred option to use
     #
     # NOTE: it is user's responsibility to take care of flattening `position_ids` if that's needed by the model.
     # See #39121 for more information.