fix(phi4): restore RoPE export workaround for transformers 5.x

optimum/exporters/executorch/tasks/causal_lm.py

@@ -20,6 +20,7 @@
 from ..integrations import CausalLMExportableModule
 from ..quantization import quantize_model_
 from ..task_registry import register_task
+from ..utils import disable_dynamic_rope_for_export
 
 
 # NOTE: It’s important to map the registered task name to the pipeline name in https://github.com/huggingface/transformers/blob/main/utils/update_metadata.py.
@@ -69,11 +70,9 @@ def load_causal_lm_model(model_name_or_path: str, **kwargs) -> CausalLMExportabl
         )
     config = kwargs.get("config") or AutoConfig.from_pretrained(model_name_or_path, gguf_file=gguf_file)
 
-    if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
-        # NOTE: To make the model exportable we need to set the rope scaling to default to avoid hitting
-        # the data-dependent control flow in _longrope_frequency_update. Alternatively, users should rewrite
-        # that function to avoid the data-dependent control flow.
-        config.rope_scaling["type"] = "default"
+    # Downgrade longrope/dynamic RoPE to the static "default" RoPE, otherwise export fails on the
+    # data-dependent control flow in the RoPE frequency update. See helper for version details.
+    disable_dynamic_rope_for_export(config)
 
     if hasattr(config, "use_cache") and config.use_cache is False:
         config.use_cache = True

optimum/exporters/executorch/tasks/multimodal_text_to_text.py

@@ -23,6 +23,7 @@
 from ..integrations import MultiModalTextToTextExportableModule
 from ..quantization import quantize_model_
 from ..task_registry import register_task
+from ..utils import disable_dynamic_rope_for_export
 
 
 # NOTE: It's important to map the registered task name to the pipeline name in https://github.com/huggingface/transformers/blob/main/utils/update_metadata.py.
@@ -90,9 +91,9 @@ def load_multimodal_text_to_text_model(model_name_or_path: str, **kwargs):
     if not (hasattr(config, "text_config")):
         raise ValueError(f"The model {model_name_or_path} does not have a `text_config`.")
 
-    if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
-        # NOTE: Avoid hitting the data-dependent control flow in _longrope_frequency_update.
-        config.rope_scaling["type"] = "default"
+    # Avoid hitting the data-dependent control flow in the longrope/dynamic RoPE frequency update
+    # during export. See helper for version details.
+    disable_dynamic_rope_for_export(config)
     if hasattr(config, "use_cache") and config.use_cache is False:
         config.use_cache = True
 

optimum/exporters/executorch/utils.py

@@ -64,6 +64,39 @@ def save_config_to_constant_methods(
     return combined_metadata
 
 
+def disable_dynamic_rope_for_export(config: PretrainedConfig) -> None:
+    """
+    Force RoPE variants that use data-dependent control flow to the static ``"default"`` RoPE so the
+    model can be exported.
+
+    ``longrope`` and ``dynamic`` RoPE recompute their frequencies inside the forward pass based on the
+    current sequence length (e.g. ``if seq_len > original_max_position_embeddings``). ``torch.export``
+    /Dynamo cannot trace this data-dependent branching, so export fails. These RoPE types are downgraded
+    to ``"default"`` (which is static and traceable); other RoPE types (``linear``/``llama3``/``yarn``/...)
+    are static already and are left untouched.
+
+    transformers>=5 renamed ``rope_scaling`` to ``rope_parameters`` (``rope_scaling`` is kept as an alias)
+    and dispatches on the ``"rope_type"`` key, whereas older versions read ``"type"``. Both keys are set so
+    the workaround is effective regardless of the installed transformers version. In transformers>=5 the
+    params may also be a nested dict keyed by layer type, which is handled here too.
+    """
+    rope_params = getattr(config, "rope_scaling", None)
+    if not rope_params:
+        return
+
+    # `rope_params` is either a single global dict or, in transformers>=5, a dict of per-layer-type dicts.
+    if all(isinstance(v, dict) for v in rope_params.values()):
+        param_dicts = list(rope_params.values())
+    else:
+        param_dicts = [rope_params]
+
+    for params in param_dicts:
+        rope_type = params.get("rope_type", params.get("type"))
+        if rope_type == "longrope" or (isinstance(rope_type, str) and "dynamic" in rope_type):
+            params["type"] = "default"
+            params["rope_type"] = "default"
+
+
 def apply_chat_template_with_fallback(processor, conversation, **kwargs):
     """
     Apply chat template with fallback for external processors.