relax requirements to have registered normalized config for usage con… (#537)

* relax requirements to have registered normalized config for usage converted decoder models

* add property for access to normalized config

Author: eaidova

optimum/exporters/openvino/stateful.py

@@ -22,7 +22,6 @@
 from openvino.runtime import opset13
 from optimum.exporters import TasksManager
 from optimum.intel.utils.import_utils import _openvino_version, is_openvino_version
-from optimum.utils.normalized_config import NormalizedConfigManager
 
 
 def model_has_state(ov_model: ov.Model):
@@ -217,9 +216,7 @@ def patch_stateful(config: PretrainedConfig, ov_model: ov.Model):
     batch_dim = 1 if config.model_type == "chatglm" else 0
 
     fuse_cache_reorder(ov_model, not_kv_inputs, key_value_input_names, batch_dim)
-
-    normalized_config = NormalizedConfigManager.get_normalized_config_class(config.model_type)(config)
-    num_attention_heads = normalized_config.num_attention_heads if config.model_type == "bloom" else 1
+    num_attention_heads = config.num_attention_heads if config.model_type == "bloom" else 1
     make_stateful(
         ov_model, not_kv_inputs, key_value_input_names, key_value_output_names, batch_dim, num_attention_heads, None
     )

optimum/intel/openvino/modeling_decoder.py

@@ -27,7 +27,7 @@
 from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
 from transformers.modeling_outputs import CausalLMOutputWithPast
 
-from optimum.utils import NormalizedConfigManager
+from optimum.utils.normalized_config import NormalizedConfigManager
 
 from ...exporters.openvino import ensure_stateful_is_available, main_export, patch_stateful
 from ...exporters.openvino.stateful import model_has_state
@@ -132,7 +132,6 @@ def __init__(
         self.stateful = model_has_sinks
         self.main_input_name = "input_ids"
         self.num_pkv = 2
-        self.normalized_config = NormalizedConfigManager.get_normalized_config_class(config.model_type)(config)
         self.key_value_input_names = [key for key in self.input_names if "key_values" in key]
         self.key_value_output_names = [key for key in self.output_names if "present" in key]
         self._original_model = self.model.clone()  # keep original model for serialization
@@ -321,6 +320,13 @@ def reshape(self, batch_size: int, sequence_length: int):
         logger.warning("Static shapes are not supported for causal language model.")
         return self
 
+    @property
+    def normalized_config(self):
+        logger.warning(
+            "access to normalized_config attribute is deprecated and will be removed in future versions, please use config"
+        )
+        return NormalizedConfigManager.get_normalized_config_class(self.config.model_type)(self.config)
+
     def compile(self):
         if self.request is None:
             super().compile()
@@ -364,7 +370,7 @@ def forward(
 
         batch_size = input_ids.shape[0]
         if self.config.model_type == "bloom":
-            batch_size *= self.normalized_config.num_attention_heads
+            batch_size *= self.config.num_attention_heads
 
         inputs = {}
         past_len = 0
@@ -592,8 +598,8 @@ def _reorder_cache(
         if self.stateful:
             beam_idx = np.array(beam_idx)
             batch_size = beam_idx.shape[0]
-            indices = np.array(range(batch_size * self.normalized_config.num_attention_heads))
-            indices = indices.reshape([batch_size, self.normalized_config.num_attention_heads])
+            indices = np.array(range(batch_size * self.config.num_attention_heads))
+            indices = indices.reshape([batch_size, self.config.num_attention_heads])
             self.next_beam_idx = np.take(indices, beam_idx, 0).flatten()
             return past_key_values
         else: