fix past kv in old model

optimum/onnxruntime/modeling_decoder.py

@@ -509,8 +509,7 @@ def _from_pretrained(
         if model_save_dir is None:
             model_save_dir = new_model_save_dir
 
-        # Since v1.7.0 decoder with past models have fixed sequence length of 1
-        # To keep these models compatible we set this dimension to dynamic
+
         onnx_model = onnx.load(str(model_cache_path), load_external_data=False)
         model_uses_external_data = check_model_uses_external_data(onnx_model)
 
@@ -521,15 +520,28 @@ def _from_pretrained(
             node.name: [dim.dim_value or dim.dim_param for dim in node.type.tensor_type.shape.dim]
             for node in onnx_model.graph.input
         }
+        output_dims = {
+            node.name: [dim.dim_value or dim.dim_param for dim in node.type.tensor_type.shape.dim]
+            for node in onnx_model.graph.output
+        }
+
+        override_dims = False
+
+        # we changed the meaning of past sequence length at some point ?
+        for dim in input_dims.keys():
+            if "past" in dim and input_dims[dim][2] == "past_sequence_length + sequence_length":
+                input_dims[dim][2] = "past_sequence_length"
+                override_dims = True
+
+        # Since v1.7.0 decoder with past models have fixed sequence length of 1
+        # To keep these models compatible we set this dimension to dynamic
         if input_dims["input_ids"][1] == 1:
             input_dims["input_ids"][1] = "sequence_length"
-            output_dims = {
-                node.name: [dim.dim_value or dim.dim_param for dim in node.type.tensor_type.shape.dim]
-                for node in onnx_model.graph.output
-            }
             output_dims["logits"][1] = "sequence_length"
-            onnx_model = update_model_dims.update_inputs_outputs_dims(onnx_model, input_dims, output_dims)
+            override_dims = True
 
+        if override_dims:
+            onnx_model = update_model_dims.update_inputs_outputs_dims(onnx_model, input_dims, output_dims)
             onnx.save(
                 onnx_model,
                 str(model_cache_path),
@@ -539,6 +551,7 @@ def _from_pretrained(
                 size_threshold=0,
                 convert_attribute=True,
             )
+
         del onnx_model
 
         model = ORTModel.load_model(