warn

optimum/onnxruntime/modeling_decoder.py

@@ -509,7 +509,6 @@ def _from_pretrained(
         if model_save_dir is None:
             model_save_dir = new_model_save_dir
 
-
         onnx_model = onnx.load(str(model_cache_path), load_external_data=False)
         model_uses_external_data = check_model_uses_external_data(onnx_model)
 
@@ -527,29 +526,38 @@ def _from_pretrained(
 
         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["logits"][1] = "sequence_length"
             override_dims = True
 
+        # Since https://github.com/huggingface/optimum/pull/871/files
+        # changed axis notation/naming during export, we need to update the dims
+        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
+
         if override_dims:
+            # this is kinda dangerous, warning the user is the least we can do
+            logger.warning(
+                "The ONNX model was probably exported with an older version of optimum. "
+                "We are updating the input/output dimensions and overwriting the model file "
+                "with new dimensions. This is necessary for the model to work correctly with "
+                "the current version of optimum. If you encounter any issues, please re-export "
+                "the model with the latest version of optimum for optimal performance."
+            )
             onnx_model = update_model_dims.update_inputs_outputs_dims(onnx_model, input_dims, output_dims)
             onnx.save(
                 onnx_model,
                 str(model_cache_path),
                 save_as_external_data=model_uses_external_data,
-                all_tensors_to_one_file=True,
                 location=model_cache_path.name + "_data",
-                size_threshold=0,
+                all_tensors_to_one_file=True,
                 convert_attribute=True,
+                size_threshold=0,
             )
 
         del onnx_model