Fix onnx export input names order (#4641)

* pass on tokenizer to pipeline

* order input names when convert to onnx

* update style

* remove unused imports

* make ordered inputs list needs to be mutable

* add test custom bert model

* remove unused imports

src/transformers/convert_graph_to_onnx.py

@@ -1,5 +1,4 @@
 from argparse import ArgumentParser
-from itertools import takewhile
 from os import listdir, makedirs
 from os.path import abspath, dirname, exists
 from typing import Dict, List, Optional, Tuple
@@ -38,14 +37,17 @@ def ensure_valid_input(model, tokens, input_names):
 
     """
     model_args_name = model.forward.__code__.co_varnames
-    model_args_pos = [(model_args_name.index(name) - 1, name) for name in input_names]
-    model_args = [None] * (max(map(lambda x: x[0], model_args_pos)) + 1)
 
-    for arg_pos, arg_name in model_args_pos:
-        model_args[arg_pos] = tokens[arg_name]
+    ordered_input_names = []
+    model_args = []
+    for arg_name in model_args_name[1:]:  # start at index 1 to skip "self" argument
+        if arg_name in input_names:
+            ordered_input_names.append(arg_name)
+            model_args.append(tokens[arg_name])
+        else:
+            break
 
-    model_args = tuple(model_args)  # Need to be ordered
-    return tuple(takewhile(lambda arg: arg is not None, model_args))
+    return ordered_input_names, tuple(model_args)
 
 
 def infer_shapes(nlp: Pipeline, framework: str) -> Tuple[List[str], List[str], Dict, BatchEncoding]:
@@ -117,13 +119,13 @@ def convert_pytorch(nlp: Pipeline, opset: int, output: str, use_external_format:
 
     with torch.no_grad():
         input_names, output_names, dynamic_axes, tokens = infer_shapes(nlp, "pt")
-        model_args = ensure_valid_input(nlp.model, tokens, input_names)
+        ordered_input_names, model_args = ensure_valid_input(nlp.model, tokens, input_names)
 
         export(
             nlp.model,
             model_args,
             f=output,
-            input_names=input_names,
+            input_names=ordered_input_names,
             output_names=output_names,
             dynamic_axes=dynamic_axes,
             do_constant_folding=True,