Update benchmark/run.py

Signed-off-by: Jael Gu <mengjia.gu@zilliz.com>

benchmark/run.py

@@ -124,52 +124,35 @@ elif args.format == 'onnx':
     collection_name = collection_name + '_onnx'
     saved_name = model_name.replace('/', '-')
     if not os.path.exists(onnx_path):
-        try:
-            op.save_model(format='onnx', path=onnx_path[:-5])
-        except Exception:
-            inputs = op.tokenizer('This is test.', return_tensors='pt')
-            input_names = list(inputs.keys())
-            dynamic_axes = {}
-            for i_n in input_names:
-                dynamic_axes[i_n] = {0: 'batch_size', 1: 'sequence_length'}
-            output_names = ['last_hidden_state']
-            for o_n in output_names:
-                dynamic_axes[o_n] = {0: 'batch_size', 1: 'sequence_length'}
-            torch.onnx.export(
-                op.model,
-                tuple(inputs.values()),
-                onnx_path,
-                input_names=input_names,
-                output_names=output_names,
-                dynamic_axes=dynamic_axes,
-                opset_version=14,
-                do_constant_folding=True,
-                )
+        inputs = op.tokenizer('This is test.', return_tensors='pt')
+        input_names = list(inputs.keys())
+        dynamic_axes = {}
+        for i_n in input_names:
+            dynamic_axes[i_n] = {0: 'batch_size', 1: 'sequence_length'}
+        output_names = ['last_hidden_state']
+        for o_n in output_names:
+            dynamic_axes[o_n] = {0: 'batch_size', 1: 'sequence_length'}
+        torch.onnx.export(
+            op.model,
+            tuple(inputs.values()),
+            onnx_path,
+            input_names=input_names,
+            output_names=output_names,
+            dynamic_axes=dynamic_axes,
+            opset_version=14,
+            do_constant_folding=True,
+            )
     sess = onnxruntime.InferenceSession(onnx_path,
                                         providers=onnxruntime.get_available_providers())
 
     @towhee.register
     def run_onnx(txt):
         inputs = op.tokenizer(txt, return_tensors='np')
-        try:
-            model_kind, model_onnx_config = FeaturesManager.check_supported_model_or_raise(
-                op.model, feature='default')
-            onnx_config = model_onnx_config(op.model.config)
-            new_inputs = onnx_config.generate_dummy_inputs_onnxruntime(inputs)
-            onnx_inputs = {}
-            for name, value in new_inputs.items():
-                if isinstance(value, (list, tuple)):
-                    value = onnx_config.flatten_output_collection_property(name, value)
-                    onnx_inputs.update({tensor_name: pt_tensor.numpy() for tensor_name, pt_tensor in value.items()})
-                else:
-                    onnx_inputs[name] = value.numpy()
-            outs = sess.run(output_names=['last_hidden_state'], input_feed=dict(onnx_inputs))
-        except Exception:
-            onnx_inputs = [x.name for x in sess.get_inputs()]
-            new_inputs = {}
-            for k in onnx_inputs:
-                new_inputs[k] = inputs[k]
-            outs = sess.run(output_names=['last_hidden_state'], input_feed=dict(new_inputs))
+        onnx_inputs = [x.name for x in sess.get_inputs()]
+        new_inputs = {}
+        for k in onnx_inputs:
+            new_inputs[k] = inputs[k]
+        outs = sess.run(output_names=['last_hidden_state'], input_feed=dict(new_inputs))
         return outs[0].squeeze(0)
 
     def insert(model_name, collection_name):