Update save_onnx

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

auto_transformers.py

@@ -15,6 +15,7 @@
 import numpy
 import os
 import torch
+import shutil
 from pathlib import Path
 from transformers import AutoTokenizer, AutoModel
 
@@ -47,6 +48,8 @@ class AutoTransformers(NNOperator):
         try:
             self.model = AutoModel.from_pretrained(model_name).to(self.device)
             self.model.eval()
+
+            self.configs = self.model.config
         except Exception as e:
             model_list = self.supported_model_names()
             if model_name not in model_list:
@@ -87,56 +90,31 @@ class AutoTransformers(NNOperator):
             name = self.model_name.replace('/', '-')
             path = os.path.join(path, name)
 
-        inputs = self.tokenizer('[CLS]', return_tensors='pt')  # a dictionary
+        dummy_input = '[CLS]'
+        inputs = self.tokenizer(dummy_input, return_tensors='pt')  # a dictionary
         if format == 'pytorch':
-            path = path + '.pt'
-            torch.save(self.model, path)
+            torch.save(self.model, path + '.pt')
         elif format == 'torchscript':
-            path = path + '.pt'
             inputs = list(inputs.values())
             try:
                 try:
                     jit_model = torch.jit.script(self.model)
                 except Exception:
                     jit_model = torch.jit.trace(self.model, inputs, strict=False)
-                torch.jit.save(jit_model, path)
+                torch.jit.save(jit_model, path + '.pt')
             except Exception as e:
                 log.error(f'Fail to save as torchscript: {e}.')
                 raise RuntimeError(f'Fail to save as torchscript: {e}.')
         elif format == 'onnx':
-            path = path + '.onnx'
-            input_names = list(inputs.keys())
-            dynamic_axes = {}
-            for i_n in input_names:
-                dynamic_axes[i_n] = {0: 'batch_size', 1: 'sequence_length'}
-            try:
-                output_names = ['last_hidden_state']
-                for o_n in output_names:
-                    dynamic_axes[o_n] = {0: 'batch_size', 1: 'sequence_length'}
-                torch.onnx.export(self.model,
-                                  tuple(inputs.values()),
-                                  path,
-                                  input_names=input_names,
-                                  output_names=output_names,
-                                  dynamic_axes=dynamic_axes,
-                                  opset_version=11,
-                                  do_constant_folding=True,
-                                  # enable_onnx_checker=True,
-                                  )
-            except Exception as e:
-                print(e, '\nTrying with 2 outputs...')
-                output_names = ['last_hidden_state', 'pooler_output']
-                for o_n in output_names:
-                    dynamic_axes[o_n] = {0: 'batch_size', 1: 'sequence_length'}
-                torch.onnx.export(self.model,
-                                  tuple(inputs.values()),
-                                  path,
-                                  input_names=input_names,
-                                  output_names=output_names,
-                                  dynamic_axes=dynamic_axes,
-                                  opset_version=11,
-                                  do_constant_folding=True,
-                                  # enable_onnx_checker=True,
+            from transformers.convert_graph_to_onnx import convert
+            if os.path.isdir(path):
+                shutil.rmtree(path)
+            path = os.path.join(path, 'model.onnx')
+            convert(
+                model=self.model_name,
+                output=Path(path),
+                framework='pt',
+                opset=13
             )
         # todo: elif format == 'tensorrt':
         else:

test_onnx.py

@@ -14,7 +14,7 @@ import psutil
 # checked_models = AutoTransformers.supported_model_names(format='onnx')
 # models = [x for x in full_models if x not in checked_models]
 models = ['bert-base-cased', 'distilbert-base-cased']
-test_txt = '[UNK]'
+test_txt = 'hello, world.'
 atol = 1e-3
 log_path = 'transformers_onnx.log'
 f = open('onnx.csv', 'w+')
@@ -43,6 +43,7 @@ status = None
 for name in models:
     logger.info(f'***{name}***')
     saved_name = name.replace('/', '-')
+    onnx_path = f'saved/onnx/{saved_name}/model.onnx'
     if status:
         f.write(','.join(status) + '\n')
     status = [name] + ['fail'] * 5
@@ -63,10 +64,10 @@ for name in models:
         continue
     try:
         try:
-            onnx_model = onnx.load(f'saved/onnx/{saved_name}.onnx')
+            onnx_model = onnx.load(onnx_path)
             onnx.checker.check_model(onnx_model)
         except Exception:
-            saved_onnx = onnx.load(f'saved/onnx/{saved_name}.onnx', load_external_data=False)
+            saved_onnx = onnx.load(onnx_path, load_external_data=False)
             onnx.checker.check_model(saved_onnx)
         logger.info('ONNX CHECKED.')
         status[3] = 'success'
@@ -74,10 +75,10 @@ for name in models:
         logger.error(f'FAIL TO CHECK ONNX: {e}')
         continue
     try:
-        sess = onnxruntime.InferenceSession(f'saved/onnx/{saved_name}.onnx',
+        sess = onnxruntime.InferenceSession(onnx_path,
                                             providers=onnxruntime.get_available_providers())
         inputs = op.tokenizer(test_txt, return_tensors='np')
-        out2 = sess.run(output_names=['last_hidden_state'], input_feed=dict(inputs))
+        out2 = sess.run(output_names=['output_0'], input_feed=dict(inputs))
         logger.info('ONNX WORKED.')
         status[4] = 'success'
         if numpy.allclose(out1, out2, atol=atol):