Modify save_model to support tritonserver

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

auto_transformers.py

@@ -119,47 +119,63 @@ class AutoTransformers(NNOperator):
         vec = features.cpu().detach().numpy()
         return vec
 
-    def save_model(self, format: str = 'pytorch', path: str = 'default'):
-        if path == 'default':
-            path = str(Path(__file__).parent)
-            path = os.path.join(path, 'saved', format)
-            os.makedirs(path, exist_ok=True)
+    def save_model(self, model_type: str = 'pytorch', output_file: str = 'default'):
+        if output_file == 'default':
+            output_file = str(Path(__file__).parent)
+            output_file = os.path.join(output_file, 'saved', model_type)
+            os.makedirs(output_file, exist_ok=True)
             name = self.model_name.replace('/', '-')
-            path = os.path.join(path, name)
+            output_file = os.path.join(output_file, name)
+            if model_type in ['pytorch', 'torchscript']:
+                output_file = output_file + '.pt'
+            elif model_type == 'onnx':
+                output_file = output_file + '.onnx'
+            else:
+                raise AttributeError('Unsupported model_type.')
 
         dummy_input = '[CLS]'
         inputs = self.tokenizer(dummy_input, return_tensors='pt')  # a dictionary
-        if format == 'pytorch':
-            torch.save(self.model, path + '.pt')
-        elif format == 'torchscript':
+        if model_type == 'pytorch':
+            torch.save(self.model, output_file)
+        elif model_type == 'torchscript':
             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 + '.pt')
+                torch.jit.save(jit_model, output_file)
             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':
+        elif model_type == 'onnx':
             from transformers.onnx.features import FeaturesManager
             from transformers.onnx import export
             model_kind, model_onnx_config = FeaturesManager.check_supported_model_or_raise(
                 self.model, feature='default')
             onnx_config = model_onnx_config(self.configs)
-            if os.path.isdir(path):
-                shutil.rmtree(path)
+            # if os.path.isdir(output_file[:-5]):
+            #     shutil.rmtree(output_file[:-5])
+            # print('********', Path(output_file))
             onnx_inputs, onnx_outputs = export(
                 self.tokenizer,
                 self.model,
                 config=onnx_config,
                 opset=13,
-                output=Path(path+'.onnx')
+                output=Path(output_file)
             )
         # todo: elif format == 'tensorrt':
         else:
             log.error(f'Unsupported format "{format}".')
+        return True
+
+    @property
+    def supported_formats(self):
+        onnxes = self.supported_model_names(format='onnx')
+        if self.model_name in onnxes:
+            return ['onnx']
+        else:
+            return ['pytorch']
 
     @staticmethod
     def supported_model_names(format: str = None):

test_onnx.py

@@ -12,7 +12,7 @@ import psutil
 # full_models = AutoTransformers.supported_model_names()
 # 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']
+models = ['distilbert-base-cased']
 test_txt = 'hello, world.'
 atol = 1e-3
 log_path = 'transformers_onnx.log'
@@ -55,7 +55,7 @@ for name in models:
         logger.error(f'FAIL TO LOAD OP: {e}')
         continue
     try:
-        op.save_model(format='onnx')
+        op.save_model(model_type='onnx')
         logger.info('ONNX SAVED.')
         status[2] = 'success'
     except Exception as e: