Allow to save model as onnx

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

auto_transformers.py

@@ -79,10 +79,13 @@ class AutoTransformers(NNOperator):
     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)
             name = self.model_name.replace('/', '-')
             path = os.path.join(path, name)
-        inputs = self.tokenizer('[CLS]', return_tensors='pt')
+        inputs = self.tokenizer('[CLS]', return_tensors='pt')  # a dictionary
         if format == 'pytorch':
+            path = path + '.pt'
             torch.save(self.model, path)
         elif format == 'torchscript':
             path = path + '.pt'
@@ -96,8 +99,36 @@ class AutoTransformers(NNOperator):
             except Exception as e:
                 log.error(f'Fail to save as torchscript: {e}.')
                 raise RuntimeError(f'Fail to save as torchscript: {e}.')
-        elif format == 'onxx':
-            pass  # todo
+        elif format == 'onnx':
+            path = path + '.onnx'
+
+            try:
+                torch.onnx.export(self.model,
+                                  tuple(inputs.values()),
+                                  path,
+                                  input_names=["input_ids", "token_type_ids", "attention_mask"],  # list(inputs.keys())
+                                  output_names=["last_hidden_state"],
+                                  opset_version=10,
+                                  dynamic_axes={
+                                      "input_ids": {0: "batch_size", 1: "input_length"},
+                                      "token_type_ids": {0: "batch_size", 1: "input_length"},
+                                      "attention_mask": {0: "batch_size", 1: "input_length"},
+                                      "last_hidden_state": {0: "batch_size"},
+                                  })
+            except Exception:
+                torch.onnx.export(self.model,
+                                  tuple(inputs.values()),
+                                  path,
+                                  input_names=["input_ids", "token_type_ids", "attention_mask"],  # list(inputs.keys())
+                                  output_names=["last_hidden_state", "pooler_output"],
+                                  opset_version=10,
+                                  dynamic_axes={
+                                      "input_ids": {0: "batch_size", 1: "input_length"},
+                                      "token_type_ids": {0: "batch_size", 1: "input_length"},
+                                      "attention_mask": {0: "batch_size", 1: "input_length"},
+                                      "last_hidden_state": {0: "batch_size"},
+                                      "pooler_outputs": {0: "batch_size"}
+                                  })
         else:
             log.error(f'Unsupported format "{format}".')
 

test_save.py

@@ -1,23 +1,39 @@
 from auto_transformers import AutoTransformers
+import onnx
 
-import torch
+f = open('onnx.csv', 'a+')
+f.write('model_name, run op, save_onnx, check_onnx\n')
 
 models = [
     'bert-base-cased',
-    'distilbert-base-cased',
-    'distilgpt2',
-    'google/fnet-base'
+    'distilbert-base-cased'
 ]
 
 for name in models:
+    line = f'{name}, '
     try:
         op = AutoTransformers(model_name=name)
         out1 = op('hello, world.')
-        op.save_model(format='torchscript')
-        op.model = torch.jit.load(name.replace('/', '-') + '.pt')
-        out2 = op('hello, world.')
-        assert (out1 == out2).all()
-        print(f'[SUCCESS] Saved torchscript for model "{name}"')
+        line += 'success, '
     except Exception as e:
-        print(f'[ERROR] Fail for model "{name}": {e}.')
+        line += 'fail, '
+        print(f'Fail to load op for {name}: {e}.')
         continue
+    try:
+        op.save_model(format='onnx')
+        line += 'success, '
+    except Exception as e:
+        line += 'fail, '
+        print(f'Fail to save onnx for {name}: {e}.')
+        continue
+    try:
+        saved_name = name.replace('/', '-')
+        onnx_model = onnx.load(f'saved/onnx/{saved_name}.onnx')
+        onnx.checker.check_model(onnx_model)
+        line += 'success'
+    except Exception as e:
+        line += 'fail'
+        print(f'Fail to check onnx for {name}: {e}.')
+        continue
+    line += '\n'
+    f.write(line)