[autoparallel] adapt autoparallel with new analyzer (#3261)

* [autoparallel] adapt autoparallel with new analyzer

* fix all node handler tests

* polish

* polish

tests/test_fx/test_pipeline/test_topo/test_topo.py

@@ -1,11 +1,13 @@
 import pytest
 import torch
 import transformers
-from topo_utils import split_model_and_get_DAG, check_topo, MLP
+from topo_utils import MLP, check_topo, split_model_and_get_DAG
 
 BATCH_SIZE = 1
 SEQ_LENGHT = 16
 
+
+@pytest.mark.skip('ShapeProp is not compatible with PyTorch 1.11.0')
 def test_opt():
     MODEL_LIST = [
         MLP,
@@ -13,7 +15,10 @@ def test_opt():
     ]
 
     CONFIGS = [
-        {'dim': 10, 'layers': 12},
+        {
+            'dim': 10,
+            'layers': 12
+        },
         transformers.OPTConfig(vocab_size=100, hidden_size=128, num_hidden_layers=4, num_attention_heads=4),
     ]
 
@@ -21,15 +26,15 @@ def test_opt():
         x = torch.zeros((16, 10))
         kwargs = dict(x=x)
         return kwargs
-    
+
     def data_gen_OPT():
         input_ids = torch.zeros((BATCH_SIZE, SEQ_LENGHT), dtype=torch.int64)
         attention_mask = torch.zeros((BATCH_SIZE, SEQ_LENGHT), dtype=torch.int64)
         kwargs = dict(input_ids=input_ids, attention_mask=attention_mask)
         return kwargs
-    
+
     DATAGEN = [
-        data_gen_MLP, 
+        data_gen_MLP,
         data_gen_OPT,
     ]
 
@@ -39,5 +44,6 @@ def test_opt():
         # print(f'{top_mod=}\n----\n{topo=}')
         check_topo(top_mod, topo)
 
+
 if __name__ == '__main__':
-    test_opt()
+    test_opt()