[FX] refactor experimental tracer and adapt it with hf models (#3157)

* pass gpt trace and meta_prop

* pass t5 trace and meta_prop

* [FX] refactor experimental tracer and adapt it with hf models

* pass all mainstream model zoo

* fix CI

* fix CI

* fix CI

* fix CI

* fix CI

* fix CI

* fix CI

* fix CI

* skip tests

* fix CI

* using packaging version

* polish

tests/kit/model_zoo/__init__.py

@@ -1,5 +1,4 @@
 from . import diffusers, timm, torchaudio, torchrec, torchvision, transformers
-
 from .registry import model_zoo
 
 __all__ = ['model_zoo']

tests/kit/model_zoo/transformers/gpt.py

@@ -17,6 +17,14 @@ def data_gen():
     return dict(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask)
 
 
+def seq_classification_data_gen():
+    # batch sizes should be 1 if no padding token is defined.
+    input_ids = torch.zeros((1, SEQ_LENGTH), dtype=torch.int64)
+    token_type_ids = torch.zeros((1, SEQ_LENGTH), dtype=torch.int64)
+    attention_mask = torch.zeros((1, SEQ_LENGTH), dtype=torch.int64)
+    return dict(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask)
+
+
 output_transform_fn = lambda x: x
 
 config = transformers.GPT2Config(n_position=64, n_layer=2, n_head=4)
@@ -44,6 +52,6 @@ model_zoo.register(name='transformers_gpt_for_token_classification',
                    model_attribute=ModelAttribute(has_control_flow=True))
 model_zoo.register(name='transformers_gpt_for_sequence_classification',
                    model_fn=lambda: transformers.GPT2ForSequenceClassification(config),
-                   data_gen_fn=data_gen,
+                   data_gen_fn=seq_classification_data_gen,
                    output_transform_fn=output_transform_fn,
                    model_attribute=ModelAttribute(has_control_flow=True))