[fx] added testing for all bert variants (#1207)

* [fx] added testing for all bert variants

* polish code

colossalai/fx/proxy.py

@@ -1,6 +1,8 @@
 import operator
 import torch
 from torch.fx.proxy import Proxy, Attribute
+from typing import List, Union
+from torch.utils._pytree import PyTree
 
 __all__ = ['ColoProxy']
 
@@ -26,8 +28,12 @@ class ColoProxy(Proxy):
         return self._meta_tensor
 
     @meta_tensor.setter
-    def meta_tensor(self, tensor: torch.Tensor):
-        assert tensor is None or tensor.is_meta, 'Expected to receive a meta tensor, but got a non-meta tensor'
+    def meta_tensor(self, tensor: Union[List[torch.Tensor], torch.Tensor]):
+
+        def _is_meta(item):
+            assert torch.is_tensor(item) and item.is_meta
+
+        torch.fx.node.map_aggregate(tensor, _is_meta)
         self._meta_tensor = tensor
 
     @property
@@ -83,6 +89,14 @@ class ColoProxy(Proxy):
     def __setitem__(self, indices, values):
         return self.tracer.create_proxy("call_function", operator.setitem, (self, indices, values), {})
 
+    def __contains__(self, key):
+        if self.node.op == "placeholder":
+            # this is used to handle like
+            # if x in kwargs
+            # we don't handle this case for now
+            return False
+        return super().__contains__(key)
+
 
 class ColoAttribute(ColoProxy):
 

tests/test_fx/test_tracer/test_hf_model/test_hf_bert.py

@@ -7,36 +7,90 @@ BATCH_SIZE = 2
 SEQ_LENGHT = 16
 
 
-def test_bert():
+def trace_bert_and_compare_output(model, data_gen):
     tracer = ColoTracer()
-    config = transformers.BertConfig()
-    model = transformers.BertModel(config=config)
-
-    input_ids = torch.zeros((BATCH_SIZE, SEQ_LENGHT), dtype=torch.int64, device='meta')
-    token_type_ids = torch.zeros((BATCH_SIZE, SEQ_LENGHT), dtype=torch.int64, device='meta')
-    attention_mask = torch.zeros((BATCH_SIZE, SEQ_LENGHT), dtype=torch.int64, device='meta')
-    meta_args = dict(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask)
-
     # make sure that the model is traceable
-    graph = tracer.trace(root=model, meta_args=meta_args)
+    try:
+        kwargs = data_gen()
+        meta_args = {k: v.to('meta') for k, v in kwargs.items()}
+        graph = tracer.trace(root=model, meta_args=meta_args)
+    except Exception as e:
+        raise RuntimeError(f"Failed to trace {model.__class__.__name__}, error: {e}")
     gm = GraphModule(model, graph, model.__class__.__name__)
     gm.recompile()
 
     # check output
-    input_ids = torch.zeros((BATCH_SIZE, SEQ_LENGHT), dtype=torch.int64)
-    token_type_ids = torch.zeros((BATCH_SIZE, SEQ_LENGHT), dtype=torch.int64)
-    attention_mask = torch.zeros((BATCH_SIZE, SEQ_LENGHT), dtype=torch.int64)
+    inputs = data_gen()
 
     # must turn on eval mode to ensure the output is consistent
     gm.eval()
     model.eval()
 
     # run forward
-    fx_out = gm(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask)
-    non_fx_out = model(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask)
-    assert fx_out['last_hidden_state'].shape == non_fx_out['last_hidden_state'].shape
-    assert torch.equal(fx_out['last_hidden_state'], non_fx_out['last_hidden_state'])
+    non_fx_out = model(**inputs)
+    fx_out = gm(**inputs)
+
+    for k in non_fx_out.keys():
+        assert torch.equal(fx_out[k], non_fx_out[k]), f'{model.__class__.__name__} has incorrect output {k}'
+
+
+def test_single_sentence_bert():
+    MODEL_LIST = [
+        transformers.BertModel,
+        transformers.BertForPreTraining,
+        transformers.BertLMHeadModel,
+        transformers.BertForMaskedLM,
+        transformers.BertForSequenceClassification,
+        transformers.BertForTokenClassification,
+    ]
+
+    config = transformers.BertConfig(hidden_size=128, num_hidden_layers=2, num_attention_heads=4, intermediate_size=256)
+
+    def data_gen():
+        input_ids = torch.zeros((BATCH_SIZE, SEQ_LENGHT), dtype=torch.int64)
+        token_type_ids = torch.zeros((BATCH_SIZE, SEQ_LENGHT), dtype=torch.int64)
+        attention_mask = torch.zeros((BATCH_SIZE, SEQ_LENGHT), dtype=torch.int64)
+        meta_args = dict(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask)
+        return meta_args
+
+    for model_cls in MODEL_LIST:
+        model = model_cls(config=config)
+        trace_bert_and_compare_output(model, data_gen)
+
+
+def test_multi_sentence_bert():
+    config = transformers.BertConfig(hidden_size=128, num_hidden_layers=2, num_attention_heads=4, intermediate_size=256)
+    tokenizer = transformers.BertTokenizer.from_pretrained("bert-base-uncased")
+
+    def data_gen_for_next_sentence():
+        prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
+        next_sentence = "The sky is blue due to the shorter wavelength of blue light."
+        encoding = tokenizer(prompt, next_sentence, return_tensors="pt")
+        return encoding
+
+    model = transformers.BertForNextSentencePrediction(config)
+    trace_bert_and_compare_output(model, data_gen_for_next_sentence)
+
+    def data_gen_for_qa():
+        question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
+        inputs = tokenizer(question, text, return_tensors="pt")
+        return inputs
+
+    model = transformers.BertForQuestionAnswering(config)
+    trace_bert_and_compare_output(model, data_gen_for_qa)
+
+    def data_gen_for_mcq():
+        prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
+        choice0 = "It is eaten with a fork and a knife."
+        choice1 = "It is eaten while held in the hand."
+        encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors="pt", padding=True)
+        encoding = {k: v.unsqueeze(0) for k, v in encoding.items()}
+        return encoding
+
+    model = transformers.BertForMultipleChoice(config)
+    trace_bert_and_compare_output(model, data_gen_for_mcq)
 
 
 if __name__ == '__main__':
-    test_bert()
+    test_single_sentence_bert()
+    test_multi_sentence_bert()