[New Sample] Add test example to extract paddlenlp models and add 7 samples. (#249)

* Add test example to extract paddle nlp samples.

* Update test codes.

* Add gpt2-medium-en.

* Update test code.

* Add hash file for gpt2.

* Add llama-7b.

* Add bert-base.

* Add ernie-1.0.

* Add ernie-3.0.

* Add nezha-base-chinese.

* Add roformer.

Author: Xreki

graph_net/test/nlp_model_getter.py

@@ -0,0 +1,109 @@
+def get_auto_model_and_inputs(model_name, text, dtype):
+    from paddlenlp.transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
+
+    config = AutoConfig.from_pretrained(model_name)
+    model = AutoModelForCausalLM.from_config(config, dtype=dtype)
+    model = model.eval()
+
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    tokenizer.pad_token = tokenizer.eos_token
+    inputs = tokenizer(
+        text, return_tensors="pd", padding=True, truncation=True, max_length=2048
+    )
+    return model, inputs
+
+
+def get_bert_model_and_inputs(model_name, text, dtype):
+    from paddlenlp.transformers import BertModel, BertTokenizer
+
+    model = BertModel.from_pretrained(model_name)
+    model.eval()
+
+    tokenizer = BertTokenizer.from_pretrained(model_name)
+    inputs = tokenizer(text, return_tensors="pd")
+    return model, inputs
+
+
+def get_convbert_model_and_inputs(model_name, text, dtype):
+    from paddlenlp.transformers import ConvBertModel as ModelClass
+    from paddlenlp.transformers import ConvBertTokenizer as TokenizerClass
+
+    model = ModelClass.from_pretrained(model_name)
+    model.eval()
+
+    tokenizer = TokenizerClass.from_pretrained(model_name)
+    inputs = tokenizer(text, return_tensors="pd")
+    return model, inputs
+
+
+def get_ernie_model_and_inputs(model_name, text, dtype):
+    from paddlenlp.transformers import ErnieModel, ErnieTokenizer
+
+    model = ErnieModel.from_pretrained(model_name)
+    tokenizer = ErnieTokenizer.from_pretrained(model_name)
+    inputs = tokenizer(text, return_tensors="pd")
+    return model, inputs
+
+
+def get_ernie_m_model_and_inputs(model_name, text, dtype):
+    from paddlenlp.transformers import ErnieMModel as ModelClass
+    from paddlenlp.transformers import ErnieMTokenizer as TokenizerClass
+
+    model = ModelClass.from_pretrained(model_name)
+    model.eval()
+
+    tokenizer = TokenizerClass.from_pretrained(model_name)
+    inputs = tokenizer(text, return_tensors="pd")
+    return model, inputs
+
+
+def get_gpt_model_and_inputs(model_name, text, dtype):
+    from paddlenlp.transformers import GPTModel, GPTTokenizer
+
+    model = GPTModel.from_pretrained(model_name)
+    model.eval()
+
+    tokenizer = GPTTokenizer.from_pretrained(model_name)
+    inputs = tokenizer(text, return_tensors="pd")
+    inputs.pop("token_type_ids")
+    return model, inputs
+
+
+def get_nezha_model_and_inputs(model_name, text, dtype):
+    from paddlenlp.transformers import NeZhaModel as ModelClass
+    from paddlenlp.transformers import NeZhaTokenizer as TokenizerClass
+
+    model = ModelClass.from_pretrained(model_name)
+    tokenizer = TokenizerClass.from_pretrained(model_name)
+    inputs = tokenizer(text, return_tensors="pd")
+    return model, inputs
+
+
+def get_ppminilm_model_and_inputs(model_name, text, dtype):
+    from paddlenlp.transformers import PPMiniLMModel as ModelClass
+    from paddlenlp.transformers import PPMiniLMTokenizer as TokenizerClass
+
+    model = ModelClass.from_pretrained(model_name)
+    tokenizer = TokenizerClass.from_pretrained(model_name)
+    inputs = tokenizer(text, return_tensors="pd")
+    return model, inputs
+
+
+def get_reformer_model_and_inputs(model_name, text, dtype):
+    from paddlenlp.transformers import RoFormerModel as ModelClass
+    from paddlenlp.transformers import RoFormerTokenizer as TokenizerClass
+
+    model = ModelClass.from_pretrained(model_name)
+    tokenizer = TokenizerClass.from_pretrained(model_name)
+    inputs = tokenizer(text, return_tensors="pd")
+    return model, inputs
+
+
+def get_skep_model_and_inputs(model_name, text, dtype):
+    from paddlenlp.transformers import SkepModel as ModelClass
+    from paddlenlp.transformers import SkepTokenizer as TokenizerClass
+
+    model = ModelClass.from_pretrained(model_name)
+    tokenizer = TokenizerClass.from_pretrained(model_name)
+    inputs = tokenizer(text, return_tensors="pd")
+    return model, inputs