[test] added transformers models to test model zoo (#3135)

tests/kit/model_zoo/transformers/bert.py

@@ -0,0 +1,88 @@
+import torch
+import transformers
+
+from ..registry import ModelAttribute, model_zoo
+
+# ===============================
+# Register single-sentence BERT
+# ===============================
+BATCH_SIZE = 2
+SEQ_LENGTH = 16
+
+
+def data_gen_fn():
+    input_ids = torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64)
+    token_type_ids = torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64)
+    attention_mask = torch.zeros((BATCH_SIZE, 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.BertConfig(hidden_size=128, num_hidden_layers=2, num_attention_heads=4, intermediate_size=256)
+
+# register the BERT variants
+model_zoo.register(name='transformers_bert',
+                   model_fn=lambda: transformers.BertModel(config),
+                   data_gen_fn=data_gen_fn,
+                   output_transform_fn=output_transform_fn,
+                   model_attribute=ModelAttribute(has_control_flow=True))
+model_zoo.register(name='transformers_bert_for_pretraining',
+                   model_fn=lambda: transformers.BertForPreTraining(config),
+                   data_gen_fn=data_gen_fn,
+                   output_transform_fn=output_transform_fn,
+                   model_attribute=ModelAttribute(has_control_flow=True))
+model_zoo.register(name='transformers_bert_lm_head_model',
+                   model_fn=lambda: transformers.BertLMHeadModel(config),
+                   data_gen_fn=data_gen_fn,
+                   output_transform_fn=output_transform_fn,
+                   model_attribute=ModelAttribute(has_control_flow=True))
+model_zoo.register(name='transformers_bert_for_masked_lm',
+                   model_fn=lambda: transformers.BertForMaskedLM(config),
+                   data_gen_fn=data_gen_fn,
+                   output_transform_fn=output_transform_fn,
+                   model_attribute=ModelAttribute(has_control_flow=True))
+model_zoo.register(name='transformers_bert_for_sequence_classification',
+                   model_fn=lambda: transformers.BertForSequenceClassification(config),
+                   data_gen_fn=data_gen_fn,
+                   output_transform_fn=output_transform_fn,
+                   model_attribute=ModelAttribute(has_control_flow=True))
+model_zoo.register(name='transformers_bert_for_token_classification',
+                   model_fn=lambda: transformers.BertForTokenClassification(config),
+                   data_gen_fn=data_gen_fn,
+                   output_transform_fn=output_transform_fn,
+                   model_attribute=ModelAttribute(has_control_flow=True))
+
+
+# ===============================
+# Register multi-sentence BERT
+# ===============================
+def data_gen_for_next_sentence():
+    tokenizer = transformers.BertTokenizer.from_pretrained("bert-base-uncased")
+    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
+
+
+def data_gen_for_mcq():
+    tokenizer = transformers.BertTokenizer.from_pretrained("bert-base-uncased")
+    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
+
+
+# register the following models
+model_zoo.register(name='transformers_bert_for_next_sentence',
+                   model_fn=lambda: transformers.BertForNextSentencePrediction(config),
+                   data_gen_fn=data_gen_for_next_sentence,
+                   output_transform_fn=output_transform_fn,
+                   model_attribute=ModelAttribute(has_control_flow=True))
+model_zoo.register(name='transformers_bert_for_mcq',
+                   model_fn=lambda: transformers.BertForMultipleChoice(config),
+                   data_gen_fn=data_gen_for_mcq,
+                   output_transform_fn=output_transform_fn,
+                   model_attribute=ModelAttribute(has_control_flow=True))