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93 lines
4.2 KiB
93 lines
4.2 KiB
import torch
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import transformers
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from ..registry import ModelAttribute, model_zoo
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# ===============================
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# Register single-sentence ALBERT
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# ===============================
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BATCH_SIZE = 2
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SEQ_LENGTH = 16
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def data_gen_fn():
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input_ids = torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64)
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token_type_ids = torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64)
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attention_mask = torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64)
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return dict(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask)
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def data_gen_for_pretrain():
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inputs = data_gen_fn()
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inputs['labels'] = inputs['input_ids'].clone()
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inputs['sentence_order_label'] = torch.zeros(BATCH_SIZE, dtype=torch.int64)
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return inputs
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output_transform_fn = lambda x: x
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config = transformers.AlbertConfig(embedding_size=128,
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hidden_size=128,
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num_hidden_layers=2,
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num_attention_heads=4,
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intermediate_size=256)
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model_zoo.register(name='transformers_albert',
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model_fn=lambda: transformers.AlbertModel(config, add_pooling_layer=False),
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data_gen_fn=data_gen_fn,
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output_transform_fn=output_transform_fn,
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model_attribute=ModelAttribute(has_control_flow=True))
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model_zoo.register(name='transformers_albert_for_pretraining',
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model_fn=lambda: transformers.AlbertForPreTraining(config),
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data_gen_fn=data_gen_for_pretrain,
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output_transform_fn=lambda x: dict(loss=x.loss),
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model_attribute=ModelAttribute(has_control_flow=True))
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model_zoo.register(name='transformers_albert_for_masked_lm',
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model_fn=lambda: transformers.AlbertForMaskedLM(config),
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data_gen_fn=data_gen_fn,
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output_transform_fn=output_transform_fn,
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model_attribute=ModelAttribute(has_control_flow=True))
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model_zoo.register(name='transformers_albert_for_sequence_classification',
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model_fn=lambda: transformers.AlbertForSequenceClassification(config),
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data_gen_fn=data_gen_fn,
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output_transform_fn=output_transform_fn,
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model_attribute=ModelAttribute(has_control_flow=True))
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model_zoo.register(name='transformers_albert_for_token_classification',
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model_fn=lambda: transformers.AlbertForTokenClassification(config),
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data_gen_fn=data_gen_fn,
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output_transform_fn=output_transform_fn,
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model_attribute=ModelAttribute(has_control_flow=True))
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# ===============================
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# Register multi-sentence ALBERT
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# ===============================
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def data_gen_for_qa():
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question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
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tokenizer = transformers.BertTokenizer.from_pretrained("bert-base-uncased")
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inputs = tokenizer(question, text, return_tensors="pt")
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return inputs
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def data_gen_for_mcq():
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prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
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choice0 = "It is eaten with a fork and a knife."
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choice1 = "It is eaten while held in the hand."
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tokenizer = transformers.BertTokenizer.from_pretrained("bert-base-uncased")
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encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors="pt", padding=True)
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encoding = {k: v.unsqueeze(0) for k, v in encoding.items()}
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return encoding
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model_zoo.register(name='transformers_albert_for_question_answering',
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model_fn=lambda: transformers.AlbertForQuestionAnswering(config),
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data_gen_fn=data_gen_for_qa,
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output_transform_fn=output_transform_fn,
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model_attribute=ModelAttribute(has_control_flow=True))
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model_zoo.register(name='transformers_albert_for_multiple_choice',
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model_fn=lambda: transformers.AlbertForMultipleChoice(config),
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data_gen_fn=data_gen_for_mcq,
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output_transform_fn=output_transform_fn,
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model_attribute=ModelAttribute(has_control_flow=True))
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