mirror of https://github.com/hpcaitech/ColossalAI
89 lines
4.2 KiB
Python
89 lines
4.2 KiB
Python
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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 BERT
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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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output_transform_fn = lambda x: x
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config = transformers.BertConfig(hidden_size=128, num_hidden_layers=2, num_attention_heads=4, intermediate_size=256)
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# register the BERT variants
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model_zoo.register(name='transformers_bert',
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model_fn=lambda: transformers.BertModel(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_bert_for_pretraining',
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model_fn=lambda: transformers.BertForPreTraining(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_bert_lm_head_model',
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model_fn=lambda: transformers.BertLMHeadModel(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_bert_for_masked_lm',
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model_fn=lambda: transformers.BertForMaskedLM(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_bert_for_sequence_classification',
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model_fn=lambda: transformers.BertForSequenceClassification(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_bert_for_token_classification',
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model_fn=lambda: transformers.BertForTokenClassification(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 BERT
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# ===============================
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def data_gen_for_next_sentence():
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tokenizer = transformers.BertTokenizer.from_pretrained("bert-base-uncased")
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prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
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next_sentence = "The sky is blue due to the shorter wavelength of blue light."
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encoding = tokenizer(prompt, next_sentence, return_tensors="pt")
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return encoding
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def data_gen_for_mcq():
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tokenizer = transformers.BertTokenizer.from_pretrained("bert-base-uncased")
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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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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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# register the following models
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model_zoo.register(name='transformers_bert_for_next_sentence',
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model_fn=lambda: transformers.BertForNextSentencePrediction(config),
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data_gen_fn=data_gen_for_next_sentence,
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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_bert_for_mcq',
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model_fn=lambda: transformers.BertForMultipleChoice(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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