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tests/kit/model_zoo/transformers/bert.py
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flybird11111
[upgrade]Upgrade transformers (#6320)
27 май 2025, 09:29
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27 май 2025, 09:29
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import torch import transformers from ..registry import ModelAttribute, model_zoo # =============================== # Register single-sentence BERT # =============================== # define data gen function def data_gen(): # Generated from following code snippet # # from transformers import BertTokenizer # input = 'Hello, my dog is cute' # tokenized_input = tokenizer(input, return_tensors='pt') # input_ids = tokenized_input['input_ids'] # attention_mask = tokenized_input['attention_mask'] # token_type_ids = tokenized_input['token_type_ids'] input_ids = torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 10140, 102]], dtype=torch.int64) token_type_ids = torch.tensor([[0, 0, 0, 0, 0, 0, 0, 0]], dtype=torch.int64) attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1, 1, 0]], dtype=torch.int64) return dict(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask) def data_gen_for_lm(): # LM data gen # the `labels` of LM is the token of the output, cause no padding, use `input_ids` as `labels` data = data_gen() data["labels"] = data["input_ids"].clone() return data def data_gen_for_pretraining(): # pretraining data gen # `next_sentence_label` is the label for next sentence prediction, 0 or 1 data = data_gen_for_lm() data["next_sentence_label"] = torch.tensor([1], dtype=torch.int64) return data def data_gen_for_sequence_classification(): # sequence classification data gen # `labels` is the label for sequence classification, 0 or 1 data = data_gen() data["labels"] = torch.tensor([1], dtype=torch.int64) return data def data_gen_for_token_classification(): # token classification data gen # `labels` is the type not the token id for token classification, 0 or 1 data = data_gen() data["labels"] = torch.tensor([[1, 0, 0, 0, 0, 0, 0, 0]], dtype=torch.int64) return data def data_gen_for_mcq(): # multiple choice question data gen # Generated from following code snippet # # 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." # data = tokenizer([prompt, prompt], [choice0, choice1], return_tensors="pt", padding=True) # data = {k: v.unsqueeze(0) for k, v in encoding.items()} # data['labels'] = torch.tensor([0], dtype=torch.int64) input_ids = torch.tensor( [ [ [ 101, 1999, 3304, 1010, 10733, 2366, 1999, 5337, 10906, 1010, 2107, 2004, 2012, 1037, 4825, 1010, 2003, 3591, 4895, 14540, 6610, 2094, 1012, 102, 2009, 2003, 8828, 2007, 1037, 9292, 1998, 1037, 5442, 1012, 102, 102, 5442, 1012, 102, 102, ], [ 101, 1999, 3304, 1010, 10733, 2366, 1999, 5337, 10906, 1010, 2107, 2004, 2012, 1037, 4825, 1010, 2003, 3591, 4895, 14540, 6610, 2094, 1012, 102, 2009, 2003, 8828, 2096, 2218, 1999, 1996, 2192, 1012, 102, 0, 0, 1012, 102, 0, 0, ], ] ] ) token_type_ids = torch.tensor( [ [ [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, ], ] ] ) attention_mask = torch.tensor( [ [ [ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ], [ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, ], ] ] ) labels = torch.tensor([0], dtype=torch.int64) return dict(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask, labels=labels) def data_gen_for_qa(): # generating data for question answering # no need for labels and use start and end position instead data = data_gen() start_positions = torch.tensor([0], dtype=torch.int64) data["start_positions"] = start_positions end_positions = torch.tensor([1], dtype=torch.int64) data["end_positions"] = end_positions return data # define output transform function output_transform_fn = lambda x: x # define loss funciton loss_fn_for_bert_model = lambda x: torch.nn.functional.mse_loss( x["last_hidden_state"], torch.ones_like(x["last_hidden_state"]) ) loss_fn = lambda x: x["loss"] config = transformers.BertConfig( hidden_size=128, num_hidden_layers=2, num_attention_heads=4, intermediate_size=256, hidden_dropout_prob=0, attention_probs_dropout_prob=0, attn_implementation="eager", ) # register the BERT variants model_zoo.register( name="transformers_bert", model_fn=lambda: transformers.BertModel(config, add_pooling_layer=False), data_gen_fn=data_gen, output_transform_fn=output_transform_fn, loss_fn=loss_fn_for_bert_model, 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_for_pretraining, output_transform_fn=output_transform_fn, loss_fn=loss_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_for_lm, output_transform_fn=output_transform_fn, loss_fn=loss_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_for_lm, output_transform_fn=output_transform_fn, loss_fn=loss_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_for_sequence_classification, output_transform_fn=output_transform_fn, loss_fn=loss_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_for_token_classification, output_transform_fn=output_transform_fn, loss_fn=loss_fn, model_attribute=ModelAttribute(has_control_flow=True), ) model_zoo.register( name="transformers_bert_for_next_sentence", model_fn=lambda: transformers.BertForNextSentencePrediction(config), data_gen_fn=data_gen_for_sequence_classification, output_transform_fn=output_transform_fn, loss_fn=loss_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, loss_fn=loss_fn, model_attribute=ModelAttribute(has_control_flow=True), ) model_zoo.register( name="transformers_bert_for_question_answering", model_fn=lambda: transformers.BertForQuestionAnswering(config), data_gen_fn=data_gen_for_qa, output_transform_fn=output_transform_fn, loss_fn=loss_fn, model_attribute=ModelAttribute(has_control_flow=True), )