mirror of
https://github.com/hpcaitech/ColossalAI.git
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27061426f7
* [gemini] remove distributed-related part from colotensor (#4379) * [gemini] remove process group dependency * [gemini] remove tp part from colo tensor * [gemini] patch inplace op * [gemini] fix param op hook and update tests * [test] remove useless tests * [test] remove useless tests * [misc] fix requirements * [test] fix model zoo * [test] fix model zoo * [test] fix model zoo * [test] fix model zoo * [test] fix model zoo * [misc] update requirements * [gemini] refactor gemini optimizer and gemini ddp (#4398) * [gemini] update optimizer interface * [gemini] renaming gemini optimizer * [gemini] refactor gemini ddp class * [example] update gemini related example * [example] update gemini related example * [plugin] fix gemini plugin args * [test] update gemini ckpt tests * [gemini] fix checkpoint io * [example] fix opt example requirements * [example] fix opt example * [example] fix opt example * [example] fix opt example * [gemini] add static placement policy (#4443) * [gemini] add static placement policy * [gemini] fix param offload * [test] update gemini tests * [plugin] update gemini plugin * [plugin] update gemini plugin docstr * [misc] fix flash attn requirement * [test] fix gemini checkpoint io test * [example] update resnet example result (#4457) * [example] update bert example result (#4458) * [doc] update gemini doc (#4468) * [example] update gemini related examples (#4473) * [example] update gpt example * [example] update dreambooth example * [example] update vit * [example] update opt * [example] update palm * [example] update vit and opt benchmark * [hotfix] fix bert in model zoo (#4480) * [hotfix] fix bert in model zoo * [test] remove chatglm gemini test * [test] remove sam gemini test * [test] remove vit gemini test * [hotfix] fix opt tutorial example (#4497) * [hotfix] fix opt tutorial example * [hotfix] fix opt tutorial example
183 lines
8.6 KiB
Python
183 lines
8.6 KiB
Python
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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# define data gen function
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def data_gen():
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# Generated from following code snippet
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#
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# from transformers import BertTokenizer
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# input = 'Hello, my dog is cute'
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# tokenized_input = tokenizer(input, return_tensors='pt')
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# input_ids = tokenized_input['input_ids']
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# attention_mask = tokenized_input['attention_mask']
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# token_type_ids = tokenized_input['token_type_ids']
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input_ids = torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 10140, 102]], dtype=torch.int64)
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token_type_ids = torch.tensor([[0, 0, 0, 0, 0, 0, 0, 0]], dtype=torch.int64)
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attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1, 1, 0]], 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_lm():
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# LM data gen
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# the `labels` of LM is the token of the output, cause no padding, use `input_ids` as `labels`
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data = data_gen()
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data['labels'] = data['input_ids'].clone()
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return data
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def data_gen_for_pretraining():
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# pretraining data gen
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# `next_sentence_label` is the label for next sentence prediction, 0 or 1
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data = data_gen_for_lm()
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data['next_sentence_label'] = torch.tensor([1], dtype=torch.int64)
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return data
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def data_gen_for_sequence_classification():
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# sequence classification data gen
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# `labels` is the label for sequence classification, 0 or 1
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data = data_gen()
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data['labels'] = torch.tensor([1], dtype=torch.int64)
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return data
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def data_gen_for_token_classification():
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# token classification data gen
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# `labels` is the type not the token id for token classification, 0 or 1
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data = data_gen()
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data['labels'] = torch.tensor([[1, 0, 0, 0, 0, 0, 0, 0]], dtype=torch.int64)
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return data
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def data_gen_for_mcq():
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# multiple choice question data gen
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# Generated from following code snippet
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#
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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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# data = tokenizer([prompt, prompt], [choice0, choice1], return_tensors="pt", padding=True)
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# data = {k: v.unsqueeze(0) for k, v in encoding.items()}
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# data['labels'] = torch.tensor([0], dtype=torch.int64)
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input_ids = torch.tensor([[[
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101, 1999, 3304, 1010, 10733, 2366, 1999, 5337, 10906, 1010, 2107, 2004, 2012, 1037, 4825, 1010, 2003, 3591,
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4895, 14540, 6610, 2094, 1012, 102, 2009, 2003, 8828, 2007, 1037, 9292, 1998, 1037, 5442, 1012, 102, 102, 5442,
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1012, 102, 102
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],
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[
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101, 1999, 3304, 1010, 10733, 2366, 1999, 5337, 10906, 1010, 2107, 2004, 2012, 1037,
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4825, 1010, 2003, 3591, 4895, 14540, 6610, 2094, 1012, 102, 2009, 2003, 8828, 2096,
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2218, 1999, 1996, 2192, 1012, 102, 0, 0, 1012, 102, 0, 0
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]]])
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token_type_ids = torch.tensor([[[
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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,
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1, 1, 1
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],
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[
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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,
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1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0
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]]])
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attention_mask = torch.tensor([[[
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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,
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1, 1, 1
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],
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[
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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,
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1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0
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]]])
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labels = torch.tensor([0], dtype=torch.int64)
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return dict(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask, labels=labels)
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def data_gen_for_qa():
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# generating data for question answering
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# no need for labels and use start and end position instead
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data = data_gen()
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start_positions = torch.tensor([0], dtype=torch.int64)
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data['start_positions'] = start_positions
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end_positions = torch.tensor([1], dtype=torch.int64)
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data['end_positions'] = end_positions
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return data
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# define output transform function
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output_transform_fn = lambda x: x
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# define loss funciton
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loss_fn_for_bert_model = lambda x: torch.nn.functional.mse_loss(x.last_hidden_state, torch.ones_like(x.last_hidden_state
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))
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loss_fn = lambda x: x.loss
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config = transformers.BertConfig(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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hidden_dropout_prob=0,
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attention_probs_dropout_prob=0)
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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, add_pooling_layer=False),
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data_gen_fn=data_gen,
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output_transform_fn=output_transform_fn,
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loss_fn=loss_fn_for_bert_model,
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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_for_pretraining,
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output_transform_fn=output_transform_fn,
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loss_fn=loss_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_for_lm,
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output_transform_fn=output_transform_fn,
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loss_fn=loss_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_for_lm,
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output_transform_fn=output_transform_fn,
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loss_fn=loss_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_for_sequence_classification,
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output_transform_fn=output_transform_fn,
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loss_fn=loss_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_for_token_classification,
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output_transform_fn=output_transform_fn,
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loss_fn=loss_fn,
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model_attribute=ModelAttribute(has_control_flow=True))
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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_sequence_classification,
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output_transform_fn=output_transform_fn,
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loss_fn=loss_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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loss_fn=loss_fn,
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model_attribute=ModelAttribute(has_control_flow=True))
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model_zoo.register(name='transformers_bert_for_question_answering',
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model_fn=lambda: transformers.BertForQuestionAnswering(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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loss_fn=loss_fn,
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model_attribute=ModelAttribute(has_control_flow=True))
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