mirror of https://github.com/hpcaitech/ColossalAI
153 lines
7.2 KiB
Python
153 lines
7.2 KiB
Python
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, 1]], 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
|
|
],
|
|
[
|
|
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
|
|
]]])
|
|
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],
|
|
[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]]])
|
|
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, 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)
|
|
|
|
|
|
# define output transform function
|
|
output_transform_fn = lambda x: x
|
|
|
|
# define loss funciton
|
|
loss_fn_for_bert_model = lambda x: x.pooler_output.mean()
|
|
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)
|
|
|
|
# register the BERT variants
|
|
model_zoo.register(name='transformers_bert',
|
|
model_fn=lambda: transformers.BertModel(config),
|
|
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))
|