ColossalAI/tests/kit/model_zoo/transformers/bert.py

183 lines
8.6 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, 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)
# 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))