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

93 lines
4.2 KiB
Python

import torch
import transformers
from ..registry import ModelAttribute, model_zoo
# ===============================
# Register single-sentence ALBERT
# ===============================
BATCH_SIZE = 2
SEQ_LENGTH = 16
def data_gen_fn():
input_ids = torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64)
token_type_ids = torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64)
attention_mask = torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64)
return dict(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask)
def data_gen_for_pretrain():
inputs = data_gen_fn()
inputs['labels'] = inputs['input_ids'].clone()
inputs['sentence_order_label'] = torch.zeros(BATCH_SIZE, dtype=torch.int64)
return inputs
output_transform_fn = lambda x: x
config = transformers.AlbertConfig(embedding_size=128,
hidden_size=128,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=256)
model_zoo.register(name='transformers_albert',
model_fn=lambda: transformers.AlbertModel(config, add_pooling_layer=False),
data_gen_fn=data_gen_fn,
output_transform_fn=output_transform_fn,
model_attribute=ModelAttribute(has_control_flow=True))
model_zoo.register(name='transformers_albert_for_pretraining',
model_fn=lambda: transformers.AlbertForPreTraining(config),
data_gen_fn=data_gen_for_pretrain,
output_transform_fn=lambda x: dict(loss=x.loss),
model_attribute=ModelAttribute(has_control_flow=True))
model_zoo.register(name='transformers_albert_for_masked_lm',
model_fn=lambda: transformers.AlbertForMaskedLM(config),
data_gen_fn=data_gen_fn,
output_transform_fn=output_transform_fn,
model_attribute=ModelAttribute(has_control_flow=True))
model_zoo.register(name='transformers_albert_for_sequence_classification',
model_fn=lambda: transformers.AlbertForSequenceClassification(config),
data_gen_fn=data_gen_fn,
output_transform_fn=output_transform_fn,
model_attribute=ModelAttribute(has_control_flow=True))
model_zoo.register(name='transformers_albert_for_token_classification',
model_fn=lambda: transformers.AlbertForTokenClassification(config),
data_gen_fn=data_gen_fn,
output_transform_fn=output_transform_fn,
model_attribute=ModelAttribute(has_control_flow=True))
# ===============================
# Register multi-sentence ALBERT
# ===============================
def data_gen_for_qa():
question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
tokenizer = transformers.BertTokenizer.from_pretrained("bert-base-uncased")
inputs = tokenizer(question, text, return_tensors="pt")
return inputs
def data_gen_for_mcq():
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."
tokenizer = transformers.BertTokenizer.from_pretrained("bert-base-uncased")
encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors="pt", padding=True)
encoding = {k: v.unsqueeze(0) for k, v in encoding.items()}
return encoding
model_zoo.register(name='transformers_albert_for_question_answering',
model_fn=lambda: transformers.AlbertForQuestionAnswering(config),
data_gen_fn=data_gen_for_qa,
output_transform_fn=output_transform_fn,
model_attribute=ModelAttribute(has_control_flow=True))
model_zoo.register(name='transformers_albert_for_multiple_choice',
model_fn=lambda: transformers.AlbertForMultipleChoice(config),
data_gen_fn=data_gen_for_mcq,
output_transform_fn=output_transform_fn,
model_attribute=ModelAttribute(has_control_flow=True))