mirror of https://github.com/hpcaitech/ColossalAI
84 lines
3.1 KiB
Python
84 lines
3.1 KiB
Python
import torch
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import transformers
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from packaging import version
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from torch.utils.data import SequentialSampler
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from transformers import BertConfig, BertForSequenceClassification
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from .registry import non_distributed_component_funcs
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def get_bert_data_loader(
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n_class,
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batch_size,
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total_samples,
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sequence_length,
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device=torch.device('cpu:0'),
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is_distrbuted=False,
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):
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train_data = torch.randint(
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low=0,
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high=n_class,
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size=(total_samples, sequence_length),
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device=device,
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dtype=torch.long,
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)
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train_label = torch.randint(low=0, high=2, size=(total_samples,), device=device, dtype=torch.long)
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train_dataset = torch.utils.data.TensorDataset(train_data, train_label)
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if is_distrbuted:
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sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
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else:
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sampler = SequentialSampler(train_dataset)
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train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, sampler=sampler)
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return train_loader
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@non_distributed_component_funcs.register(name='bert')
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def get_training_components():
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hidden_dim = 8
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num_head = 4
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sequence_length = 12
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num_layer = 2
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vocab_size = 32
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def bert_model_builder(checkpoint: bool = False):
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config = BertConfig(vocab_size=vocab_size,
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gradient_checkpointing=checkpoint,
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hidden_size=hidden_dim,
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intermediate_size=hidden_dim * 4,
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num_attention_heads=num_head,
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max_position_embeddings=sequence_length,
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num_hidden_layers=num_layer,
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hidden_dropout_prob=0.,
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attention_probs_dropout_prob=0.)
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print('building BertForSequenceClassification model')
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# adapting huggingface BertForSequenceClassification for single unitest calling interface
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class ModelAaptor(BertForSequenceClassification):
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def forward(self, input_ids, labels):
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"""
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inputs: data, label
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outputs: loss
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"""
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return super().forward(input_ids=input_ids, labels=labels)[0]
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model = ModelAaptor(config)
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if checkpoint and version.parse(transformers.__version__) >= version.parse("4.11.0"):
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model.gradient_checkpointing_enable()
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return model
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trainloader = get_bert_data_loader(n_class=vocab_size,
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batch_size=2,
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total_samples=10000,
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sequence_length=sequence_length,
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is_distrbuted=True)
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testloader = get_bert_data_loader(n_class=vocab_size,
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batch_size=2,
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total_samples=10000,
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sequence_length=sequence_length,
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is_distrbuted=True)
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criterion = None
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return bert_model_builder, trainloader, testloader, torch.optim.Adam, criterion
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