def create_dataset(args):
# a simple copy of main bert.py until the dataset creation
config = BertConfig()
model = Bert(config, builder=popart.Builder())
indices, positions, segments, masks, labels = bert_add_inputs(
args, model)
inputs = [indices, positions, segments, masks, labels]
embedding_dict, positional_dict = model.get_model_embeddings()
shapeOf = model.builder.getTensorShape
inputs = reduce(chain, inputs[3:], inputs[:3])
tensor_shapes = [(tensorId, shapeOf(tensorId)) for tensorId in inputs]
dataset = get_bert_dataset(tensor_shapes,
input_file=args.input_files,
output_dir=args.output_dir,
sequence_length=args.sequence_length,
vocab_file=args.vocab_file,
vocab_length=args.vocab_length,
batch_size=args.batch_size,
batches_per_step=args.batches_per_step,
embedding_dict=embedding_dict,
positional_dict=positional_dict,
generated_data=args.generated_data,
is_training=False,
no_drop_remainder=True,
shuffle=args.shuffle,
mpi_size=args.mpi_size,
is_distributed=(args.mpi_size > 1))
return dataset
def create_dataset(args):
# a simple copy of main bert.py until the dataset creation
config = BertConfig()
model = Bert(config, builder=popart.Builder())
indices, positions, segments, masks, labels = bert_add_inputs(
args, model)
inputs = [indices, positions, segments, masks, labels]
embedding_dict, positional_dict = model.get_model_embeddings()
shapeOf = model.builder.getTensorShape
inputs = reduce(chain, inputs[3:], inputs[:3])
tensor_shapes = [(tensorId, shapeOf(tensorId)) for tensorId in inputs]
dataset = get_bert_dataset(args, tensor_shapes)
return dataset