# Copyright 2022 Cerebras Systems.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from cerebras.modelzoo.common.registry import registry
from cerebras.modelzoo.data.common.h5_map_dataset import MultiModalHDF5Dataset
from cerebras.modelzoo.data.common.restartable_dataloader import (
RestartableDataLoader,
)
[docs]@registry.register_datasetprocessor("LlavaHDF5MapDataProcessor")
class LlavaHDF5MapDataProcessor:
def __init__(self, params):
self.dataset = MultiModalHDF5Dataset(params)
if not self.dataset.by_sample:
raise NotImplementedError(
"Training with 'corpus' format data is not currently supported "
"Please switch to 'sample' format."
)
if params.get("use_vsl", False):
raise NotImplementedError(
"Variable sequence length (VSL) training is not"
"currently supported."
)
features_list = [
"text_input_ids", # input_ids <-> text_input_ids
"loss_mask", # input_mask <-> loss_mask
"labels",
"key_padding_mask", # attention_mask <-> key_padding_mask
]
if "dataset_map_fn" in params:
self.dataset.map(params["dataset_map_fn"])
else:
self.dataset.map(
lambda x: {
feature: x[idx] for idx, feature in enumerate(features_list)
}
)
self.num_workers = params.get("num_workers", 0)
self.prefetch_factor = params.get("prefetch_factor", 10)
self.persistent_workers = params.get("persistent_workers", True)
if not self.num_workers:
self.prefetch_factor = None # the default value in DataLoader
self.persistent_workers = False
def create_dataloader(self):
return RestartableDataLoader(
self.dataset,
batch_sampler=self.dataset.sampler,
num_workers=self.num_workers,
prefetch_factor=self.prefetch_factor,
persistent_workers=self.persistent_workers,
)