# 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.
"""
Processor for PyTorch BERT training.
"""
import json
import os
from cerebras.modelzoo.common.registry import registry
from cerebras.modelzoo.data.common.h5_map_dataset import MLMHDF5Dataset
from cerebras.modelzoo.data.common.restartable_dataloader import (
RestartableDataLoader,
)
[docs]@registry.register_datasetprocessor("BertHDF5DataProcessor")
class BertHDF5DataProcessor:
def __init__(self, params):
self.dataset = MLMHDF5Dataset(params)
use_vsl = params.get("use_vsl", False)
features_list = {
"data": ["input_ids", "attention_mask"],
"labels": ["labels"],
}
data_params_path = os.path.join(
self.dataset.data_dir, "data_params.json"
)
self.mlm = False
with open(data_params_path, 'r') as file:
data_params = json.load(file)
dataset_params = data_params.get("dataset", None)
mlm_with_gather = dataset_params.get("mlm_with_gather", False)
training_objective = dataset_params.get("training_objective", None)
self.mlm = (
(training_objective == 'mlm')
if training_objective is not None
else False
)
if self.mlm and mlm_with_gather:
features_list["labels"].extend(
["masked_lm_positions", "masked_lm_mask"]
)
if use_vsl:
if self.dataset.by_sample:
features_list["data"].extend(["attention_span", "position_ids"])
else:
raise NotImplementedError(
"Variable sequence length (VSL) training is not "
"currently supported with 'corpus' format data. Please "
"switch to 'sample' format data to use VSL."
)
if "dataset_map_fn" in params:
self.dataset.map(params["dataset_map_fn"])
elif self.dataset.by_sample:
self.dataset.map(
lambda x: {
feature: x[key][idx]
for key, value in features_list.items()
for idx, feature in enumerate(value)
}
)
else:
raise NotImplementedError(
"MLM mode is not "
"currently supported with 'corpus' format data. Please "
"switch to 'sample' format data to use MLM."
)
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,
)