Source code for cerebras.modelzoo.data_preparation.nlp.hdf5_preprocessing.create_hdf5_dataset

# 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.

"""
Script that generates a dataset in HDF5 format for GPT Models.
"""

import logging
import os
import sys
from multiprocessing import cpu_count
from pathlib import Path

sys.path.append(os.path.join(os.path.dirname(__file__), "../../../"))
from cerebras.modelzoo.common.utils.utils import check_and_create_output_dirs
from cerebras.modelzoo.data_preparation.nlp.hdf5_preprocessing.utils import (
    dump_args,
    dump_result,
    get_files,
    get_params,
    get_verification_args,
    multimodal_add_image_patch_start_idx,
    process_dataset,
    verify_saved_hdf5_files_mp,
)

# Custom preprocessors
from cerebras.modelzoo.data_preparation.nlp.hdf5_preprocessing.hdf5_curation_corpus_preprocessor import (  # noqa
    CurationCorpusPreprocessor,
)
from cerebras.modelzoo.data_preparation.nlp.hdf5_preprocessing.hdf5_nlg_preprocessor import (  # noqa
    NLGPreprocessor,
)

from cerebras.modelzoo.data_preparation.nlp.hdf5_preprocessing.hdf5_dataset_preprocessors import (  # noqa
    FIMDataPreprocessor,
    LlavaPhaseOnePreprocessor,
    LlavaPhaseTwoPreprocessor,
    LMDataPreprocessor,
    SummarizationPreprocessor,
    VSLLMDataPreprocessor,
    VSLSummarizationPreprocessor,
)

logging.basicConfig()
logger = logging.getLogger(__file__)
logger.setLevel(logging.INFO)


[docs]def main(): """Main function for execution.""" params = get_params(desc="Create HDF5 dataset for language models") logger.warning( f"'create_hdf5_dataset.py script has been deprecated and will be removed in the next release version.\ Please use script from `data_preprocessing` folder under `cerebras.modelzoo.data_preparation.data_preprocessing`.\ Reach out to `[email protected] ` for any concerns or support." ) output_dir = params["setup"].get("output_dir", "./data_dir/") if not params["processing"].get("resume_from_checkpoint", False): check_and_create_output_dirs(output_dir, filetype="h5") logger.info(f"\nWriting data to {output_dir}.") json_params_file = os.path.join(output_dir, "data_params.json") dump_args(params, json_params_file) metadata_files = params["setup"].pop("metadata_files", None) if metadata_files: metadata_files = metadata_files.split(",") input_dir = params["setup"].pop("input_dir", None) input_files = get_files(input_dir=input_dir, metadata_files=metadata_files) processes = params["setup"].pop("processes", 0) if processes == 0: processes = cpu_count() ds_processor = params["setup"].pop( "dataset_processor", "LMDataPreprocessor" ) module_name = params["setup"].pop("module", None) dataset_processor = getattr(sys.modules[__name__], ds_processor)(params) unused_setup_params = [ key for key in params["setup"].keys() if key != "output_dir" ] if unused_setup_params: logger.warning( "The following setup params are unused: " + ", ".join(unused_setup_params) ) unused_dataset_params = [key for key in params["dataset"].keys()] if unused_dataset_params: logger.warning( "The following dataset params are unused: " + ", ".join(unused_dataset_params) ) ## Set this to avoid the warning - The current process just got forked. Disabling parallelism to avoid deadlocks... # To disable this warning, please explicitly set TOKENIZERS_PARALLELISM=(true | false) os.environ["TOKENIZERS_PARALLELISM"] = "false" results = process_dataset(input_files, dataset_processor, processes) vocab_size = dataset_processor.get_vocab_size() logger.info( f"\nFinished writing data to {output_dir}." f" Runtime arguments and outputs can be found at {json_params_file}." ) logger.info(f"Verifying the converted dataset at: {output_dir}") output_files = list(Path(output_dir).glob("*.h5")) verification_args = get_verification_args( processes, dataset_processor ) # for verify_saved_hdf5_files_mp dataset_stats = verify_saved_hdf5_files_mp( output_files, verification_args, vocab_size ) logger.info("Done verifying the converted dataset.") dump_result( results, dataset_stats, json_params_file, dataset_processor.eos_id, dataset_processor.pad_id, vocab_size, ) multimodal_add_image_patch_start_idx( json_params_file, dataset_processor, )
if __name__ == "__main__": main()