finetune.py

# coding=utf-8
# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
#
# 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.

"""Race."""

from megatron import get_args
from megatron import print_rank_0
from megatron import get_tokenizer
from megatron import mpu
from megatron.model.multiple_choice import MultipleChoice, MultipleChoiceFirstStage, MultipleChoiceIntermediateStage, MultipleChoiceLastStage
from tasks.eval_utils import accuracy_func_provider
from tasks.finetune_utils import finetune
from tasks.race.data import RaceDataset


def train_valid_datasets_provider():
    """Provide train and validation datasets."""
    args = get_args()
    tokenizer = get_tokenizer()

    train_dataset = RaceDataset('training', args.train_data,
                                tokenizer, args.seq_length)
    valid_dataset = RaceDataset('validation', args.valid_data,
                                tokenizer, args.seq_length)

    return train_dataset, valid_dataset


def model_provider():
    """Build the model."""

    print_rank_0('building multichoice model for RACE ...')
    if mpu.get_pipeline_model_parallel_world_size() > 1:
        # Determine model based on position of stage in pipeline.
        if mpu.is_pipeline_first_stage():
            model = MultipleChoiceFirstStage(num_tokentypes=2)
        elif mpu.is_pipeline_last_stage():
            model = MultipleChoiceLastStage(num_tokentypes=2)
        else:
            model = MultipleChoiceIntermediateStage(num_tokentypes=2)
    else:
        model = MultipleChoice(num_tokentypes=2)

    return model


def metrics_func_provider():
    """Privde metrics callback function."""
    args = get_args()
    tokenizer = get_tokenizer()

    def single_dataset_provider(datapath):
        name = datapath.split('RACE')[-1].strip('/').replace('/', '-')
        return RaceDataset(name, [datapath], tokenizer, args.seq_length)

    return accuracy_func_provider(single_dataset_provider)


def main():

    finetune(train_valid_datasets_provider, model_provider,
             end_of_epoch_callback_provider=metrics_func_provider)