model_base.py

# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.

"""Module of the model-benchmark base class."""

from abc import abstractmethod

from superbench.common.utils import logger
from superbench.benchmarks import Precision, ModelAction, BenchmarkType, ReturnCode
from superbench.benchmarks.base import Benchmark
from superbench.benchmarks.context import Enum


class DistributedImpl(Enum):
    """The Enum class representing different distributed implementations."""
    DDP = 'ddp'
    MIRRORED = 'mirrored'
    MW_MIRRORED = 'multiworkermirrored'
    PS = 'parameterserver'
    HOROVOD = 'horovod'


class DistributedBackend(Enum):
    """The Enum class representing different distributed backends."""
    NCCL = 'nccl'
    MPI = 'mpi'
    GLOO = 'gloo'


class ModelBenchmark(Benchmark):
    """The base class of E2E model benchmarks."""
    def __init__(self, name, parameters=''):
        """Constructor.

        Args:
            name (str): benchmark name.
            parameters (str): benchmark parameters.
        """
        super().__init__(name, parameters)

        self._benchmark_type = BenchmarkType.MODEL
        self._world_size = 1
        self._local_rank = None
        self._dataset = None
        self._dataloader = None
        self._model = None
        self._optimizer_type = None
        self._optimizer = None
        self._loss_fn = None
        self._target = None
        self._supported_precision = []

    def add_parser_arguments(self):
        """Add the specified arguments."""
        super().add_parser_arguments()

        self._parser.add_argument(
            '--num_warmup',
            type=int,
            default=64,
            required=False,
            help='The number of warmup step',
        )
        self._parser.add_argument(
            '--num_steps',
            type=int,
            default=2048,
            required=False,
            help='The number of test step',
        )
        self._parser.add_argument(
            '--batch_size',
            type=int,
            default=32,
            required=False,
            help='The number of batch size',
        )
        self._parser.add_argument(
            '--precision',
            type=Precision,
            default=[Precision.FLOAT32, Precision.FLOAT16],
            nargs='+',
            required=False,
            help='Model precision. E.g. {}.'.format(' '.join(Precision.get_values())),
        )
        self._parser.add_argument(
            '--model_action',
            type=ModelAction,
            default=[ModelAction.TRAIN],
            nargs='+',
            required=False,
            help='Benchmark model process. E.g. {}.'.format(' '.join(ModelAction.get_values())),
        )
        self._parser.add_argument(
            '--distributed_impl',
            type=DistributedImpl,
            default=None,
            required=False,
            help='Distributed implementations. E.g. {}'.format(' '.join(DistributedImpl.get_values())),
        )

        self._parser.add_argument(
            '--distributed_backend',
            type=DistributedBackend,
            default=None,
            required=False,
            help='Distributed backends. E.g. {}'.format(' '.join(DistributedBackend.get_values())),
        )

    @abstractmethod
    def _init_distributed_setting(self):
        """Initialize the distributed library and bind the worker to GPU.

        Return:
            True if distributed library is initialized successfully.
        """
        pass

    @abstractmethod
    def _generate_dataset(self):
        """Generate dataset for benchmarking according to shape info.

        Return:
            True if dataset is created successfully.
        """
        pass

    @abstractmethod
    def _init_dataloader(self):
        """Initialize the dataloader.

        Return:
            True if dataloader is created successfully.
        """
        pass

    def _preprocess(self):
        """Preprocess/preparation operations before the benchmarking.

        Return:
            True if _preprocess() succeed.
        """
        if not super()._preprocess():
            return False

        if not self._init_distributed_setting():
            self._result.set_return_code(ReturnCode.DISTRIBUTED_SETTING_INIT_FAILURE)
            return False

        if not self._generate_dataset():
            self._result.set_return_code(ReturnCode.DATASET_GENERATION_FAILURE)
            return False

        if not self._init_dataloader():
            self._result.set_return_code(ReturnCode.DATALOADER_INIT_FAILURE)
            return False

        return True

    @abstractmethod
    def _create_optimizer(self):
        """Create the optimzier instance used for training and wrap with distributed library if need.

        Return:
            True if optimizer instance is created successfully.
        """
        pass

    @abstractmethod
    def _create_model(self, precision):
        """Construct the model for benchmarking.

        Args:
            precision (Precision): precision of model and input data, such as float32, float16.
        """
        pass

    def __train(self, precision):
        """Launch the training benchmark.

        Args:
            precision (Precision): precision of model and input data, such as float32, float16.

        Return:
            True if step_times list is not empty.
        """
        if not self._create_model(precision):
            self._result.set_return_code(ReturnCode.MODEL_CREATION_FAILURE)
            return False

        if not self._create_optimizer():
            self._result.set_return_code(ReturnCode.OPTIMIZER_CREATION_FAILURE)
            return False

        # The unit of step time should be millisecond.
        step_times = self._train_step(precision)
        if len(step_times) == 0:
            logger.error(
                'Step time list for training is empty - round: {}, model: {}, precision: {}.'.format(
                    self._curr_run_index, self._name, precision
                )
            )
            return False

        average_time = sum(step_times) / len(step_times)
        logger.info(
            'Average train time - round: {}, model: {}, precision: {}, step time: {:.6f} ms.'.format(
                self._curr_run_index, self._name, precision, average_time
            )
        )

        self.__process_model_result(ModelAction.TRAIN, precision, step_times)
        return True

    def __inference(self, precision):
        """Launch the inference benchmark.

        Args:
            precision (Precision): precision of model and input data, such as float32, float16.

        Return:
            True if step_times list is not empty.
        """
        self._create_model(precision)
        # The unit of step time should be millisecond.
        step_times = self._inference_step(precision)
        if len(step_times) == 0:
            logger.error(
                'Step time list for inference is empty - round: {}, model: {}, precision: {}.'.format(
                    self._curr_run_index, self._name, precision
                )
            )
            return False

        average_time = sum(step_times) / len(step_times)
        logger.info(
            'Average inference time - round: {}, model: {}, precision: {}, step time: {:.6f} ms.'.format(
                self._curr_run_index, self._name, precision, average_time
            )
        )

        self.__process_model_result(ModelAction.INFERENCE, precision, step_times)
        return True

    @abstractmethod
    def _train_step(self, precision):
        """Define the training process.

        Args:
            precision (Precision): precision of model and input data, such as float32, float16.

        Return:
            The step-time list of every training step.
        """
        pass

    @abstractmethod
    def _inference_step(self, precision):
        """Define the inference process.

        Args:
            precision (Precision): precision of model and input data,
              such as float32, float16.

        Return:
            The latency list of every inference operation.
        """
        pass

    def _benchmark(self):
        """Implementation for benchmarking.

        Return:
            True if run benchmark successfully.
        """
        precision_need_to_run = list()
        for precision in self._args.precision:
            # Check if the precision is supported or not.
            if precision not in self._supported_precision:
                logger.warning(
                    'Can not run with specified precision - model: {}, supprted precision: {}, specified precision: {}'.
                    format(self._name, ' '.join([p.value for p in self._supported_precision]), precision)
                )
            else:
                precision_need_to_run.append(precision)

        if len(precision_need_to_run) == 0:
            self._result.set_return_code(ReturnCode.NO_SUPPORTED_PRECISION)
            return False

        for precision in precision_need_to_run:
            for model_action in self._args.model_action:
                if model_action == ModelAction.TRAIN:
                    if not self.__train(precision):
                        self._result.set_return_code(ReturnCode.MODEL_TRAIN_FAILURE)
                        return False
                elif model_action == ModelAction.INFERENCE:
                    if not self.__inference(precision):
                        self._result.set_return_code(ReturnCode.MODEL_INFERENCE_FAILURE)
                        return False
                else:
                    logger.warning(
                        'Model action has no implementation yet - model: {}, model_action: {}'.format(
                            self._name, model_action
                        )
                    )

        return True

    def __process_model_result(self, model_action, precision, step_times):
        """Function to process raw results and save the summarized results.

        Args:
            model_action (ModelAction): train or inference.
            precision (Precision): precision of model and input data, such as float32, float16.
            step_times (list): The step time list of every training/inference step, unit is millisecond.
        """
        metric = 'steptime_{}_{}'.format(model_action.value, precision.value)
        self._result.add_raw_data(metric, step_times)
        avg = sum(step_times) / len(step_times)
        self._result.add_result(metric, avg)

        # The unit of step time is millisecond, use it to calculate the throughput with the unit samples/sec.
        millisecond_per_second = 1000
        throughput = [millisecond_per_second / step_time * self._args.batch_size for step_time in step_times]
        metric = 'throughput_{}_{}'.format(model_action.value, precision.value)
        self._result.add_raw_data(metric, throughput)
        avg = sum(throughput) / len(throughput)
        self._result.add_result(metric, avg)

    @abstractmethod
    def _cal_params_size(self):
        """Calculate the parameters scale of the model.

        Return:
            The count of trainable parameters.
        """
        pass

    def print_env_info(self):
        """Print environments or dependencies information."""
        # TODO: will implement it when add real benchmarks in the future.
        pass