Source code for mindspore.nn.transformer.op_parallel_config

# Copyright 2021 Huawei Technologies Co., Ltd
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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# ============================================================================
"""
Parallel Config for the Parallel Training
This is an experimental interface that is subject to change and/or deletion.
"""
from __future__ import absolute_import

from mindspore._checkparam import Validator
from mindspore import context
import mindspore.communication.management as D
from mindspore.context import ParallelMode
from mindspore.parallel._utils import _get_parallel_mode
from mindspore import log as logger

__all__ = [
    "OpParallelConfig"
]


class _Config:
    r""" A basic class of the configure"""

    def __str__(self):
        info = "[ParallelConfig]" + '\n'
        for k, v in self.__dict__.items():
            var_info = "{}:{}\n".format(k, v)
            info += var_info
        return info


class MoEParallelConfig(_Config):
    r"""
        MoEParallelConfig for MoE structure, which includes setting data parallel, model parallel and expert parallel.

        Args:
            data_parallel (int): The data parallel way. Default: 1
            model_parallel (int): The model parallel way. Default: 1
            expert_parallel (int): The expert parallel way. Default: 1
        Supported Platforms:
            ``Ascend``
    """

    def __init__(self, data_parallel=1, model_parallel=1, expert_parallel=1):
        Validator.check_positive_int(data_parallel, "data_parallel")
        Validator.check_positive_int(model_parallel, "model_parallel")
        Validator.check_positive_int(expert_parallel, "expert_parallel")
        self._dpmp = OpParallelConfig(data_parallel=data_parallel, model_parallel=model_parallel)
        self.expert_parallel = expert_parallel

    @property
    def data_parallel(self):
        return self._dpmp.data_parallel

    @data_parallel.setter
    def data_parallel(self, value):
        Validator.check_positive_int(value, "data_parallel")
        self._dpmp.data_parallel = value

    @property
    def model_parallel(self):
        return self._dpmp.model_parallel

    @model_parallel.setter
    def model_parallel(self, value):
        Validator.check_positive_int(value, "model_parallel")
        self._dpmp.model_parallel = value

    @property
    def expert_parallel(self):
        return self._expert_parallel

    @expert_parallel.setter
    def expert_parallel(self, value):
        Validator.check_positive_int(value, "expert_parallel")
        self._expert_parallel = value

    @property
    def dpmp(self):
        """ Get the configuration for dpmp """
        return self._dpmp


[文档]class OpParallelConfig(_Config): r""" OpParallelConfig for the setting data parallel and model parallel. Args: data_parallel (int): The data parallel way. Default: 1 model_parallel (int): The model parallel way. Default: 1 Supported Platforms: ``Ascend`` ``GPU`` Examples: >>> from mindspore.nn.transformer import OpParallelConfig >>> config=OpParallelConfig(data_parallel=1, model_parallel=1) """ def __init__(self, data_parallel=1, model_parallel=1): Validator.check_positive_int(data_parallel, "data_parallel") Validator.check_positive_int(model_parallel, "model_parallel") self.data_parallel = data_parallel self.model_parallel = model_parallel @property def data_parallel(self): return self._data_parallel @data_parallel.setter def data_parallel(self, value): Validator.check_positive_int(value, "data_parallel") self._data_parallel = value @property def model_parallel(self): return self._model_parallel @model_parallel.setter def model_parallel(self, value): Validator.check_positive_int(value, "model_parallel") self._model_parallel = value
class _PipeLineConfig(_Config): r""" PPConfig for the setting data parallel, model parallel Args: pipeline_stage (int): The number of the pipeline stages. Default: 1 micro_batch_num (int): The model parallel way. Default: 1 Supported Platforms: ``Ascend`` ``GPU`` Examples: >>> config=_PipeLineConfig(pipeline_stage=1, micro_batch_num=1) """ def __init__(self, pipeline_stage=1, micro_batch_num=1): Validator.check_positive_int(pipeline_stage, "pipeline_stage") Validator.check_positive_int(micro_batch_num, "micro_batch_num") self.pipeline_stage = pipeline_stage self.micro_batch_num = micro_batch_num @property def pipeline_stage(self): return self._pipeline_stage @pipeline_stage.setter def pipeline_stage(self, value): Validator.check_positive_int(value, "pipeline_stage") self._pipeline_stage = value context.set_auto_parallel_context(pipeline_stages=value) @property def micro_batch_num(self): return self._micro_batch_num @micro_batch_num.setter def micro_batch_num(self, value): Validator.check_positive_int(value, "micro_batch_num") self._micro_batch_num = value # In case the user doesn't pass a config as args. default_dpmp_config = OpParallelConfig() default_moeparallel_config = MoEParallelConfig() def _check_config(config): """ Check if micro_batch_num >= pipeline_stage """ # the config pipeline_stage is same with context.pipeline_stage pipeline_stage = context.get_auto_parallel_context("pipeline_stages") if hasattr(config, 'pipeline_stage') and pipeline_stage != config.pipeline_stage: raise ValueError( f"The pipeline stage {pipeline_stage} in auto_parallel_context is not equal to the pipeline_stage " f"{config.pipeline_stage}" f" in the config.") # make sure the following is in auto parallel mode is_auto_parallel = _get_parallel_mode() in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL) if not is_auto_parallel: return device_num = D.get_group_size() optimizer_shard = context.get_auto_parallel_context("enable_parallel_optimizer") if config.data_parallel * config.model_parallel * pipeline_stage > device_num: raise ValueError(f"The product of the data parallel {config.data_parallel}, " f"model parallel {config.model_parallel} " f"pipeline stages {pipeline_stage} " f"should be less than device_num {device_num}.") # the config optimizer_shard is same with context.optimizer_shard if hasattr(config, "optimizer_shard") and optimizer_shard and optimizer_shard != config.optimizer_shard: logger.warning(f"The optimizer shard {optimizer_shard} in auto_parallel_context is not equal to the" f" optimizer_shard {config.optimizer_shard} in the OpParallelConfig. Please check the " f"optimizer_shard to make them consistent.")