mindspore2022/mindspore/context.py

# Copyright 2020-2021 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
"""
The context of mindspore, used to configure the current execution environment,
includes the execution mode, execution backend and other feature switches.
"""
import json
import os
import time
import threading
from collections import namedtuple
from types import FunctionType
from mindspore import log as logger
from mindspore._c_expression import MSContext, ms_ctx_param
from mindspore._checkparam import args_type_check, Validator
from mindspore.parallel._auto_parallel_context import _set_auto_parallel_context, _get_auto_parallel_context, \
    _reset_auto_parallel_context
from mindspore.parallel._ps_context import _set_ps_context, _get_ps_context, _reset_ps_context
from .default_config import __device_target__, __package_name__

__all__ = ['GRAPH_MODE', 'PYNATIVE_MODE', 'set_context', 'get_context', 'set_auto_parallel_context',
           'get_auto_parallel_context', 'reset_auto_parallel_context', 'ParallelMode', 'set_ps_context',
           'get_ps_context', 'reset_ps_context']

GRAPH_MODE = 0
PYNATIVE_MODE = 1
_DEVICE_APP_MEMORY_SIZE = 31 # The max memory size of graph plus variable.
_re_pattern = r'[1-9][0-9]*(\.)?[0-9]*GB|0\.[0-9]*GB'
_k_context = None

def _make_directory(path):
    """Make directory."""
    real_path = None
    if path is None or not isinstance(path, str) or path.strip() == "":
        raise ValueError(f"Input path `{path}` is invalid type")

    # convert the relative paths
    path = os.path.realpath(path)
    logger.debug("The absolute path is %r", path)

    # check whether the path is already existed and has written permissions
    if os.path.exists(path):
        real_path = path
    else:
        # All exceptions need to be caught because create directory maybe have some limit(permissions)
        logger.debug("The directory(%s) doesn't exist, will create it", path)
        try:
            os.makedirs(path)
            real_path = path
        except PermissionError as e:
            logger.error(f"No write permission on the directory `{path}, error = {e}")
            raise ValueError(f"No write permission on the directory `{path}`.")
    return real_path


def _get_print_file_name(file_name):
    """Add timestamp suffix to file name. Rename the file name:  file_name + "." + time(seconds)."""
    time_second = str(int(time.time()))
    file_name = file_name + "." + time_second
    if os.path.exists(file_name):
        ValueError("This file {} already exists.".format(file_name))
    return file_name


class _ThreadLocalInfo(threading.local):
    """
    Thread local Info used for store thread local attributes.
    """

    def __init__(self):
        super(_ThreadLocalInfo, self).__init__()
        self._reserve_class_name_in_scope = True

    @property
    def reserve_class_name_in_scope(self):
        """Gets whether to save the network class name in the scope."""
        return self._reserve_class_name_in_scope

    @reserve_class_name_in_scope.setter
    def reserve_class_name_in_scope(self, reserve_class_name_in_scope):
        """Sets whether to save the network class name in the scope."""
        if not isinstance(reserve_class_name_in_scope, bool):
            raise ValueError(
                "Set reserve_class_name_in_scope value must be bool!")
        self._reserve_class_name_in_scope = reserve_class_name_in_scope


_ContextRecord = namedtuple(
    "_ContextRecord", ["is_pynative_mode", "switch_context_fn"])


class _ContextSwitchInfo(threading.local):
    """
    Record of context switch information.

    Args:
        is_pynative (bool): Whether to adopt the PyNative mode.
    """

    def __init__(self, is_pynative):
        super(_ContextSwitchInfo, self).__init__()
        self.context_stack = []
        if is_pynative:
            self.push(True, None)

    def push(self, is_pynative, switch_context_fn):
        """
        Push a context switch record onto the stack.

        Args:
            is_pynative (bool): Whether context switch to PyNative mode.
            switch_context_fn (Function): A callable that executes the context switch.
        """
        if isinstance(switch_context_fn, FunctionType):
            switch_context_fn()
        self.context_stack.append(
            _ContextRecord(is_pynative, switch_context_fn))

    def pop(self):
        self.context_stack.pop()


class _Context:
    """
    _Context is the environment in which operations are executed

    Note:
        Create a context through instantiating Context object is not recommended.
        should use context() to get the context since Context is singleton.
    """
    _instance = None
    _instance_lock = threading.Lock()

    def __init__(self):
        self._thread_local_info = _ThreadLocalInfo()
        self._context_switches = _ContextSwitchInfo(True)
        self._context_handle = MSContext.get_instance()

    def __new__(cls, *args, **kwargs):
        if cls._instance is None:
            cls._instance_lock.acquire()
            cls._instance = object.__new__(cls)
            cls._instance_lock.release()
        return cls._instance

    def __getattribute__(self, attr):
        value = object.__getattribute__(self, attr)
        if attr == "_context_handle" and value is None:
            raise ValueError("Context handle is none in context!!!")
        return value

    def get_param(self, param):
        return self._context_handle.get_param(param)

    def set_param(self, param, value):
        self._context_handle.set_param(param, value)

    def set_mode(self, mode):
        """
        Switch between Graph mode and PyNative mode.

        Args:
            mode (int): GRAPH_MODE or PYNATIVE_MODE.
        """
        if mode == PYNATIVE_MODE:
            if self.enable_debug_runtime:
                self.set_backend_policy("vm")
            self._context_switches.push(True, None)
        elif mode == GRAPH_MODE:
            if self.enable_debug_runtime:
                self.set_backend_policy("ge")
            self._context_switches.push(False, None)
        else:
            raise ValueError(f'The execution mode {mode} is invalid!')
        self.set_param(ms_ctx_param.mode, mode)

    def set_backend_policy(self, policy):
        success = self._context_handle.set_backend_policy(policy)
        if not success:
            raise RuntimeError("Backend policy must be one of ge, vm, ms.")

    def set_save_graphs_path(self, save_graphs_path):
        self.set_param(ms_ctx_param.save_graphs_path, _make_directory(save_graphs_path))

    def set_device_target(self, target):
        valid_targets = ["CPU", "GPU", "Ascend", "Davinci"]
        if not target in valid_targets:
            raise ValueError(f"Target device name {target} is invalid! It must be one of {valid_targets}")
        if target == "Davinci":
            target = "Ascend"
        self.set_param(ms_ctx_param.device_target, target)
        if self.enable_debug_runtime and target == "CPU":
            self.set_backend_policy("vm")

    def set_device_id(self, device_id):
        if device_id < 0 or device_id > 4095:
            raise ValueError(f"Device id must be in [0, 4095], but got {device_id}")
        self.set_param(ms_ctx_param.device_id, device_id)

    def set_max_call_depth(self, max_call_depth):
        if max_call_depth <= 0:
            raise ValueError(f"Max call depth must be greater than 0, but got {max_call_depth}")
        self.set_param(ms_ctx_param.max_call_depth, max_call_depth)

    def set_profiling_options(self, option):
        if not isinstance(option, str):
            raise TypeError("The parameter option must be str.")
        self.set_param(ms_ctx_param.profiling_options, option)

    def set_variable_memory_max_size(self, variable_memory_max_size):
        """set values of variable_memory_max_size and graph_memory_max_size"""
        if not Validator.check_str_by_regular(variable_memory_max_size, _re_pattern):
            raise ValueError("Context param variable_memory_max_size should be in correct format! Such as \"5GB\"")
        if int(variable_memory_max_size[:-2]) > _DEVICE_APP_MEMORY_SIZE:
            raise ValueError("Context param variable_memory_max_size should be not greater than 31GB.")
        variable_memory_max_size_ = variable_memory_max_size[:-2] + " * 1024 * 1024 * 1024"
        graph_memory_max_size = _DEVICE_APP_MEMORY_SIZE - int(variable_memory_max_size[:-2])
        graph_memory_max_size_ = str(graph_memory_max_size) + " * 1024 * 1024 * 1024"
        self.set_param(ms_ctx_param.variable_memory_max_size, variable_memory_max_size_)
        # pylint: disable=protected-access
        self.set_param(ms_ctx_param._graph_memory_max_size, graph_memory_max_size_)

    def set_max_device_memory(self, max_device_memory):
        if not Validator.check_str_by_regular(max_device_memory, _re_pattern):
            raise ValueError("Context param max_device_memory should be in correct format! Such as \"3.5GB\"")
        max_device_memory_value = float(max_device_memory[:-2])
        if max_device_memory_value == 0:
            raise ValueError("Context param max_device_memory should be in correct format! Such as \"3.5GB\"")
        self.set_param(ms_ctx_param.max_device_memory, max_device_memory_value)

    def set_print_file_path(self, file_path):
        """Add timestamp suffix to file name. Sets print file path."""
        print_file_path = os.path.realpath(file_path)
        if os.path.isdir(print_file_path):
            raise IOError("Print_file_path should be file path, but got {}.".format(file_path))

        if os.path.exists(print_file_path):
            _path, _file_name = os.path.split(print_file_path)
            path = _make_directory(_path)
            file_name = _get_print_file_name(_file_name)
            full_file_name = os.path.join(path, file_name)
        else:
            full_file_name = print_file_path
        self.set_param(ms_ctx_param.print_file_path, full_file_name)

    def set_env_config_path(self, env_config_path):
        """Check and set env_config_path."""
        if not self._context_handle.enable_dump_ir():
            raise ValueError("The 'env_config_path' is not supported, please enable ENABLE_DUMP_IR "
                             "with '-D on' and recompile source.")
        env_config_path = os.path.realpath(env_config_path)
        if not os.path.isfile(env_config_path):
            raise ValueError("The %r set by 'env_config_path' should be an existing json file." % env_config_path)
        try:
            with open(env_config_path, 'r') as f:
                json.load(f)
        except (TypeError, ValueError) as exo:
            raise ValueError("The %r set by 'env_config_path' should be a json file. "
                             "Detail: %s." % (env_config_path, str(exo)))
        self.set_param(ms_ctx_param.env_config_path, env_config_path)

    setters = {
        'mode': set_mode,
        'save_graphs_path': set_save_graphs_path,
        'device_target': set_device_target,
        'device_id': set_device_id,
        'max_call_depth': set_max_call_depth,
        'profiling_options': set_profiling_options,
        'variable_memory_max_size': set_variable_memory_max_size,
        'max_device_memory': set_max_device_memory,
        'print_file_path': set_print_file_path,
        'env_config_path': set_env_config_path
    }

    @property
    def reserve_class_name_in_scope(self):
        """Gets whether to save the network class name in the scope."""
        return self._thread_local_info.reserve_class_name_in_scope

    @reserve_class_name_in_scope.setter
    def reserve_class_name_in_scope(self, reserve_class_name_in_scope):
        """Sets whether to save the network class name in the scope."""
        self._thread_local_info.reserve_class_name_in_scope = reserve_class_name_in_scope

    @property
    def enable_ge(self):
        return self._context_handle.get_backend_policy() == 'ge'

    @property
    def enable_debug_runtime(self):
        return self._thread_local_info.debug_runtime

    @enable_debug_runtime.setter
    def enable_debug_runtime(self, enable):
        thread_info = self._thread_local_info
        thread_info.debug_runtime = enable


def _context():
    """
    Get the global _context, if context is not created, create a new one.

    Returns:
        _Context, the global context in PyNative mode.
    """
    global _k_context
    if _k_context is None:
        default_backend = 'debug'
        try:
            from mindspore import default_config
            default_backend = default_config.__backend__
        except ImportError:
            logger.error("import default config fail")
        _k_context = _Context()
        _k_context.enable_debug_runtime = False
        if default_backend == 'debug':
            _k_context.enable_debug_runtime = True
            default_backend = 'vm'
        _k_context.set_backend_policy(default_backend)
    return _k_context


@args_type_check(device_num=int, global_rank=int, gradients_mean=bool, gradient_fp32_sync=bool, parallel_mode=str,
                 auto_parallel_search_mode=str, parameter_broadcast=bool, strategy_ckpt_load_file=str,
                 strategy_ckpt_save_file=str, full_batch=bool, enable_parallel_optimizer=bool,
                 all_reduce_fusion_config=list, pipeline_stages=int)
def set_auto_parallel_context(**kwargs):
    r"""
    Set auto parallel context, which is valid only for Ascend and GPU target.

    Auto parallel context should be configured before the initialization of your network.

    Note:
        Attribute name is required for setting attributes.
        If a program has tasks with different parallel modes, then before setting new parallel mode for the
        next task, interface mindspore.context.reset_auto_parallel_context() needs to be called to reset
        the configuration.
        Setting or changing parallel modes must be called before any creating Initializer, otherwise,
        RuntimeError may be raised when compiling the network.

    Some configurations are parallel mode specific, see the below table for details:

    ===========================  ===========================
    Common                       AUTO_PARALLEL
    ===========================  ===========================
    device_num                   gradient_fp32_sync
    global_rank                  loss_repeated_mean
    gradients_mean               auto_parallel_search_mode
    parallel_mode                strategy_ckpt_load_file
    all_reduce_fusion_config     strategy_ckpt_save_file
    enable_parallel_optimizer    full_batch
               \                 pipeline_stages
    ===========================  ===========================

    Args:
        device_num (int): Available device number, the value must be in [1, 4096]. Default: 1.
        global_rank (int): Global rank id, the value must be in [0, 4095]. Default: 0.
        gradients_mean (bool): Whether to perform mean operator after allreduce of gradients.
                     "stand_alone" do not support gradients_mean. Default: False.
        gradient_fp32_sync (bool): Run allreduce of gradients in fp32.
                     "stand_alone", "data_parallel" and "hybrid_parallel" do not support
                     gradient_fp32_sync. Default: True.
        parallel_mode (str): There are five kinds of parallel modes, "stand_alone", "data_parallel",
                     "hybrid_parallel", "semi_auto_parallel" and "auto_parallel". Default: "stand_alone".

                     - stand_alone: Only one processor is working.

                     - data_parallel: Distributes the data across different processors.

                     - hybrid_parallel: Achieves data parallelism and model parallelism manually.

                     - semi_auto_parallel: Achieves data parallelism and model parallelism by
                       setting parallel strategies.

                     - auto_parallel: Achieving parallelism automatically.
        auto_parallel_search_mode (str): There are two kinds of shard strategy search modes, "recursive_programming"
                     and "dynamic_programming". Default: "dynamic_programming".

                     - recursive_programming: Recursive programming search mode.

                     - dynamic_programming: Dynamic programming search mode.
        parameter_broadcast (bool): Whether to broadcast parameters before training. Before training, in order to have
                     the same network initialization parameter values for all devices, broadcast the parameters
                     on device 0 to other devices. Parameter broadcasting in different parallel modes is different,
                     data_parallel mode, all parameters are broadcast except for the parameter whose attribute
                     layerwise_parallel is True. Hybrid_parallel, semi_auto_parallel and auto_parallel mode, the
                     segmented parameters do not participate in broadcasting. Default: False.
        strategy_ckpt_load_file (str): The path to load parallel strategy checkpoint. Default: ''
        strategy_ckpt_save_file (str): The path to save parallel strategy checkpoint. Default: ''
        full_batch (bool): If you load whole batch datasets in auto_parallel mode, this parameter
                       should be set with True. Default: False.
        enable_parallel_optimizer (bool): This is a developing feature, which shards the weight update computation for
                       data parallel training in the benefit of time and memory saving. Currently, auto and semi auto
                       parallel mode support all optimizers in both Ascend and GPU. Data parallel mode only supports
                       `Lamb` and `AdamWeightDecay` in Ascend . Default: False.
        all_reduce_fusion_config (list): Set allreduce fusion strategy by parameters indices. Only support ReduceOp.SUM
                       and HCCL_WORLD_GROUP/NCCL_WORLD_GROUP. No Default, if it is not set, the fusion is closed.
        pipeline_stages (int): Set the stage information for pipeline parallel. This indicates how
                        the devices are distributed alone the pipeline. The total devices will be divided into
                        'pipeline_stags' stages. This currently could only be used when
                        parallel mode semi_auto_parallel is enabled. Default: 1.

    Raises:
        ValueError: If input key is not attribute in auto parallel context.

    Examples:
        >>> context.set_auto_parallel_context(device_num=8)
        >>> context.set_auto_parallel_context(global_rank=0)
        >>> context.set_auto_parallel_context(gradients_mean=True)
        >>> context.set_auto_parallel_context(gradient_fp32_sync=False)
        >>> context.set_auto_parallel_context(parallel_mode="auto_parallel")
        >>> context.set_auto_parallel_context(auto_parallel_search_mode="dynamic_programming")
        >>> context.set_auto_parallel_context(parameter_broadcast=False)
        >>> context.set_auto_parallel_context(strategy_ckpt_load_file="./strategy_stage1.ckpt")
        >>> context.set_auto_parallel_context(strategy_ckpt_save_file="./strategy_stage1.ckpt")
        >>> context.set_auto_parallel_context(full_batch=True)
        >>> context.set_auto_parallel_context(enable_parallel_optimizer=False)
        >>> context.set_auto_parallel_context(all_reduce_fusion_config=[8, 160])
        >>> context.set_auto_parallel_context(pipeline_stages=2)
    """
    _set_auto_parallel_context(**kwargs)

def get_auto_parallel_context(attr_key):
    """
    Gets auto parallel context attribute value according to the key.

    Args:
        attr_key (str): The key of the attribute.

    Returns:
        Returns attribute value according to the key.

    Raises:
        ValueError: If input key is not attribute in auto parallel context.
    """
    return _get_auto_parallel_context(attr_key)


def reset_auto_parallel_context():
    """
    Reset auto parallel context attributes to the default values:

    - device_num: 1.
    - global_rank: 0.
    - gradients_mean: False.
    - gradient_fp32_sync: True.
    - parallel_mode: 'stand_alone'.
    - auto_parallel_search_mode: 'dynamic_programming'.
    - parameter_broadcast: False.
    - strategy_ckpt_load_file: ''.
    - strategy_ckpt_save_file: ''.
    - full_batch: False.
    - enable_parallel_optimizer: False.
    - pipeline_stages: 1.
    """
    _reset_auto_parallel_context()


def _check_target_specific_cfgs(device, arg_key):
    """Checking whether a config is suitable for a specified device"""
    device_cfgs = {
        'enable_auto_mixed_precision': ['Ascend'],
        'enable_dump': ['Ascend'],
        'save_dump_path': ['Ascend'],
        'enable_graph_kernel': ['Ascend', 'GPU'],
        'enable_reduce_precision': ['Ascend'],
        'enable_profiling': ['Ascend'],
        'profiling_options': ['Ascend'],
        'print_file_path': ['Ascend'],
        'variable_memory_max_size': ['Ascend'],
        'max_device_memory': ['GPU']
    }
    # configs not in map device_cfgs are supposed to be suitable for all devices
    if not arg_key in device_cfgs:
        return True
    supported_devices = device_cfgs[arg_key]
    if device in supported_devices:
        return True
    logger.warning(f"Config '{arg_key}' only supports devices in {supported_devices}, current device is '{device}'"
                   ", ignore it.")
    return False


@args_type_check(mode=int, precompile_only=bool, device_target=str, device_id=int, save_graphs=bool,
                 save_graphs_path=str, enable_dump=bool,
                 save_dump_path=str, enable_reduce_precision=bool, variable_memory_max_size=str,
                 enable_profiling=bool, profiling_options=str, enable_auto_mixed_precision=bool,
                 enable_graph_kernel=bool, check_bprop=bool, max_device_memory=str, print_file_path=str,
                 enable_sparse=bool, max_call_depth=int, env_config_path=str)
def set_context(**kwargs):
    """
    Sets context for running environment.

    Context should be configured before running your program. If there is no configuration,
    the "Ascend" device target will be used by default. GRAPH_MODE or
    PYNATIVE_MODE can be set by `mode` attribute and both modes support all backends, default
    mode is PYNATIVE_MODE.

    When the `save_graphs` attribute is set to True, attribute of `save_graphs_path` is used to set the
    intermediate compilation graph storage path. By default, the graphs are saved in the current directory.
    For other configurations and arguments, please refer to the corresponding module
    description, the configuration is optional and can be enabled when needed.

    Note:
        Attribute name is required for setting attributes.
        The mode is not recommended to be changed after net was initialized because the implementations of some
        operations are different in graph mode and pynative mode. Default: PYNATIVE_MODE.

    Some configurations are device specific, see the below table for details:

    ===========================  ===========================  =================
    Common(CPU/GPU/Ascend)       Ascend                       GPU
    ===========================  ===========================  =================
    check_bprop                  print_file_path              max_device_memory
    device_id                    enable_dump                  enable_graph_kernel
    device_target                save_dump_path
    enable_sparse                enable_graph_kernel
    max_call_depth               enable_reduce_precision
    mode                         enable_profiling
    reserve_class_name_in_scope  profiling_options
    save_graphs                  variable_memory_max_size
    save_graphs_path
    env_config_path
    grad_for_scalar
    ===========================  ===========================  =================

    Args:
        mode (int): Running in GRAPH_MODE(0) or PYNATIVE_MODE(1). Default: PYNATIVE_MODE(1).
        device_target (str): The target device to run, support "Ascend", "GPU", and "CPU". Default: "Ascend".
        device_id (int): ID of the target device, the value must be in [0, device_num_per_host-1],
                    while device_num_per_host should be no more than 4096. Default: 0.
        save_graphs (bool): Whether to save graphs. Default: False.
        save_graphs_path (str): Path to save graphs. Default: "."
        enable_graph_kernel (bool): Whether to enable composition of basic primitives. These primitives would be
            compiled into a fused kernel automatically. Default: False.
        reserve_class_name_in_scope (bool) : Whether to save the network class name in the scope. Default: True.
        enable_reduce_precision (bool): Whether to enable precision reduction. Default: True.
        enable_dump (bool): Whether to enable dump. Default: False.
        save_dump_path (str): When the program is executed on Ascend, operators can dump data in this path.
            The root dump path is configured in /home/HwHiAiUser/ide_daemon/ide_daemon.cfg.
            So the real dump path is "{configured root dump path}/{`save_dump_path`}". Default: ".".
        variable_memory_max_size (str): Set the maximum size of the variable memory max size. Default: "0GB".
        enable_profiling (bool): Whether to open profiling. Default: False.
        profiling_options (str): Set profiling collection options, operators can profiling data here.
            The values of profiling collection options are as follows, supporting the collection of multiple data.

            - output: the saving the path of the profiling collection result file. The directory spectified by this
              parameter needs to be created in advance on the training environment (container or host side) and ensure
              that the running user configured during installation has read and write permissions.It supports the
              configuration of absolute or relative paths(relative to the current path when executing the command line).
              The absolute path configuration starts with '/', for example:/home/data/output.
              The relative path configuration directly starts with the directory name,for example:output.

            - training_trace: collect iterative trajectory data, that is, the training task and software information of
              the AI software stack, to achieve performance analysis of the training task, focusing on data
              enhancement, forward and backward calculation, gradient aggregation update and other related data.
              The value is on/off.

            - task_trace: collect task trajectory data, that is, the hardware information of the HWTS/AICore of
              the Ascend 910 processor, and analyze the information of beginning and ending of the task.
              The value is on/off.

            - aicpu: collect profiling data enhanced by aicpu data. The value is on/off.

            - fp_point: specify the start position of the forward operator of the training network iteration trajectory,
              which is used to record the start timestamp of the forward calculation.The configuration value is the name
              of the first operator specified in the forward direction. when the value is empty,the system will
              automatically obtain the forward operator name.

            - bp_point: specify the end position of the iteration trajectory reversal operator of the training network,
              record the end timestamp of the backward calculation. The configuration value is the name of the operator
              after the specified reverse. when the value is empty,the system will automatically obtain the backward
              operator name.

            - aic_metrics: the values are as follows:
              ArithmeticUtilization: percentage statistics of various calculation indicators.
              PipeUtilization: the time-consuming ratio of calculation unit and handling unit,this item is
              the default value.
              Memory: percentage of external memory read and write instructions.
              MemoryL0: percentage of internal memory read and write instructions.
              ResourceConflictRatio: proportion of pipline queue instructions.

            The profiling_options is like '{"output":'/home/data/output','training_trace':'on'}'

        check_bprop (bool): Whether to check bprop. Default: False.
        max_device_memory (str): Sets the maximum memory available for devices.
            Currently, it is only supported on GPU. The format is "xxGB". Default: "1024GB".
        print_file_path (str): The path of saving print data. If this parameter is set, print data is saved to
            a file by default, and turns off printing to the screen. If the file already exists, add a timestamp
            suffix to the file. Default: ''.
        enable_sparse (bool): Whether to enable sparsity feature. Default: False.
        max_call_depth (int): Specify the maximum depth of function call. Default: 1000.
        env_config_path (str): Config path for DFX.
        grad_for_scalar (bool): Whether to get gradient for scalar. Default: False.

    Raises:
        ValueError: If input key is not an attribute in context.

    Examples:
        >>> context.set_context(mode=context.GRAPH_MODE)
        >>> context.set_context(mode=context.PYNATIVE_MODE)
        >>> context.set_context(device_target="Ascend")
        >>> context.set_context(device_id=0)
        >>> context.set_context(save_graphs=True, save_graphs_path="./model.ms")
        >>> context.set_context(enable_reduce_precision=True)
        >>> context.set_context(enable_dump=True, save_dump_path=".")
        >>> context.set_context(reserve_class_name_in_scope=True)
        >>> context.set_context(variable_memory_max_size="6GB")
        >>> context.set_context(mode=context.GRAPH_MODE,
        ...                     device_target="Ascend",device_id=0, save_graphs=True,
        ...                     save_graphs_path="/mindspore")
        >>> context.set_context(enable_profiling=True, \
                                profiling_options='{"output":"/home/data/output","training_trace":"on"}')
        >>> context.set_context(max_device_memory="3.5GB")
        >>> context.set_context(print_file_path="print.pb")
        >>> context.set_context(max_call_depth=80)
        >>> context.set_context(env_config_path="./env_config.json")
    """
    ctx = _context()
    # set device target first
    if 'device_target' in kwargs:
        ctx.set_device_target(kwargs['device_target'])
        device = ctx.get_param(ms_ctx_param.device_target)
        if not device.lower() in __device_target__:
            raise ValueError(f"Error, package type {__package_name__} support device type {__device_target__}, "
                             f"but got device target {device}")
    device = ctx.get_param(ms_ctx_param.device_target)
    for key, value in kwargs.items():
        if not _check_target_specific_cfgs(device, key):
            continue
        if hasattr(ctx, key):
            setattr(ctx, key, value)
            continue
        if key in ctx.setters:
            ctx.setters[key](ctx, value)
            continue
        # enum variables beginning with '_' are for internal use
        if key in ms_ctx_param.__members__ and key[0] != '_':
            ctx.set_param(ms_ctx_param.__members__[key], value)
            continue
        raise ValueError("Set context keyword %s is not recognized!" % key)


def get_context(attr_key):
    """
    Gets context attribute value according to the input key.

    Args:
        attr_key (str): The key of the attribute.

    Returns:
        Object, The value of given attribute key.

    Raises:
        ValueError: If input key is not an attribute in context.
    """
    ctx = _context()
    device = ctx.get_param(ms_ctx_param.device_target)
    _ = _check_target_specific_cfgs(device, attr_key)
    if hasattr(ctx, attr_key):
        return getattr(ctx, attr_key)
    # enum variables beginning with '_' are for internal use
    if attr_key in ms_ctx_param.__members__ and attr_key[0] != '_':
        return ctx.get_param(ms_ctx_param.__members__[attr_key])
    raise ValueError("Get context keyword %s is not recognized!" % attr_key)


class ParallelMode:
    """
    Parallel mode options.

    There are five kinds of parallel modes, "STAND_ALONE", "DATA_PARALLEL",
    "HYBRID_PARALLEL", "SEMI_AUTO_PARALLEL" and "AUTO_PARALLEL". Default: "STAND_ALONE".

    - STAND_ALONE: Only one processor is working.
    - DATA_PARALLEL: Distributes the data across different processors.
    - HYBRID_PARALLEL: Achieves data parallelism and model parallelism manually.
    - SEMI_AUTO_PARALLEL: Achieves data parallelism and model parallelism by setting parallel strategies.
    - AUTO_PARALLEL: Achieves parallelism automatically.

    MODE_LIST: The list of all supported parallel modes.
    """

    STAND_ALONE = "stand_alone"
    DATA_PARALLEL = "data_parallel"
    HYBRID_PARALLEL = "hybrid_parallel"
    SEMI_AUTO_PARALLEL = "semi_auto_parallel"
    AUTO_PARALLEL = "auto_parallel"
    MODE_LIST = [STAND_ALONE, DATA_PARALLEL, HYBRID_PARALLEL, SEMI_AUTO_PARALLEL, AUTO_PARALLEL]

@args_type_check(enable_ps=bool)
def set_ps_context(**kwargs):
    """
    Set parameter server training mode context.

    Note:
        Some other environment variables should also be set for parameter server training mode.
        These environment variables are listed below:

        .. code-block::

            MS_SERVER_NUM  # Server number
            MS_WORKER_NUM  # Worker number
            MS_SCHED_HOST  # Scheduler IP address
            MS_SCHED_PORT  # Scheduler port
            MS_ROLE        # The role of this process:
                           # MS_SCHED represents the scheduler,
                           # MS_WORKER represents the worker,
                           # MS_PSERVER represents the Server


    Args:
        enable_ps (bool): Whether to enable parameter server training mode.
                          Only after enable_ps is set True, the environment variables will be effective.
                          Default: False.

    Raises:
        ValueError: If input key is not the attribute in parameter server training mode context.

    Examples:
        >>> context.set_ps_context(enable_ps=True)
    """
    _set_ps_context(**kwargs)


def get_ps_context(attr_key):
    """
    Get parameter server training mode context attribute value according to the key.

    Args:
        attr_key (str): The key of the attribute.

    Returns:
        Returns attribute value according to the key.

    Raises:
        ValueError: If input key is not attribute in auto parallel context.
    """
    return _get_ps_context(attr_key)

def reset_ps_context():
    """
    Reset parameter server training mode context attributes to the default values:

    - enable_ps: False.
    """
    _reset_ps_context()