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experiment

Module diagram

classDiagram
  class experiment {
  }
  class importlib {
  }
  class multiprocessing {
  }
  class sweep {
  }

experiment

importlib

get_module_from_path 

get_module_from_path(file_path: str) -> ModuleType

입력으로 받은 파이썬 파일을 모듈화하여 반환합니다.

Parameters:

  • file_path (str) –

    path to python file

Returns:

  • ModuleType ( ModuleType ) –

    loaded module

Source code in ResearchToolkit/experiment/importlib.py 
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def get_module_from_path(file_path: str) -> ModuleType:
    """입력으로 받은 파이썬 파일을 모듈화하여 반환합니다.

    Args:
        file_path (str): path to python file

    Returns:
        ModuleType: loaded module
    """
    module_name = os.path.basename(file_path).rstrip(".py")
    spec = importlib.util.spec_from_file_location(module_name, file_path)
    module = importlib.util.module_from_spec(spec)
    spec.loader.exec_module(module)

    return module

multiprocessing

MultiJobHandler 

MultiJobHandler(gpu_ids: Union[int, List[int]], job_per_gpu: int)

여러 실험을 여러 GPU에서 parallel하게 돌려주는 class 입니다.

Example
100가지의 서로 다른 실험을 0, 1 GPU에, GPU당 실험 2개씩 순차적으로 실행

multi_job_handler = MultiJobHandler(gpu_ids=[0, 1], job_per_gpu=2) multi_job_handler.launch(func=some_function, kwargs_list=[ config1, config2, config3, ... , config 100 ])

Note
  1. launch 함수를 실행하기 전에 pytorch의 GPU 기능을 사용할 수 없습니다. 1-1. fork 대신 spawn을 사용하면 해결할 수 있으나, spawn을 사용할 경우 code변경 이슈가 생길 수 있음.
  2. CUDA_VISIBLE_DEVICES를 사용할 경우 주의가 필요합니다. 현재 구현 방식은 CUDA_VISIBLE_DEVICES=1,3,5,7 이고 gpu_ids=[1, 2]라면 실제 GPU는 3, 5를 사용합니다.

Parameters:

  • gpu_ids (Union[int, List[int]]) –

    실험에 사용할 GPU id list.

  • job_per_gpu (int) –

    각 GPU device에 최대로 할당할 수 있는 job의 개 수.

Source code in ResearchToolkit/experiment/multiprocessing.py 
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def __init__(
    self,
    gpu_ids: Union[int, List[int]],
    job_per_gpu: int,
) -> None:
    """
    Args:
        gpu_ids (Union[int, List[int]]): 실험에 사용할 GPU id list.
        job_per_gpu (int): 각 GPU device에 최대로 할당할 수 있는 job의 개 수.
    """
    assert job_per_gpu > 0
    self.job_per_gpu = job_per_gpu
    if isinstance(gpu_ids, int):
        gpu_ids = [gpu_ids]
    self.gpu_ids = list(set(gpu_ids))
    for gpu_id in gpu_ids:
        assert isinstance(gpu_id, int)
    self.proc_list_per_gpu = {gpu_id: [] for gpu_id in self.gpu_ids}
    self.cuda_visible_devices = {gpu_id: str(gpu_id) for gpu_id in self.gpu_ids}
    if os.environ.get("CUDA_VISIBLE_DEVICES") is not None:
        cuda_visible_devices = os.environ["CUDA_VISIBLE_DEVICES"].split(",")
        for gpu_id in self.gpu_ids:
            self.cuda_visible_devices[gpu_id] = cuda_visible_devices[gpu_id]
job_per_gpu instance-attribute 
job_per_gpu = job_per_gpu
gpu_ids instance-attribute 
gpu_ids = list(set(gpu_ids))
proc_list_per_gpu instance-attribute 
proc_list_per_gpu = {gpu_id: [] for gpu_id in (gpu_ids)}
cuda_visible_devices instance-attribute 
cuda_visible_devices = {gpu_id: (str(gpu_id)) for gpu_id in (gpu_ids)}
launch 
launch(func: Callable, kwargs_list: List) -> None

여러 실험을 여러 GPU에서 parallel하게 실행합니다.

Parameters:

  • func (Callable) –

    Function to be executed by the child process.

  • kwargs_list (List) –

    List of Keyword arguments to be input to the function.

Source code in ResearchToolkit/experiment/multiprocessing.py 
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def launch(self, func: Callable, kwargs_list: List) -> None:
    """여러 실험을 여러 GPU에서 parallel하게 실행합니다.

    Args:
        func (Callable): Function to be executed by the child process.
        kwargs_list (List): List of Keyword arguments to be input to the function.
    """
    for kwargs in kwargs_list:
        while self._is_busy():
            self._join()

        cur_gpu_id = self._get_gpu_id()

        context_type = "spawn" if platform.system() == "Windows" else "fork"
        proc = multiprocessing.get_context(context_type).Process(
            target=self.run, kwargs={"gpu_id": cur_gpu_id, "func": func, "kwargs": kwargs}
        )
        proc.start()
        self.proc_list_per_gpu[cur_gpu_id].append(proc)

    self._join(timeout=None)
_is_busy 
_is_busy() -> bool

Check devices are busy

Returns:

  • bool ( bool ) –

    Returns True if all devices are working else False

Source code in ResearchToolkit/experiment/multiprocessing.py 
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def _is_busy(self) -> bool:
    """Check devices are busy

    Returns:
        bool: Returns True if all devices are working else False
    """
    is_busy = True
    for gpu_id in self.gpu_ids:
        is_busy = is_busy and (len(self.proc_list_per_gpu[gpu_id]) >= self.job_per_gpu)

    return is_busy
_join 
_join(timeout: float = 0.1) -> None

Waits until the child process is finished or as long as the timeout time.

Parameters:

  • timeout (float, default: 0.1 ) –

    Waiting time for each child process. Defaults to 0.1.

Source code in ResearchToolkit/experiment/multiprocessing.py 
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def _join(self, timeout: float = 0.1) -> None:
    """Waits until the child process is finished or as long as the timeout time.

    Args:
        timeout (float, optional): Waiting time for each child process. Defaults to 0.1.
    """
    for gpu_id in self.gpu_ids:
        proc_idx = 0
        while proc_idx < len(self.proc_list_per_gpu[gpu_id]):
            self.proc_list_per_gpu[gpu_id][proc_idx].join(timeout=timeout)
            if self.proc_list_per_gpu[gpu_id][proc_idx].is_alive():
                proc_idx = proc_idx + 1
            else:
                self.proc_list_per_gpu[gpu_id].pop(proc_idx)
_get_gpu_id 
_get_gpu_id() -> int

Returns the available gpu id.

Returns:

  • int ( int ) –

    gpu id

Source code in ResearchToolkit/experiment/multiprocessing.py 
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def _get_gpu_id(self) -> int:
    """Returns the available gpu id.

    Returns:
        int: gpu id
    """
    min_job_per_gpu = self.job_per_gpu
    min_gpu_id = -1

    for gpu_id in self.gpu_ids:
        if len(self.proc_list_per_gpu[gpu_id]) < min_job_per_gpu:
            min_job_per_gpu = len(self.proc_list_per_gpu[gpu_id])
            min_gpu_id = gpu_id

    return min_gpu_id
run 
run(gpu_id: int, func: Callable, kwargs) -> None

Set the GPU id and execute the function.

Parameters:

  • gpu_id (int) –

    GPU id

  • func (Callable) –

    Function to be executed by the child process.

  • kwargs (_type_) –

    Keyword arguments to be input to the function.

Source code in ResearchToolkit/experiment/multiprocessing.py 
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def run(self, gpu_id: int, func: Callable, kwargs) -> None:
    """Set the GPU id and execute the function.

    Args:
        gpu_id (int): GPU id
        func (Callable): Function to be executed by the child process.
        kwargs (_type_): Keyword arguments to be input to the function.
    """
    os.environ["CUDA_VISIBLE_DEVICES"] = self.cuda_visible_devices[gpu_id]
    func(**kwargs)

sweep

SweepList 

SweepList(sweep_list: List[Any], tag: Optional[str] = None, descriptions: Optional[List[Any]] = None, rank: Union[int, float] = float('inf'))

sweep 하고 싶은 config를 나타낼 때 사용하는 class 입니다.

Parameters:

  • sweep_list (List) –

    sweep할 element들이 들어있는 List입니다.

  • tag (str, default: None ) –

    SweepList를 group으로 묶고 싶을 때 사용하는 tag 입니다. tag가 같은 SweepList는 Group으로 묶여서 동시에 sweep 됩니다. Defaults to None.

  • descriptions (List[Any], default: None ) –

    sweep할 element들을 설명해주는 description이 들어있는 List입니다. sweep_list와 길이가 일치해야 합니다. Defaults to None.

  • rank (Union[int, float], default: float('inf') ) –

    생성된 config의 반복 되는 순서를 정해주는 값입니다. rank가 낮은 SweepList가 먼저 반복되어 생성됩니다. Defaults to float("inf").

Source code in ResearchToolkit/experiment/sweep.py 
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def __init__(
    self,
    sweep_list: List[Any],
    tag: Optional[str] = None,
    descriptions: Optional[List[Any]] = None,
    rank: Union[int, float] = float("inf"),
) -> None:
    self.sweep_list = sweep_list
    self.tag = tag
    self.descriptions = descriptions
    self.rank = rank
    if self.descriptions is None:
        self.descriptions = [_NoDescription() for _ in range(len(self.sweep_list))]
    assert len(self.sweep_list) == len(
        self.descriptions
    ), "sweep_list and descriptions must be the same length."
sweep_list instance-attribute 
sweep_list = sweep_list
tag instance-attribute 
tag = tag
descriptions instance-attribute 
descriptions = descriptions
rank instance-attribute 
rank = rank

_NoDescription

Description이 없는지를 나타내기 위한 class

_SweepList 

_SweepList(sweeplist: SweepList, key_list: List)

Bases: SweepList

sweep 함수의 쉬운 구현을 위해 SweepList에 약간의 구현이 추가된 class

Source code in ResearchToolkit/experiment/sweep.py 
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def __init__(self, sweeplist: SweepList, key_list: List) -> None:
    super().__init__(
        sweep_list=sweeplist.sweep_list,
        tag=sweeplist.tag,
        descriptions=sweeplist.descriptions,
        rank=sweeplist.rank,
    )
    self.key_list = key_list
key_list instance-attribute 
key_list = key_list
__iter__ 
__iter__() -> Iterator
Source code in ResearchToolkit/experiment/sweep.py 
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def __iter__(self) -> Iterator:
    return [
        {
            "config_value": config_value,
            "description": description,
            "key_list": self.key_list,
        }
        for config_value, description in zip(self.sweep_list, self.descriptions)
    ].__iter__()
__len__ 
__len__() -> int
Source code in ResearchToolkit/experiment/sweep.py 
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def __len__(self) -> int:
    return len(self.sweep_list)

_SweepGroup 

_SweepGroup(sweeplist_list: Optional[List[_SweepList]] = None)

tag가 같은 _SweepList를 Group으로 묶어서 관리하는 class

Source code in ResearchToolkit/experiment/sweep.py 
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def __init__(self, sweeplist_list: Optional[List[_SweepList]] = None) -> None:
    if sweeplist_list is None:
        self.sweep_group = []
    else:
        self.sweep_group = sweeplist_list
sweep_group instance-attribute 
sweep_group = []
rank property 
rank: int
append 
append(sweeplist: _SweepList) -> None
Source code in ResearchToolkit/experiment/sweep.py 
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def append(self, sweeplist: _SweepList) -> None:
    self.sweep_group.append(sweeplist)
__iter__ 
__iter__() -> zip
Source code in ResearchToolkit/experiment/sweep.py 
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def __iter__(self) -> zip:
    for _sweeplist in self.sweep_group:
        assert len(_sweeplist) == len(
            self.sweep_group[0]
        ), "All SweepLists with the same tag must have the same length."
    return zip(*self.sweep_group)

_SweepListManager 

_SweepListManager(sweeplist_list: List[_SweepList])

_SweepList를 관리하고 데카르트 곱(cartesian product)을 iterator로 만들어주는 class

Source code in ResearchToolkit/experiment/sweep.py 
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def __init__(self, sweeplist_list: List[_SweepList]) -> None:
    self.tagged_sweepgroup_dict = {}
    self.non_tagged_sweepgroup_list = []

    for sweeplist in sweeplist_list:
        tag = sweeplist.tag
        if tag is None:
            self.non_tagged_sweepgroup_list.append(_SweepGroup([sweeplist]))
        else:
            if tag not in self.tagged_sweepgroup_dict.keys():
                self.tagged_sweepgroup_dict[tag] = _SweepGroup()
            self.tagged_sweepgroup_dict[tag].append(sweeplist)
tagged_sweepgroup_dict instance-attribute 
tagged_sweepgroup_dict = {}
non_tagged_sweepgroup_list instance-attribute 
non_tagged_sweepgroup_list = []
__iter__ 
__iter__() -> product
Source code in ResearchToolkit/experiment/sweep.py 
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def __iter__(self) -> itertools.product:
    iter_list = [(sweepgroup.rank, sweepgroup) for sweepgroup in self.non_tagged_sweepgroup_list] + [
        (sweepgroup.rank, sweepgroup) for _, sweepgroup in self.tagged_sweepgroup_dict.items()
    ]
    iter_list.sort(key=lambda sweepgroup: sweepgroup[0], reverse=True)
    iter_list = [sweepgroup for rank, sweepgroup in iter_list]
    return itertools.product(*iter_list)

_find_sweeplist 

_find_sweeplist(config: Dict, key_list: List) -> List[_SweepList]

Find the SweepList by recursively traversing the config dictionary. For subsequent operations, SweepList is changed to _SweepList.

Parameters:

  • config (Dict) –

    The config dictionary in which to find the SweepList.

  • key_list (List) –

    List containing the keys of the dictionary sequentially.

Returns:

  • List[_SweepList] –

    List[_SweepList]: List of _SweepList

Source code in ResearchToolkit/experiment/sweep.py 
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def _find_sweeplist(config: Dict, key_list: List) -> List[_SweepList]:
    """
    Find the SweepList by recursively traversing the config dictionary.
    For subsequent operations, SweepList is changed to _SweepList.

    Args:
        config (Dict): The config dictionary in which to find the SweepList.
        key_list (List): List containing the keys of the dictionary sequentially.

    Returns:
        List[_SweepList]: List of _SweepList
    """
    sweeplist_list = []
    for name, values in config.items():
        if isinstance(values, SweepList):
            cur_key = key_list + [name]
            sweeplist_list.append(_SweepList(values, cur_key))
        elif isinstance(values, Dict):
            sweeplist_list = sweeplist_list + _find_sweeplist(values, key_list + [name])

    return sweeplist_list

_set_value 

_set_value(config: Dict, key_list: List, value: Any, make_sub_dict: bool = False) -> None

Set the value in the config dict using the keys in key_list.

Parameters:

  • config (Dict) –

    The dictionary in which you want to set the value.

  • key_list (List) –

    List containing the keys of the dictionary sequentially.

  • value (Any) –

    The value you want to set.

Example

config = { "A": { "B": { "C" = 3 } } }

_set_value(config=config, key_list=["A", "B", "C"], value=5)

results: { "A": { "B": { "C" = 5 } } }

Source code in ResearchToolkit/experiment/sweep.py 
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def _set_value(config: Dict, key_list: List, value: Any, make_sub_dict: bool = False) -> None:
    """
    Set the value in the config dict using the keys in key_list.

    Args:
        config (Dict): The dictionary in which you want to set the value.
        key_list (List): List containing the keys of the dictionary sequentially.
        value (Any): The value you want to set.

    Example:
        config = {
            "A": {
                "B": {
                    "C" = 3
                }
            }
        }

        _set_value(config=config, key_list=["A", "B", "C"], value=5)

        results:
        {
            "A": {
                "B": {
                    "C" = 5
                }
            }
        }
    """
    for idx, key in enumerate(key_list):
        if idx == len(key_list) - 1:
            config[key] = value
        else:
            if make_sub_dict and key not in config:
                config[key] = {}
            config = config[key]

sweep 

sweep(config: Dict) -> List[Dict]

config 내부에 있는 SweepList를 찾아 데카르트 곱(cartesian product)을 만들어줍니다.

Parameters:

  • config (Dict) –

    sweep을 진행하고자 하는 config dictionary.

Returns:

  • List[Dict] –

    List[Dict]: 만들어진 (description, config)들이 들어있는 List.

  • Dict ( List[Dict] ) –

    { "description": Dict, 해당 config에 대해 설정한 description. config와 같은 구조를 가집니다. "config": Dict, 만들어진 config

  • List[Dict] –

    }

Example

config = { "A": 3, "B": SweepList([1, 2], description=["1", "2"]), "C": SweepList([3, 4]), }

configs = sweep(config)

results: configs => [ { "description": {"B": "1",}, "config": {"A": 3, "B": 1, "C": 3}, }, { "description": {"B": "1"}, "config": {"A": 3, "B": 1, "C": 4}, } { "description": {"B": "2"}, "config": {"A": 3, "B": 2, "C": 3}, } { "description": {"B": "2"}, "config": {"A": 3, "B": 2, "C": 4}, } ]

Source code in ResearchToolkit/experiment/sweep.py 
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def sweep(config: Dict) -> List[Dict]:
    """
    config 내부에 있는 SweepList를 찾아 데카르트 곱(cartesian product)을 만들어줍니다.

    Args:
        config (Dict): sweep을 진행하고자 하는 config dictionary.

    Returns:
        List[Dict]: 만들어진 (description, config)들이 들어있는 List.
        Dict: {
            "description": Dict, 해당 config에 대해 설정한 description.
                                 config와 같은 구조를 가집니다.
            "config": Dict, 만들어진 config
        }

    Example:
        config = {
            "A": 3,
            "B": SweepList([1, 2], description=["1", "2"]),
            "C": SweepList([3, 4]),
        }

        configs = sweep(config)

        results:
        configs => [
            {
                "description": {"B": "1",},
                "config": {"A": 3, "B": 1, "C": 3},
            },
            {
                "description": {"B": "1"},
                "config": {"A": 3, "B": 1, "C": 4},
            }
            {
                "description": {"B": "2"},
                "config": {"A": 3, "B": 2, "C": 3},
            }
            {
                "description": {"B": "2"},
                "config": {"A": 3, "B": 2, "C": 4},
            }
        ]
    """
    config = copy.deepcopy(config)

    sweeplist_list = _find_sweeplist(config, [])
    sweeplist_manager = _SweepListManager(sweeplist_list)

    config_list = []
    for sweepgroup_list in sweeplist_manager:
        config_description_dict = {}
        for sweepgroup in sweepgroup_list:
            for config_info in sweepgroup:
                config_value = config_info["config_value"]
                description = config_info["description"]
                key_list = config_info["key_list"]

                _set_value(config, key_list, config_value)
                if not isinstance(description, _NoDescription):
                    _set_value(config_description_dict, key_list, description, make_sub_dict=True)

        config_list.append({"description": config_description_dict, "config": copy.deepcopy(config)})

    return config_list

sweep_native 

sweep_native(options: Dict[str, List[Any]]) -> List[Dict]

SweepList 없이 native list 를 이용해 sweep을 수행합니다.

Example

config = { "A": [3], "B": [1, 2], "C": [3, 4], }

configs = sweep_native(config)

results: configs => [ {"A": 3, "B": 1, "C": 3}, {"A": 3, "B": 1, "C": 4}, {"A": 3, "B": 2, "C": 3}, {"A": 3, "B": 2, "C": 4}, ]

Source code in ResearchToolkit/experiment/sweep.py 
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def sweep_native(options: Dict[str, List[Any]]) -> List[Dict]:
    """SweepList 없이 native list 를 이용해 sweep을 수행합니다.

    Example:
        config = {
            "A": [3],
            "B": [1, 2],
            "C": [3, 4],
        }

        configs = sweep_native(config)

        results:
        configs => [
            {"A": 3, "B": 1, "C": 3},
            {"A": 3, "B": 1, "C": 4},
            {"A": 3, "B": 2, "C": 3},
            {"A": 3, "B": 2, "C": 4},
        ]
    """
    option_tuples = [[(name, value) for value in values] for name, values in options.items()]
    return [dict(option) for option in itertools.product(*option_tuples)]