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util

[ allennlp.common.util ]


Various utilities that don't fit anywhere else.

JsonDict#

JsonDict = Dict[str, Any]

START_SYMBOL#

START_SYMBOL = "@start@"

END_SYMBOL#

END_SYMBOL = "@end@"

PathType#

PathType = Union[os.PathLike, str]

T#

T = TypeVar("T")

ContextManagerFunctionReturnType#

ContextManagerFunctionReturnType = Generator[T, None, None]

sanitize#

def sanitize(x: Any) -> Any

Sanitize turns PyTorch and Numpy types into basic Python types so they can be serialized into JSON.

group_by_count#

def group_by_count(
    iterable: List[Any],
    count: int,
    default_value: Any
) -> List[List[Any]]

Takes a list and groups it into sublists of size count, using default_value to pad the list at the end if the list is not divisable by count.

For example:

>>> group_by_count([1, 2, 3, 4, 5, 6, 7], 3, 0)
[[1, 2, 3], [4, 5, 6], [7, 0, 0]]

This is a short method, but it's complicated and hard to remember as a one-liner, so we just make a function out of it.

A#

A = TypeVar("A")

lazy_groups_of#

def lazy_groups_of(
    iterable: Iterable[A],
    group_size: int
) -> Iterator[List[A]]

Takes an iterable and batches the individual instances into lists of the specified size. The last list may be smaller if there are instances left over.

pad_sequence_to_length#

def pad_sequence_to_length(
    sequence: List,
    desired_length: int,
    default_value: Callable[[], Any] = lambda: 0,
    padding_on_right: bool = True
) -> List

Take a list of objects and pads it to the desired length, returning the padded list. The original list is not modified.

Parameters

  • sequence : List
    A list of objects to be padded.

  • desired_length : int
    Maximum length of each sequence. Longer sequences are truncated to this length, and shorter ones are padded to it.

  • default_value : Callable, optional (default = lambda: 0)
    Callable that outputs a default value (of any type) to use as padding values. This is a lambda to avoid using the same object when the default value is more complex, like a list.

  • padding_on_right : bool, optional (default = True)
    When we add padding tokens (or truncate the sequence), should we do it on the right or the left?

Returns

  • padded_sequence : List

add_noise_to_dict_values#

def add_noise_to_dict_values(
    dictionary: Dict[A, float],
    noise_param: float
) -> Dict[A, float]

Returns a new dictionary with noise added to every key in dictionary. The noise is uniformly distributed within noise_param percent of the value for every value in the dictionary.

namespace_match#

def namespace_match(pattern: str, namespace: str)

Matches a namespace pattern against a namespace string. For example, *tags matches passage_tags and question_tags and tokens matches tokens but not stemmed_tokens.

prepare_environment#

def prepare_environment(params: Params)

Sets random seeds for reproducible experiments. This may not work as expected if you use this from within a python project in which you have already imported Pytorch. If you use the scripts/run_model.py entry point to training models with this library, your experiments should be reasonably reproducible. If you are using this from your own project, you will want to call this function before importing Pytorch. Complete determinism is very difficult to achieve with libraries doing optimized linear algebra due to massively parallel execution, which is exacerbated by using GPUs.

Parameters

  • params : Params
    A Params object or dict holding the json parameters.

LOADED_SPACY_MODELS#

LOADED_SPACY_MODELS = {}

get_spacy_model#

def get_spacy_model(
    spacy_model_name: str,
    pos_tags: bool,
    parse: bool,
    ner: bool
) -> SpacyModelType

In order to avoid loading spacy models a whole bunch of times, we'll save references to them, keyed by the options we used to create the spacy model, so any particular configuration only gets loaded once.

pushd#

@contextmanager
def pushd(
    new_dir: PathType,
    verbose: bool = False
) -> ContextManagerFunctionReturnType[None]

Changes the current directory to the given path and prepends it to sys.path.

This method is intended to use with with, so after its usage, the current directory will be set to the previous value.

push_python_path#

@contextmanager
def push_python_path(
    path: PathType
) -> ContextManagerFunctionReturnType[None]

Prepends the given path to sys.path.

This method is intended to use with with, so after its usage, its value willbe removed from sys.path.

import_module_and_submodules#

def import_module_and_submodules(package_name: str) -> None

Import all submodules under the given package. Primarily useful so that people using AllenNLP as a library can specify their own custom packages and have their custom classes get loaded and registered.

peak_memory_mb#

def peak_memory_mb() -> float

Get peak memory usage for this process, as measured by max-resident-set size:

https://unix.stackexchange.com/questions/30940/getrusage-system-call-what-is-maximum-resident-set-size

Only works on OSX and Linux, returns 0.0 otherwise.

gpu_memory_mb#

def gpu_memory_mb() -> Dict[int, int]

Get the current GPU memory usage. Based on https://discuss.pytorch.org/t/access-gpu-memory-usage-in-pytorch/3192/4

Returns

  • Dict[int, int]
    Keys are device ids as integers. Values are memory usage as integers in MB. Returns an empty dict if GPUs are not available.

ensure_list#

def ensure_list(iterable: Iterable[A]) -> List[A]

An Iterable may be a list or a generator. This ensures we get a list without making an unnecessary copy.

is_lazy#

def is_lazy(iterable: Iterable[A]) -> bool

Checks if the given iterable is lazy, which here just means it's not a list.

int_to_device#

def int_to_device(device: Union[int, torch.device]) -> torch.device

log_frozen_and_tunable_parameter_names#

def log_frozen_and_tunable_parameter_names(
    model: torch.nn.Module
) -> None

get_frozen_and_tunable_parameter_names#

def get_frozen_and_tunable_parameter_names(
    model: torch.nn.Module
) -> Tuple[Iterable[str], Iterable[str]]

dump_metrics#

def dump_metrics(
    file_path: Optional[str],
    metrics: Dict[str, Any],
    log: bool = False
) -> None

flatten_filename#

def flatten_filename(file_path: str) -> str

is_master#

def is_master(
    global_rank: int = None,
    world_size: int = None,
    num_procs_per_node: int = None
) -> bool

Checks if the process is a "master" of its node in a distributed process group. If a process group is not initialized, this returns True.

Parameters

  • global_rank : int, optional (default = None)
    Global rank of the process if in a distributed process group. If not given, rank is obtained using torch.distributed.get_rank()
  • world_size : int, optional (default = None)
    Number of processes in the distributed group. If not given, this is obtained using torch.distributed.get_world_size()
  • num_procs_per_node : int, optional (default = None)
    Number of GPU processes running per node

is_distributed#

def is_distributed() -> bool

Checks if the distributed process group is available and has been initialized

sanitize_wordpiece#

def sanitize_wordpiece(wordpiece: str) -> str

Sanitizes wordpieces from BERT, RoBERTa or ALBERT tokenizers.

sanitize_ptb_tokenized_string#

def sanitize_ptb_tokenized_string(text: str) -> str

Sanitizes string that was tokenized using PTBTokenizer