Source code for sparseml.sparsification.info

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"""
Functionality related to describing availability and information of sparsification
algorithms to models within in the ML frameworks.

The file is executable and will get the sparsification info for a given framework:

##########
Command help:
usage: info.py [-h] [--path PATH] framework

Compile the available setup and information for the sparsification of a model
in a given framework.

positional arguments:
  framework    the ML framework or path to a framework file to load the
               sparsification info for

optional arguments:
  -h, --help   show this help message and exit
  --path PATH  A full file path to save the sparsification info to. If not
               supplied, will print out the sparsification info to the
               console.

#########
EXAMPLES
#########

##########
Example command for getting the sparsification info for pytorch.
python src/sparseml/sparsification/info.py pytorch
"""

import argparse
import logging
import os
from enum import Enum
from typing import Any, List, Optional

from pydantic import BaseModel, Field

from sparseml.base import execute_in_sparseml_framework
from sparseml.utils import clean_path, create_parent_dirs


__all__ = [
    "ModifierType",
    "ModifierPropInfo",
    "ModifierInfo",
    "SparsificationInfo",
    "sparsification_info",
    "save_sparsification_info",
    "load_sparsification_info",
]


_LOGGER = logging.getLogger(__name__)


[docs]class ModifierType(Enum): """ Types of modifiers for grouping what functionality a Modifier falls under. """ general = "general" training = "training" pruning = "pruning" quantization = "quantization" act_sparsity = "act_sparsity" misc = "misc"
[docs]class ModifierPropInfo(BaseModel): """ Class for storing information and associated metadata for a property on a given Modifier. Extends pydantics BaseModel class for serialization to and from json in addition to proper type checking on construction. """ name: str = Field( title="name", description=( "Name of the property for a Modifier. " "It can be accessed by this name on the modifier instance." ), ) description: str = Field( title="description", description="Description and information for the property for a Modifier.", ) type_: str = Field( title="type_", description=( "The format type for the property for a Modifier such as " "int, float, str, etc." ), ) restrictions: Optional[List[Any]] = Field( default=None, title="restrictions", description=( "Value restrictions for the property for a Modifier. " "If set, restrict the set value to one of the contained restrictions." ), )
[docs]class ModifierInfo(BaseModel): """ Class for storing information and associated metadata for a given Modifier. Extends pydantics BaseModel class for serialization to and from json in addition to proper type checking on construction. """ name: str = Field( title="name", description=( "Name/class of the Modifier to be used for construction and identification." ), ) description: str = Field( title="description", description="Description and info for the Modifier and what its used for.", ) type_: ModifierType = Field( default=ModifierType.misc, title="type_", description=( "The type the given Modifier is for grouping by similar functionality." ), ) props: List[ModifierPropInfo] = Field( default=[], title="props", description="The properties for the Modifier that can be set and controlled.", ) warnings: Optional[List[str]] = Field( default=None, title="warnings", description=( "Any warnings that apply for the Modifier and using it within a system" ), )
[docs]class SparsificationInfo(BaseModel): """ Class for storing the information for sparsifying in a given framework. Extends pydantics BaseModel class for serialization to and from json in addition to proper type checking on construction. """ modifiers: List[ModifierInfo] = Field( default=[], title="modifiers", description="A list of the information for the available modifiers", )
[docs] def type_modifiers(self, type_: ModifierType) -> List[ModifierInfo]: """ Get the contained Modifiers for a specific ModifierType. :param type_: The ModifierType to filter the returned list of Modifiers by. :type type_: ModifierType :return: The filtered list of Modifiers that match the given type_. :rtype: List[ModifierInfo] """ modifiers = [] for mod in self.modifiers: if mod.type_ == type_: modifiers.append(mod) return modifiers
[docs]def sparsification_info(framework: Any) -> SparsificationInfo: """ Get the available setup for sparsifying model in the given framework. :param framework: The item to detect the ML framework for. See :func:`detect_framework` for more information. :type framework: Any :return: The sparsification info for the given framework :rtype: SparsificationInfo """ _LOGGER.debug("getting sparsification info for framework %s", framework) info: SparsificationInfo = execute_in_sparseml_framework( framework, "sparsification_info" ) _LOGGER.info("retrieved sparsification info for framework %s: %s", framework, info) return info
[docs]def save_sparsification_info(framework: Any, path: Optional[str] = None): """ Save the sparsification info for a given framework. If path is provided, will save to a json file at that path. If path is not provided, will print out the info. :param framework: The item to detect the ML framework for. See :func:`detect_framework` for more information. :type framework: Any :param path: The path, if any, to save the info to in json format. If not provided will print out the info. :type path: Optional[str] """ _LOGGER.debug( "saving sparsification info for framework %s to %s", framework, path if path else "sys.out", ) info = ( sparsification_info(framework) if not isinstance(framework, SparsificationInfo) else framework ) if path: path = clean_path(path) create_parent_dirs(path) with open(path, "w") as file: file.write(info.json()) _LOGGER.info( "saved sparsification info for framework %s in file at %s", framework, path ), else: print(info.json(indent=4)) _LOGGER.info("printed out sparsification info for framework %s", framework)
[docs]def load_sparsification_info(load: str) -> SparsificationInfo: """ Load the sparsification info from a file or raw json. If load exists as a path, will read from the file and use that. Otherwise will try to parse the input as a raw json str. :param load: Either a file path to a json file or a raw json string. :type load: str :return: The loaded sparsification info. :rtype: SparsificationInfo """ load_path = clean_path(load) if os.path.exists(load_path): with open(load_path, "r") as file: load = file.read() info = SparsificationInfo.parse_raw(load) return info
def _parse_args(): parser = argparse.ArgumentParser( description=( "Compile the available setup and information for the sparsification " "of a model in a given framework." ) ) parser.add_argument( "framework", type=str, help=( "the ML framework or path to a framework file to load the " "sparsification info for" ), ) parser.add_argument( "--path", type=str, default=None, help=( "A full file path to save the sparsification info to. " "If not supplied, will print out the sparsification info to the console." ), ) return parser.parse_args() def _main(): args = _parse_args() save_sparsification_info(args.framework, args.path) if __name__ == "__main__": _main()