Source code for cerebras.modelzoo.config_manager.config_classes.base.sparsity_config

# Copyright 2022 Cerebras Systems.
#
# 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
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# See the License for the specific language governing permissions and
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"""
Config classes of Sparsity Configs

"""

from dataclasses import dataclass
from typing import List, Literal, Union

from cerebras.modelzoo.config_manager.config_classes.base.base_config import (
    BaseConfig,
    required,
)
from cerebras.pytorch.sparse import map_sparsity_algorithm


[docs]@dataclass class SparsityAlgorithmConfig(BaseConfig): algorithm: Literal["static", "gmp", "set", "rigl"] = "static" """ Sparsity Algorithm to apply. ["static", "gmp", "set", "rigl"] """ params: dict = required """ Parameters such as init_method, sparsity, param_name_patterns, schedule etc These vary based on the type of sparsity chosen. Please see sparsity documentaion for valid parameters for each type """ def __init__(self, **kwargs): for field_name, field_type in self.__annotations__.items(): if field_name in kwargs: setattr(self, field_name, kwargs.pop(field_name)) self.params = kwargs super().__init__() def __post_init__(self): # This will error out if we are not able to map the params to # the mentioned sparsity class map_sparsity_algorithm(algorithm=self.algorithm) super().__post_init__()
[docs]@dataclass class SparsityBaseConfig(BaseConfig): groups: List[SparsityAlgorithmConfig] = required # Custom init for sparsity, where we want to capture all fixed sparsity params as members. # sparsity params is a dict that is populated by checking all additional params supplied to us. # These are sparsity specific and we use signature of that sparsity to validate these. def __init__(self, *args, **kwargs): "Sparsity related params: see Sparsity documentation" if len(args) > 0: if len(kwargs) > 0: raise ValueError( "Expected a list of dicts or a single dict, not both" ) self.groups = [SparsityAlgorithmConfig(**group) for group in args] else: self.groups = [SparsityAlgorithmConfig(**kwargs)] super().__init__()
SparsityConfig = Union[float, SparsityBaseConfig]