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""" Common type definitions that are used across aimet """
from enum import Enum
from typing import List, Optional, Union
from dataclasses import dataclass
import tensorflow as tf
from aimet_common.defs import GreedySelectionParameters
[docs]class ModuleCompRatioPair:
"""
Pair of tf.Operation and a compression-ratio
:ivar module: Module of type tf.Operation
:ivar comp_ratio: Compression ratio. Compression ratio is the ratio of cost of compressed model
to cost of the original model.
"""
def __init__(self, module: tf.Operation, comp_ratio: float):
self.module = module
self.comp_ratio = comp_ratio
[docs]class SpatialSvdParameters:
""" Configuration parameters for spatial svd compression """
[docs] class ManualModeParams:
"""
Configuration parameters for manual-mode spatial svd compression
"""
def __init__(self, list_of_module_comp_ratio_pairs: List[ModuleCompRatioPair]):
"""
:param list_of_module_comp_ratio_pairs: List of (module, comp-ratio) pairs
"""
self.list_of_module_comp_ratio_pairs = list_of_module_comp_ratio_pairs
[docs] class AutoModeParams:
"""
Configuration parameters for auto-mode compression
"""
def __init__(self, greedy_select_params: GreedySelectionParameters,
modules_to_ignore: Optional[List[tf.Operation]] = None):
"""
:param greedy_select_params: Params for greedy comp-ratio selection algorithm
:param modules_to_ignore: List of modules to ignore (None indicates nothing to ignore)
"""
self.greedy_params = greedy_select_params
self.modules_to_ignore = [] if modules_to_ignore is None else modules_to_ignore
[docs] class Mode(Enum):
""" Mode enumeration """
manual = 1
""" Manual mode """
auto = 2
""" Auto mode """
def __init__(self, input_op_names: List[str], output_op_names: List[str], mode: Mode,
params: Union[ManualModeParams, AutoModeParams], multiplicity=1):
"""
:param input_op_names: list of input op names to the model
:param output_op_names: List of output op names of the model
:param mode: Either auto mode or manual mode
:param params: Parameters for the mode selected
:param multiplicity: The multiplicity to which ranks/input channels will get rounded. Default: 1
"""
self.input_op_names = input_op_names
self.output_op_names = output_op_names
self.mode = mode
self.mode_params = params
self.multiplicity = multiplicity
[docs]class ChannelPruningParameters:
""" Configuration parameters for channel pruning compression """
[docs] class ManualModeParams:
"""
Configuration parameters for manual-mode channel pruning compression
"""
def __init__(self, list_of_module_comp_ratio_pairs: List[ModuleCompRatioPair]):
"""
:param list_of_module_comp_ratio_pairs: List of (module, comp-ratio) pairs
"""
self.list_of_module_comp_ratio_pairs = list_of_module_comp_ratio_pairs
[docs] class AutoModeParams:
"""
Configuration parameters for auto-mode compression
"""
def __init__(self, greedy_select_params: GreedySelectionParameters,
modules_to_ignore: Optional[List[tf.Operation]] = None):
"""
:param greedy_select_params: Params for greedy comp-ratio selection algorithm
:param modules_to_ignore: List of modules to ignore (None indicates nothing to ignore)
"""
self.greedy_params = greedy_select_params
self.modules_to_ignore = [] if modules_to_ignore is None else modules_to_ignore
[docs] class Mode(Enum):
""" Mode enumeration """
manual = 1
""" Manual mode: User specifies comp-ratio per layer """
auto = 2
""" Auto mode: aimet computes optimal comp-ratio per layer """
def __init__(self, input_op_names: List[str], output_op_names: List[str], data_set: tf.data.Dataset,
batch_size: int, num_reconstruction_samples: int, allow_custom_downsample_ops: bool, mode: Mode,
params: Union[ManualModeParams, AutoModeParams], multiplicity=1):
"""
:param input_op_names: list of input op names to the model
:param output_op_names: List of output op names of the model
:param data_set: data set
:param batch_size: batch size
:param num_reconstruction_samples: number of samples to be used for reconstruction
:param allow_custom_downsample_ops: If set to True, DownSampleLayer and UpSampleLayer will be added as required
:param mode: indicates whether the mode is manual or auto
:param params: ManualModeParams or AutoModeParams, depending on teh value of mode
:param multiplicity: The multiplicity to which ranks/input channels will get rounded. Default: 1
"""
# pylint: disable=too-many-arguments
self.input_op_names = input_op_names
self.output_op_names = output_op_names
self.data_set = data_set
self.batch_size = batch_size
self.num_reconstruction_samples = num_reconstruction_samples
self.allow_custom_downsample_ops = allow_custom_downsample_ops
self.mode = mode
self.mode_params = params
self.multiplicity = multiplicity
class ParameterInfo:
""" Store information required for parameter quantization """
def __init__(self, param_type: str, op_with_param_name: List):
self.param_type = param_type
self.op_with_param_name = op_with_param_name
@dataclass
class Tensorflow2Version:
"""
Enumeration for checking TensorFlow version for import statements
"""
v2_4_3 = '2.4.3'
v2_10_1 = '2.10.1'