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""" Common type definitions that are used across AIMET """
from enum import Enum
from typing import List, Optional, Union
import torch.utils.data
from aimet_common.defs import GreedySelectionParameters, RankSelectScheme
[docs]class ModuleCompRatioPair:
"""
Pair of torch.nn.module and a compression-ratio
:ivar module: Module of type torch.nn.module
: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: torch.nn.Module, comp_ratio: float):
self.module = module
self.comp_ratio = comp_ratio
class OpToIOTensors:
"""
Data class to store the input and output tensor names of an operation as a lists.
"""
def __init__(self, node_inputs: List[str], node_outputs: List[str]):
"""
:param node_inputs: name of inputs to the node
:param node_outputs: name of output from the node
"""
self.inputs = node_inputs
self.outputs = node_outputs
[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[torch.nn.Module]] = 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, mode: Mode, params: Union[ManualModeParams, AutoModeParams], multiplicity=1):
"""
: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.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[torch.nn.Module]] = 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, data_loader: torch.utils.data.DataLoader, num_reconstruction_samples: int,
allow_custom_downsample_ops: bool,
mode: Mode, params: Union[ManualModeParams, AutoModeParams], multiplicity=1):
self.data_loader = data_loader
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
[docs]class WeightSvdParameters:
""" Configuration parameters for weight svd compression """
[docs] class ManualModeParams:
"""
Configuration parameters for manual-mode weight 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,
rank_select_scheme: RankSelectScheme,
select_params: Union[GreedySelectionParameters, ],
modules_to_ignore: Optional[List[torch.nn.Module]] = None):
"""
:param rank_select_scheme: supports two options greedy and tar
:param select_params: Params for greedy/TAR comp-ratio selection algorithm
:param modules_to_ignore: List of modules to ignore (None indicates nothing to ignore)
"""
self.rank_select_scheme = rank_select_scheme
self.select_params = 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, mode: Mode, params: Union[ManualModeParams, AutoModeParams], multiplicity=1):
"""
: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.mode = mode
self.mode_params = params
self.multiplicity = multiplicity
class PassThroughOp(torch.nn.Module):
"""
This is a pass-through op, used for purpose of making an op a no-op
"""
# pylint:disable=arguments-differ
@staticmethod
def forward(inputx):
"""
Forward pass for passthrough op
"""
return inputx