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108 lines
4.0 KiB
108 lines
4.0 KiB
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import numpy as np
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import collections
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from .... import layers
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__all__ = ['Pruner', 'StructurePruner']
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class Pruner(object):
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"""
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Base class of all pruners.
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"""
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def __init__(self):
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pass
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def prune(self, param):
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pass
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class StructurePruner(Pruner):
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"""
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Pruner used to pruning parameters by groups.
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"""
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def __init__(self, pruning_axis, criterions):
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"""
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Args:
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pruning_axis(dict): The key is the name of parameter to be pruned,
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'*' means all the parameters.
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The value is the axis to be used. Given a parameter
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with shape [3, 4], the result of pruning 50% on aixs 1
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is a parameter with shape [3, 2].
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criterions(dict): The key is the name of parameter to be pruned,
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'*' means all the parameters.
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The value is the criterion used to sort groups for pruning.
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It only supports 'l1_norm' currently.
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"""
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self.pruning_axis = pruning_axis
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self.criterions = criterions
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def cal_pruned_idx(self, name, param, ratio, axis=None):
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"""
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Calculate the index to be pruned on axis by given pruning ratio.
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Args:
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name(str): The name of parameter to be pruned.
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param(np.array): The data of parameter to be pruned.
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ratio(float): The ratio to be pruned.
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axis(int): The axis to be used for pruning given parameter.
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If it is None, the value in self.pruning_axis will be used.
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default: None.
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Returns:
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list<int>: The indexes to be pruned on axis.
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"""
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criterion = self.criterions[
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name] if name in self.criterions else self.criterions['*']
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if axis is None:
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assert self.pruning_axis is not None, "pruning_axis should set if axis is None."
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axis = self.pruning_axis[
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name] if name in self.pruning_axis else self.pruning_axis['*']
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prune_num = int(round(param.shape[axis] * ratio))
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reduce_dims = [i for i in range(len(param.shape)) if i != axis]
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if criterion == 'l1_norm':
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criterions = np.sum(np.abs(param), axis=tuple(reduce_dims))
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pruned_idx = criterions.argsort()[:prune_num]
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return pruned_idx
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def prune_tensor(self, tensor, pruned_idx, pruned_axis, lazy=False):
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"""
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Pruning a array by indexes on given axis.
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Args:
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tensor(numpy.array): The target array to be pruned.
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pruned_idx(list<int>): The indexes to be pruned.
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pruned_axis(int): The axis of given array to be pruned on.
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lazy(bool): True means setting the pruned elements to zero.
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False means remove the pruned elements from memory.
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default: False.
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Returns:
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numpy.array: The pruned array.
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"""
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mask = np.zeros(tensor.shape[pruned_axis], dtype=bool)
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mask[pruned_idx] = True
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def func(data):
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return data[~mask]
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def lazy_func(data):
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data[mask] = 0
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return data
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if lazy:
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return np.apply_along_axis(lazy_func, pruned_axis, tensor)
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else:
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return np.apply_along_axis(func, pruned_axis, tensor)
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