# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # Copyright (c) 2021 NVIDIA Corporation. All rights reserved. # # 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 # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import print_function import unittest import threading, time import paddle from paddle.fluid.contrib import sparsity import numpy as np class TestASPUtils(unittest.TestCase): def test_get_check_method(self): self.assertEqual( sparsity.CheckMethod.get_checking_method(sparsity.MaskAlgo.MASK_1D), sparsity.CheckMethod.CHECK_1D) self.assertEqual( sparsity.CheckMethod.get_checking_method( sparsity.MaskAlgo.MASK_2D_GREEDY), sparsity.CheckMethod.CHECK_2D) self.assertEqual( sparsity.CheckMethod.get_checking_method( sparsity.MaskAlgo.MASK_2D_BEST), sparsity.CheckMethod.CHECK_2D) def test_density(self): x = np.array([[1.0, 1.0, 1.0, 0.0, 1.0], [1.0, 1.0, 0.0, 0.0, 1.0], [1.0, 0.0, 0.0, 0.0, 1.0], [1.0, 1.0, 0.0, 0.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0]]) self.assertEqual(sparsity.density(x), 0.56) x[:, 0] = 0.0 self.assertEqual(sparsity.density(x), 0.4) def test_check_mask_1d(self): x = np.array([[1.0, 0.0, 0.0, 1.0, 1.0], [1.0, 1.0, 0.0, 0.0, 1.0], [1.0, 1.0, 0.0, 0.0, 1.0], [1.0, 1.0, 0.0, 0.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0]]) self.assertTrue(sparsity.check_mask_1d(x, 2, 4)) self.assertFalse(sparsity.check_mask_1d(x, 3, 4)) self.assertTrue(sparsity.check_mask_1d(x, 2, 5)) self.assertFalse(sparsity.check_mask_1d(x, 3, 5)) self.assertTrue(sparsity.check_mask_1d(x, 3, 6)) self.assertFalse(sparsity.check_mask_1d(x, 4, 6)) def test_get_mask_1d(self): for _ in range(10): x = np.random.randint(10, size=(5, 5)) x = sparsity.get_mask_1d(x, 2, 4) self.assertTrue(sparsity.check_mask_1d(x, 2, 4)) x = np.random.randn(5, 4) x = sparsity.get_mask_1d(x, 2, 4) self.assertTrue(sparsity.check_mask_1d(x, 2, 4)) def test_check_mask_2d(self): x = np.array([[1.0, 0.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0, 1.0], [1.0, 1.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0, 1.0]]) self.assertTrue(sparsity.check_mask_2d(x, 2, 4)) self.assertFalse(sparsity.check_mask_2d(x, 3, 4)) self.assertTrue(sparsity.check_mask_2d(x, 2, 5)) self.assertFalse(sparsity.check_mask_2d(x, 3, 5)) self.assertTrue(sparsity.check_mask_2d(x, 3, 6)) self.assertFalse(sparsity.check_mask_2d(x, 4, 6)) def test_get_mask_2d_greedy(self): for _ in range(10): x = np.random.randint(10, size=(5, 5)) x = sparsity.get_mask_2d_greedy(x, 2, 4) self.assertTrue(sparsity.check_mask_2d(x, 2, 4)) x = np.random.randn(5, 4) x = sparsity.get_mask_2d_greedy(x, 2, 4) self.assertTrue(sparsity.check_mask_2d(x, 2, 4)) def test_get_mask_2d_best(self): for _ in range(10): x = np.random.randint(10, size=(5, 5)) x = sparsity.get_mask_2d_best(x, 2, 4) self.assertTrue(sparsity.check_mask_2d(x, 2, 4)) x = np.random.randn(5, 4) x = sparsity.get_mask_2d_best(x, 2, 4) self.assertTrue(sparsity.check_mask_2d(x, 2, 4)) def test_threadsafe_valid_2d_patterns(self): def get_reference(m=4, n=2): from itertools import permutations patterns = np.zeros(m) patterns[:n] = 1 patterns = list(set(permutations(patterns.tolist()))) patterns = patterns + patterns patterns = np.asarray(list(set(permutations(patterns, m)))) valid = ((patterns.sum(axis=1) <= n).sum(axis=1) == m ).nonzero()[0].reshape(-1) valid_patterns = np.empty((valid.shape[0], m, m)) valid_patterns[:] = patterns[valid[:]] return valid_patterns for _ in range(4): computing_thread = threading.Thread( target=paddle.fluid.contrib.sparsity.utils. compute_valid_2d_patterns, args=(2, 4)) computing_thread.start() time.sleep(3) patterns_map = paddle.fluid.contrib.sparsity.utils.valid_2d_patterns reference_patterns = get_reference() reference_key = '4_2' self.assertTrue(reference_key in patterns_map) self.assertTrue(len(patterns_map) == 1) self.assertTrue((reference_patterns == patterns_map[reference_key]).all( )) def test_check_sparsity(self): for _ in range(10): x = np.random.randint(10, size=(5)) x_2d = x.reshape(1, x.shape[0]) self.__test_1D_2D_sparsity_checking_methods(x_2d) x = np.random.randint(10, size=(5, 5)) x_2d = x self.__test_1D_2D_sparsity_checking_methods(x_2d) x = np.random.randint(10, size=(5, 5, 5)) x_2d = x.reshape(x.shape[0] * x.shape[1], x.shape[2]) self.__test_1D_2D_sparsity_checking_methods(x_2d) x = np.random.randint(10, size=(5, 5, 5, 5)) x_2d = x.reshape(x.shape[0], x.shape[1] * x.shape[2] * x.shape[3]) self.__test_1D_2D_sparsity_checking_methods(x_2d) def test_create_mask(self): for _ in range(10): x = np.random.randint(10, size=(5)) self.__test_1D_2D_sparse_mask_generation_methods(x) x = np.random.randint(10, size=(5, 5)) self.__test_1D_2D_sparse_mask_generation_methods(x) x = np.random.randint(10, size=(5, 5, 5)) self.__test_1D_2D_sparse_mask_generation_methods(x) x = np.random.randint(10, size=(5, 5, 5, 5)) self.__test_1D_2D_sparse_mask_generation_methods(x) def __test_1D_2D_sparsity_checking_methods(self, x_2d): mask = sparsity.get_mask_1d(x_2d, 2, 4) self.assertEqual( sparsity.check_sparsity( mask, func_name=sparsity.CheckMethod.CHECK_1D, n=2, m=4), sparsity.check_mask_1d(mask, 2, 4)) mask = sparsity.get_mask_2d_best(x_2d, 2, 4) self.assertEqual( sparsity.check_sparsity( mask, func_name=sparsity.CheckMethod.CHECK_2D, n=2, m=4), sparsity.check_mask_2d(mask, 2, 4)) def __test_1D_2D_sparse_mask_generation_methods(self, x): mask = sparsity.create_mask( x, func_name=sparsity.MaskAlgo.MASK_1D, n=2, m=4) self.assertTrue( sparsity.check_sparsity( mask, func_name=sparsity.CheckMethod.CHECK_1D, n=2, m=4)) mask = sparsity.create_mask( x, func_name=sparsity.MaskAlgo.MASK_2D_GREEDY, n=2, m=4) self.assertTrue( sparsity.check_sparsity( mask, func_name=sparsity.CheckMethod.CHECK_2D, n=2, m=4)) mask = sparsity.create_mask( x, func_name=sparsity.MaskAlgo.MASK_2D_BEST, n=2, m=4) self.assertTrue( sparsity.check_sparsity( mask, func_name=sparsity.CheckMethod.CHECK_2D, n=2, m=4))