Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into thinnerdocker
yi.wu
9 years ago
commit
c1d5aaa15c
@ -1,9 +1,9 @@
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# Use ccache if found ccache program
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find_program(CCACHE_FOUND ccache)
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find_program(CCACHE_PATH ccache)
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if(CCACHE_FOUND)
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if(CCACHE_PATH)
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message(STATUS "Ccache is founded, use ccache to speed up compile.")
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set_property(GLOBAL PROPERTY RULE_LAUNCH_COMPILE ccache)
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set_property(GLOBAL PROPERTY RULE_LAUNCH_LINK ccache)
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endif(CCACHE_FOUND)
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set_property(GLOBAL PROPERTY RULE_LAUNCH_COMPILE ${CCACHE_PATH})
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set_property(GLOBAL PROPERTY RULE_LAUNCH_LINK ${CCACHE_PATH})
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endif(CCACHE_PATH)
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@ -0,0 +1,174 @@
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/* Copyright (c) 2016 Baidu, Inc. All Rights Reserve.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#include <gtest/gtest.h>
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#include "ModelConfig.pb.h"
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#include "paddle/gserver/layers/DataLayer.h"
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#include "paddle/gserver/layers/LinearChainCRF.h"
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#include "paddle/trainer/Trainer.h"
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#include "LayerGradUtil.h"
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#include "paddle/testing/TestUtil.h"
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using namespace paddle; // NOLINT
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DECLARE_int32(gpu_id);
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DECLARE_bool(thread_local_rand_use_global_seed);
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static inline bool getNextSequence(std::vector<int>& seq, int numClasses) {
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for (auto& v : seq) {
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if (++v < numClasses) {
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return true;
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}
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v = 0;
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}
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return false;
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}
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// log(exp(x) + exp(y))
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static inline real logSum(real x, real y) {
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real maxValue = std::max(x, y);
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if (std::isinf(maxValue)) {
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return -std::numeric_limits<real>::infinity();
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} else {
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return maxValue + log(exp(x - maxValue) + exp(y - maxValue));
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}
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}
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static inline std::vector<int> genRandLabels(int numClasses, int length) {
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std::vector<int> labels(length);
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for (int i = 0; i < length; ++i) {
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labels[i] = rand() % numClasses; // NOLINT
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}
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return labels;
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}
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TEST(CRFLayer, cost) {
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const int numClasses = 4;
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CpuVector para(numClasses * (numClasses + 2));
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real* a = para.getData();
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real* b = para.getData() + numClasses;
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real* w = para.getData() + 2 * numClasses;
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LinearChainCRF crf(4, para.getData());
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for (int length : {1, 2, 3, 10}) {
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for (int tries = 0; tries < 10; ++tries) {
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CpuMatrix x(length, numClasses);
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x.randomizeUniform();
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para.randnorm(0, 2);
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std::vector<int> goldenLabels = genRandLabels(numClasses, length);
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real cost = crf.forward(x.getData(), goldenLabels.data(), length);
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real logZ = -std::numeric_limits<real>::infinity();
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real logNominator = -std::numeric_limits<real>::infinity();
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std::vector<int> testResult(length, 0);
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do {
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real score = a[testResult.front()];
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score += x.getElement(0, testResult.front());
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for (int k = 1; k < length; ++k) {
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score += x.getElement(k, testResult[k]) +
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w[numClasses * testResult[k - 1] + testResult[k]];
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}
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score += b[testResult.back()];
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logZ = logSum(logZ, score);
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if (goldenLabels == testResult) {
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logNominator = score;
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}
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} while (getNextSequence(testResult, numClasses));
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real trueCost = -logNominator + logZ;
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real diff = fabs(trueCost - cost);
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diff /= fabs(cost) < fabs(trueCost) ? fabs(cost) : fabs(trueCost);
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VLOG(1) << "cost=" << cost << " trueCost=" << trueCost << " diff=" << diff
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<< std::endl;
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if (typeid(real) == typeid(double)) { // NOLINT
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EXPECT_LE(diff, 1e-10);
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} else {
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EXPECT_LE(diff, 5e-3);
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}
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}
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}
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}
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inline real epsilon() { return typeid(real) == typeid(double) ? 1e-10 : 0.06; }
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TestConfig initTestConfig(size_t numClasses, bool withWeight) {
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TestConfig config;
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config.layerConfig.set_type("crf");
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config.layerConfig.set_size(numClasses);
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config.biasSize = 0;
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config.inputDefs.push_back({INPUT_SEQUENCE_DATA,
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"layer_0",
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numClasses,
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numClasses * (numClasses + 2)});
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config.layerConfig.add_inputs();
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config.inputDefs.push_back(
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{INPUT_SEQUENCE_LABEL, "layer_label", numClasses, 0});
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config.layerConfig.add_inputs();
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if (withWeight) {
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config.inputDefs.push_back({INPUT_DENSE_DIM_DATA, "layer_weight", 1, 0});
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config.layerConfig.add_inputs();
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}
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return config;
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}
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TEST(Layer, CRFLayer) {
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size_t numClasses = 10;
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for (int tries = 0; tries < 5; ++tries) {
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TestConfig config = initTestConfig(numClasses, /* withWeight= */ false);
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for (int length : {1, 3, 100}) {
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// Not support GPU now
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testLayerGrad(config,
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"crf",
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length,
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/* trans= */ false,
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/* useGpu= */ false,
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/* useWeight= */ false,
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epsilon());
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}
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}
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}
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TEST(Layer, CRFLayerUseWeight) {
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size_t numClasses = 10;
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for (int tries = 0; tries < 5; ++tries) {
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TestConfig config = initTestConfig(numClasses, /* withWeight= */ true);
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for (int length : {1, 3, 100}) {
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// Not support GPU now
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testLayerGrad(config,
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"crf",
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length,
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/* trans= */ false,
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/* useGpu= */ false,
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/* useWeight= */ false,
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epsilon());
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}
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}
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}
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int main(int argc, char** argv) {
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initMain(argc, argv);
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hl_start();
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hl_init(FLAGS_gpu_id);
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FLAGS_thread_local_rand_use_global_seed = true;
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srand(1);
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testing::InitGoogleTest(&argc, argv);
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return RUN_ALL_TESTS();
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}
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