[GPU-Plugin] Integration of a faster version of grow_gpu plugin into mainstream (#2360)
* Integrating a faster version of grow_gpu plugin 1. Removed the older files to reduce duplication 2. Moved all of the grow_gpu files under 'exact' folder 3. All of them are inside 'exact' namespace to avoid any conflicts 4. Fixed a bug in benchmark.py while running only 'grow_gpu' plugin 5. Added cub and googletest submodules to ease integration and unit-testing 6. Updates to CMakeLists.txt to directly build cuda objects into libxgboost * Added support for building gpu plugins through make flow 1. updated makefile and config.mk to add right targets 2. added unit-tests for gpu exact plugin code * 1. Added support for building gpu plugin using 'make' flow as well 2. Updated instructions for building and testing gpu plugin * Fix travis-ci errors for PR#2360 1. lint errors on unit-tests 2. removed googletest, instead depended upon dmlc-core provide gtest cache * Some more fixes to travis-ci lint failures PR#2360 * Added Rory's copyrights to the files containing code from both. * updated copyright statement as per Rory's request * moved the static datasets into a script to generate them at runtime * 1. memory usage print when silent=0 2. tests/ and test/ folder organization 3. removal of the dependency of googletest for just building xgboost 4. coding style updates for .cuh as well * Fixes for compilation warnings * add cuda object files as well when JVM_BINDINGS=ON
This commit is contained in:
Thejaswi
Rory Mitchell
308
plugin/updater_gpu/test/cpp/gpu_builder.cu
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308
plugin/updater_gpu/test/cpp/gpu_builder.cu
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/*
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* Copyright (c) 2017, NVIDIA CORPORATION. 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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*/
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#include "gtest/gtest.h"
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#include "utils.cuh"
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#include "../../src/exact/gpu_builder.cuh"
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#include "../../src/exact/node.cuh"
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namespace xgboost {
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namespace tree {
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namespace exact {
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static const std::vector<int> smallColSizes = {0, 5, 0, 6, 4, 0, 0, 2, 0, 11,
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2, 9, 0, 5, 1, 0, 12, 3};
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template <typename node_id_t>
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void testSmallData() {
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GPUBuilder<node_id_t> builder;
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std::shared_ptr<DMatrix> dm =
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setupGPUBuilder<node_id_t>("plugin/updater_gpu/test/cpp/data/small.sample.libsvm",
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builder, 1);
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// data dimensions
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ASSERT_EQ(60, builder.nVals);
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ASSERT_EQ(15, builder.nRows);
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ASSERT_EQ(18, builder.nCols);
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ASSERT_TRUE(builder.allocated);
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// column counts
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int* tmpOff = new int[builder.nCols+1];
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updateHostPtr<int>(tmpOff, builder.colOffsets.data(), builder.nCols+1);
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for (int i = 0; i < 15; ++i) {
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EXPECT_EQ(smallColSizes[i], tmpOff[i+1]-tmpOff[i]);
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}
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float* tmpVal = new float[builder.nVals];
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updateHostPtr<float>(tmpVal, builder.vals.current(), builder.nVals);
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int* tmpInst = new int[builder.nVals];
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updateHostPtr<int>(tmpInst, builder.instIds.current(), builder.nVals);
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gpu_gpair* tmpGrad = new gpu_gpair[builder.nRows];
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updateHostPtr<gpu_gpair>(tmpGrad, builder.gradsInst.data(), builder.nRows);
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EXPECT_EQ(0, tmpInst[0]);
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EXPECT_FLOAT_EQ(1.f, tmpVal[0]);
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EXPECT_FLOAT_EQ(1.f+(float)(tmpInst[0]%10), get(0, tmpGrad, tmpInst).g);
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EXPECT_FLOAT_EQ(.5f+(float)(tmpInst[0]%10), get(0, tmpGrad, tmpInst).h);
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EXPECT_EQ(2, tmpInst[1]);
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EXPECT_FLOAT_EQ(1.f, tmpVal[1]);
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EXPECT_FLOAT_EQ(1.f+(float)(tmpInst[1]%10), get(1, tmpGrad, tmpInst).g);
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EXPECT_FLOAT_EQ(.5f+(float)(tmpInst[1]%10), get(1, tmpGrad, tmpInst).h);
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EXPECT_EQ(7, tmpInst[2]);
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EXPECT_FLOAT_EQ(1.f, tmpVal[2]);
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EXPECT_FLOAT_EQ(1.f+(float)(tmpInst[2]%10), get(2, tmpGrad, tmpInst).g);
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EXPECT_FLOAT_EQ(.5f+(float)(tmpInst[2]%10), get(2, tmpGrad, tmpInst).h);
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delete [] tmpGrad;
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delete [] tmpOff;
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delete [] tmpInst;
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delete [] tmpVal;
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int* colIds = new int[builder.nVals];
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updateHostPtr<int>(colIds, builder.colIds.data(), builder.nVals);
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std::vector<int> colSizeCopy(smallColSizes);
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int colIdxCurr = 0;
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for (int i = 0; i < builder.nVals; ++i) {
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while (colSizeCopy[colIdxCurr] == 0) {
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++colIdxCurr;
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}
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--colSizeCopy[colIdxCurr];
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EXPECT_EQ(colIdxCurr, colIds[i]);
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}
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delete [] colIds;
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}
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TEST(CudaGPUBuilderTest, SetupOneTimeDataSmallInt16) {
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testSmallData<int16_t>();
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}
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TEST(CudaGPUBuilderTest, SetupOneTimeDataSmallInt32) {
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testSmallData<int>();
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}
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template <typename node_id_t>
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void testLargeData() {
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GPUBuilder<node_id_t> builder;
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std::shared_ptr<DMatrix> dm =
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setupGPUBuilder<node_id_t>("plugin/updater_gpu/test/cpp/data/sample.libsvm",
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builder, 1);
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ASSERT_EQ(35442, builder.nVals);
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ASSERT_EQ(1611, builder.nRows);
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ASSERT_EQ(127, builder.nCols);
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ASSERT_TRUE(builder.allocated);
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int* tmpOff = new int[builder.nCols+1];
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updateHostPtr<int>(tmpOff, builder.colOffsets.data(), builder.nCols+1);
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EXPECT_EQ(0, tmpOff[1]-tmpOff[0]); // 1st col
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EXPECT_EQ(83, tmpOff[2]-tmpOff[1]); // 2nd col
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EXPECT_EQ(1, tmpOff[3]-tmpOff[2]); // 3rd col
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float* tmpVal = new float[builder.nVals];
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updateHostPtr<float>(tmpVal, builder.vals.current(), builder.nVals);
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int* tmpInst = new int[builder.nVals];
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updateHostPtr<int>(tmpInst, builder.instIds.current(), builder.nVals);
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gpu_gpair* tmpGrad = new gpu_gpair[builder.nRows];
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updateHostPtr<gpu_gpair>(tmpGrad, builder.gradsInst.data(), builder.nRows);
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// the order of observations is messed up before the convertToCsc call!
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// hence, the instance IDs have been manually checked and put here.
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EXPECT_EQ(1164, tmpInst[0]);
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EXPECT_FLOAT_EQ(1.f, tmpVal[0]);
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EXPECT_FLOAT_EQ(1.f+(float)(tmpInst[0]%10), get(0, tmpGrad, tmpInst).g);
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EXPECT_FLOAT_EQ(.5f+(float)(tmpInst[0]%10), get(0, tmpGrad, tmpInst).h);
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EXPECT_EQ(1435, tmpInst[1]);
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EXPECT_FLOAT_EQ(1.f, tmpVal[1]);
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EXPECT_FLOAT_EQ(1.f+(float)(tmpInst[1]%10), get(1, tmpGrad, tmpInst).g);
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EXPECT_FLOAT_EQ(.5f+(float)(tmpInst[1]%10), get(1, tmpGrad, tmpInst).h);
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EXPECT_EQ(1421, tmpInst[2]);
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EXPECT_FLOAT_EQ(1.f, tmpVal[2]);
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EXPECT_FLOAT_EQ(1.f+(float)(tmpInst[2]%10), get(2, tmpGrad, tmpInst).g);
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EXPECT_FLOAT_EQ(.5f+(float)(tmpInst[2]%10), get(2, tmpGrad, tmpInst).h);
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delete [] tmpGrad;
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delete [] tmpOff;
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delete [] tmpInst;
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delete [] tmpVal;
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}
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TEST(CudaGPUBuilderTest, SetupOneTimeDataLargeInt16) {
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testLargeData<int16_t>();
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}
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TEST(CudaGPUBuilderTest, SetupOneTimeDataLargeInt32) {
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testLargeData<int>();
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}
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int getColId(int* offsets, int id, int nCols) {
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for (int i = 1; i <= nCols; ++i) {
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if (id < offsets[i]) {
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return (i-1);
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}
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}
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return -1;
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}
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template <typename node_id_t>
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void testAllocate() {
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GPUBuilder<node_id_t> builder;
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std::shared_ptr<DMatrix> dm =
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setupGPUBuilder<node_id_t>("plugin/updater_gpu/test/cpp/data/small.sample.libsvm",
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builder, 1);
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ASSERT_EQ(3, builder.maxNodes);
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ASSERT_EQ(2, builder.maxLeaves);
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Node<node_id_t>* n = new Node<node_id_t>[builder.maxNodes];
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updateHostPtr<Node<node_id_t> >(n, builder.nodes.data(), builder.maxNodes);
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for (int i = 0; i < builder.maxNodes; ++i) {
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if (i == 0) {
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EXPECT_FALSE(n[i].isLeaf());
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EXPECT_FALSE(n[i].isUnused());
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} else {
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EXPECT_TRUE(n[i].isLeaf());
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EXPECT_FALSE(n[i].isUnused());
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}
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}
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gpu_gpair sum;
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sum.g = 0.f;
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sum.h = 0.f;
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for (int i = 0; i < builder.maxNodes; ++i) {
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if (!n[i].isUnused()) {
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sum += n[i].gradSum;
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}
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}
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// law of conservation of gradients! :)
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EXPECT_FLOAT_EQ(2.f*n[0].gradSum.g, sum.g);
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EXPECT_FLOAT_EQ(2.f*n[0].gradSum.h, sum.h);
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node_id_t* assigns = new node_id_t[builder.nVals];
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int* offsets = new int[builder.nCols+1];
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updateHostPtr<node_id_t>(assigns, builder.nodeAssigns.current(),
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builder.nVals);
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updateHostPtr<int>(offsets, builder.colOffsets.data(), builder.nCols+1);
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for (int i = 0; i < builder.nVals; ++i) {
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EXPECT_EQ((node_id_t)0, assigns[i]);
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}
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delete [] n;
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delete [] assigns;
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delete [] offsets;
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}
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TEST(CudaGPUBuilderTest, AllocateNodeDataInt16) {
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testAllocate<int16_t>();
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}
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TEST(CudaGPUBuilderTest, AllocateNodeDataInt32) {
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testAllocate<int>();
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}
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template <typename node_id_t>
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void assign(Node<node_id_t> *n, float g, float h, float sc, float wt,
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DefaultDirection d, float th, int c, int i) {
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n->gradSum.g = g;
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n->gradSum.h = h;
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n->score = sc;
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n->weight = wt;
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n->dir = d;
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n->threshold = th;
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n->colIdx = c;
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n->id = (node_id_t)i;
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}
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template <typename node_id_t>
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void testMarkLeaves() {
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GPUBuilder<node_id_t> builder;
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std::shared_ptr<DMatrix> dm =
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setupGPUBuilder<node_id_t>("plugin/updater_gpu/test/cpp/data/small.sample.libsvm",
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builder, 3);
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ASSERT_EQ(15, builder.maxNodes);
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ASSERT_EQ(8, builder.maxLeaves);
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Node<node_id_t>* hNodes = new Node<node_id_t>[builder.maxNodes];
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assign<node_id_t>(&hNodes[0], 2.f, 1.f, .75f, 0.5f, LeftDir, 0.25f, 0, 0);
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assign<node_id_t>(&hNodes[1], 2.f, 1.f, .75f, 0.5f, RightDir, 0.5f, 1, 1);
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assign<node_id_t>(&hNodes[2], 2.f, 1.f, .75f, 0.5f, LeftDir, 0.75f, 2, 2);
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assign<node_id_t>(&hNodes[3], 2.f, 1.f, .75f, 0.5f, RightDir, 1.f, 3, 3);
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assign<node_id_t>(&hNodes[4], 2.f, 1.f, .75f, 0.5f, LeftDir, 1.25f, 4, 4);
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hNodes[5] = Node<node_id_t>();
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assign<node_id_t>(&hNodes[6], 2.f, 1.f, .75f, 0.5f, LeftDir, 1.75f, 6, 6);
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hNodes[7] = Node<node_id_t>();
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hNodes[8] = Node<node_id_t>();
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hNodes[9] = Node<node_id_t>();
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hNodes[10] = Node<node_id_t>();
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hNodes[11] = Node<node_id_t>();
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hNodes[12] = Node<node_id_t>();
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hNodes[13] = Node<node_id_t>();
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hNodes[14] = Node<node_id_t>();
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updateDevicePtr<Node<node_id_t> >(builder.nodes.data(), hNodes, builder.maxNodes);
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builder.markLeaves();
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Node<node_id_t>* outNodes = new Node<node_id_t>[builder.maxNodes];
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updateHostPtr<Node<node_id_t> >(outNodes, builder.nodes.data(), builder.maxNodes);
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for (int i = 0; i < builder.maxNodes; ++i) {
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if ((i >= 7) || (i == 5)) {
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EXPECT_TRUE(outNodes[i].isUnused());
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} else {
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EXPECT_FALSE(outNodes[i].isUnused());
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}
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}
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for (int i = 0; i < builder.maxNodes; ++i) {
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if ((i == 3) || (i == 4) || (i == 6)) {
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EXPECT_TRUE(outNodes[i].isLeaf());
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} else {
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EXPECT_FALSE(outNodes[i].isLeaf());
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}
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}
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delete [] outNodes;
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delete [] hNodes;
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}
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TEST(CudaGPUBuilderTest, MarkLeavesInt16) {
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testMarkLeaves<int16_t>();
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}
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TEST(CudaGPUBuilderTest, MarkLeavesInt32) {
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testMarkLeaves<int>();
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}
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template <typename node_id_t>
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void testDense2Sparse() {
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GPUBuilder<node_id_t> builder;
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std::shared_ptr<DMatrix> dm =
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setupGPUBuilder<node_id_t>("plugin/updater_gpu/test/cpp/data/small.sample.libsvm",
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builder, 3);
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ASSERT_EQ(15, builder.maxNodes);
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ASSERT_EQ(8, builder.maxLeaves);
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Node<node_id_t>* hNodes = new Node<node_id_t>[builder.maxNodes];
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assign<node_id_t>(&hNodes[0], 2.f, 1.f, .75f, 0.5f, LeftDir, 0.25f, 0, 0);
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assign<node_id_t>(&hNodes[1], 2.f, 1.f, .75f, 0.5f, RightDir, 0.5f, 1, 1);
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assign<node_id_t>(&hNodes[2], 2.f, 1.f, .75f, 0.5f, LeftDir, 0.75f, 2, 2);
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assign<node_id_t>(&hNodes[3], 2.f, 1.f, .75f, 0.5f, RightDir, 1.f, 3, 3);
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assign<node_id_t>(&hNodes[4], 2.f, 1.f, .75f, 0.5f, LeftDir, 1.25f, 4, 4);
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hNodes[5] = Node<node_id_t>();
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assign<node_id_t>(&hNodes[6], 2.f, 1.f, .75f, 0.5f, LeftDir, 1.75f, 6, 6);
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assign<node_id_t>(&hNodes[7], 2.f, 1.f, .75f, 0.5f, LeftDir, 1.75f, 7, 7);
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hNodes[8] = Node<node_id_t>();
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hNodes[9] = Node<node_id_t>();
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hNodes[10] = Node<node_id_t>();
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hNodes[11] = Node<node_id_t>();
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hNodes[12] = Node<node_id_t>();
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hNodes[13] = Node<node_id_t>();
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hNodes[14] = Node<node_id_t>();
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updateDevicePtr<Node<node_id_t> >(builder.nodes.data(), hNodes, builder.maxNodes);
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builder.markLeaves();
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RegTree tree;
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builder.dense2sparse(tree);
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EXPECT_EQ(9, tree.param.num_nodes);
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delete [] hNodes;
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}
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TEST(CudaGPUBuilderTest, Dense2SparseInt16) {
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testDense2Sparse<int16_t>();
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}
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TEST(CudaGPUBuilderTest, Dense2SparseInt32) {
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testDense2Sparse<int>();
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}
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} // namespace exact
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} // namespace tree
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} // namespace xgboost
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