cc6a5a3666
* Added finding quantiles on GPU.
- this includes datasets where weights are assigned to data rows
- as the quantiles found by the new algorithm are not the same
as those found by the old one, test thresholds in
tests/python-gpu/test_gpu_updaters.py have been adjusted.
* Adjustments and improved testing for finding quantiles on the GPU.
- added C++ tests for the DeviceSketch() function
- reduced one of the thresholds in test_gpu_updaters.py
- adjusted the cuts found by the find_cuts_k kernel
95 lines
2.5 KiB
Plaintext
95 lines
2.5 KiB
Plaintext
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/*!
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* Copyright 2017 XGBoost contributors
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*/
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#include <thrust/device_vector.h>
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#include <xgboost/base.h>
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#include "../helpers.h"
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#include "gtest/gtest.h"
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#include "../../../src/data/sparse_page_source.h"
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#include "../../../src/gbm/gbtree_model.h"
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#include "../../../src/tree/updater_gpu_hist.cu"
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namespace xgboost {
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namespace tree {
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TEST(gpu_hist_experimental, TestSparseShard) {
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int rows = 100;
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int columns = 80;
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int max_bins = 4;
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auto dmat = CreateDMatrix(rows, columns, 0.9f);
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common::HistCutMatrix hmat;
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common::GHistIndexMatrix gmat;
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hmat.Init(dmat.get(), max_bins);
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gmat.cut = &hmat;
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gmat.Init(dmat.get());
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TrainParam p;
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p.max_depth = 6;
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dmlc::DataIter<SparsePage>* iter = dmat->RowIterator();
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iter->BeforeFirst();
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CHECK(iter->Next());
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const SparsePage& batch = iter->Value();
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DeviceShard shard(0, 0, 0, rows, p);
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shard.InitRowPtrs(batch);
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shard.InitCompressedData(hmat, batch);
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CHECK(!iter->Next());
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ASSERT_LT(shard.row_stride, columns);
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auto host_gidx_buffer = shard.gidx_buffer.AsVector();
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common::CompressedIterator<uint32_t> gidx(host_gidx_buffer.data(),
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hmat.row_ptr.back() + 1);
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for (int i = 0; i < rows; i++) {
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int row_offset = 0;
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for (auto j = gmat.row_ptr[i]; j < gmat.row_ptr[i + 1]; j++) {
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ASSERT_EQ(gidx[i * shard.row_stride + row_offset], gmat.index[j]);
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row_offset++;
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}
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for (; row_offset < shard.row_stride; row_offset++) {
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ASSERT_EQ(gidx[i * shard.row_stride + row_offset], shard.null_gidx_value);
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}
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}
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}
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TEST(gpu_hist_experimental, TestDenseShard) {
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int rows = 100;
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int columns = 80;
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int max_bins = 4;
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auto dmat = CreateDMatrix(rows, columns, 0);
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common::HistCutMatrix hmat;
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common::GHistIndexMatrix gmat;
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hmat.Init(dmat.get(), max_bins);
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gmat.cut = &hmat;
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gmat.Init(dmat.get());
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TrainParam p;
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p.max_depth = 6;
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dmlc::DataIter<SparsePage>* iter = dmat->RowIterator();
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iter->BeforeFirst();
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CHECK(iter->Next());
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const SparsePage& batch = iter->Value();
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DeviceShard shard(0, 0, 0, rows, p);
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shard.InitRowPtrs(batch);
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shard.InitCompressedData(hmat, batch);
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CHECK(!iter->Next());
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ASSERT_EQ(shard.row_stride, columns);
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auto host_gidx_buffer = shard.gidx_buffer.AsVector();
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common::CompressedIterator<uint32_t> gidx(host_gidx_buffer.data(),
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hmat.row_ptr.back() + 1);
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for (int i = 0; i < gmat.index.size(); i++) {
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ASSERT_EQ(gidx[i], gmat.index[i]);
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}
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}
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} // namespace tree
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} // namespace xgboost
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