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
61 lines
1.8 KiB
Plaintext
61 lines
1.8 KiB
Plaintext
#include "../../../src/common/device_helpers.cuh"
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#include "../../../src/common/hist_util.h"
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#include "gtest/gtest.h"
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#include "xgboost/c_api.h"
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#include <algorithm>
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#include <cmath>
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#include <thrust/device_vector.h>
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#include <thrust/iterator/counting_iterator.h>
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namespace xgboost {
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namespace common {
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TEST(gpu_hist_util, TestDeviceSketch) {
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// create the data
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int nrows = 10001;
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std::vector<float> test_data(nrows);
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auto count_iter = thrust::make_counting_iterator(0);
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// fill in reverse order
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std::copy(count_iter, count_iter + nrows, test_data.rbegin());
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// create the DMatrix
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DMatrixHandle dmat_handle;
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XGDMatrixCreateFromMat(test_data.data(), nrows, 1, -1,
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&dmat_handle);
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auto dmat = *static_cast<std::shared_ptr<xgboost::DMatrix> *>(dmat_handle);
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// parameters for finding quantiles
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tree::TrainParam p;
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p.max_bin = 20;
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p.gpu_id = 0;
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p.n_gpus = 1;
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// ensure that the exact quantiles are found
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p.gpu_batch_nrows = nrows * 10;
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// find quantiles on the CPU
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HistCutMatrix hmat_cpu;
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hmat_cpu.Init(dmat.get(), p.max_bin);
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// find the cuts on the GPU
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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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HistCutMatrix hmat_gpu;
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DeviceSketch(batch, dmat->Info(), p, &hmat_gpu);
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CHECK(!iter->Next());
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// compare the cuts
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double eps = 1e-2;
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ASSERT_EQ(hmat_gpu.min_val.size(), 1);
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ASSERT_EQ(hmat_gpu.row_ptr.size(), 2);
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ASSERT_EQ(hmat_gpu.cut.size(), hmat_cpu.cut.size());
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ASSERT_LT(fabs(hmat_cpu.min_val[0] - hmat_gpu.min_val[0]), eps * nrows);
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for (int i = 0; i < hmat_gpu.cut.size(); ++i) {
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ASSERT_LT(fabs(hmat_cpu.cut[i] - hmat_gpu.cut[i]), eps * nrows);
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}
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}
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} // namespace common
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} // namespace xgboost
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