c589eff941
* Only define `gpu_id` and `n_gpus` in `LearnerTrainParam` * Pass LearnerTrainParam through XGBoost vid factory method. * Disable all GPU usage when GPU related parameters are not specified (fixes XGBoost choosing GPU over aggressively). * Test learner train param io. * Fix gpu pickling.
98 lines
2.5 KiB
Plaintext
98 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 "../../../src/common/device_helpers.cuh"
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#include "../helpers.h"
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#include "gtest/gtest.h"
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using xgboost::common::Span;
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struct Shard { int id; };
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TEST(DeviceHelpers, Basic) {
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std::vector<Shard> shards (4);
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for (int i = 0; i < 4; ++i) {
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shards[i].id = i;
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}
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int sum = dh::ReduceShards<int>(&shards, [](Shard& s) { return s.id ; });
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ASSERT_EQ(sum, 6);
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}
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void CreateTestData(xgboost::bst_uint num_rows, int max_row_size,
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thrust::host_vector<int> *row_ptr,
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thrust::host_vector<xgboost::bst_uint> *rows) {
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row_ptr->resize(num_rows + 1);
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int sum = 0;
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for (xgboost::bst_uint i = 0; i <= num_rows; i++) {
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(*row_ptr)[i] = sum;
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sum += rand() % max_row_size; // NOLINT
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if (i < num_rows) {
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for (int j = (*row_ptr)[i]; j < sum; j++) {
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(*rows).push_back(i);
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}
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}
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}
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}
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void TestLbs() {
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srand(17);
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dh::CubMemory temp_memory;
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std::vector<int> test_rows = {4, 100, 1000};
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std::vector<int> test_max_row_sizes = {4, 100, 1300};
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for (auto num_rows : test_rows) {
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for (auto max_row_size : test_max_row_sizes) {
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thrust::host_vector<int> h_row_ptr;
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thrust::host_vector<xgboost::bst_uint> h_rows;
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CreateTestData(num_rows, max_row_size, &h_row_ptr, &h_rows);
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thrust::device_vector<size_t> row_ptr = h_row_ptr;
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thrust::device_vector<int> output_row(h_rows.size());
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auto d_output_row = output_row.data();
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dh::TransformLbs(0, &temp_memory, h_rows.size(), dh::Raw(row_ptr),
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row_ptr.size() - 1, false,
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[=] __device__(size_t idx, size_t ridx) {
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d_output_row[idx] = ridx;
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});
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dh::safe_cuda(cudaDeviceSynchronize());
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ASSERT_TRUE(h_rows == output_row);
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}
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}
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}
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TEST(cub_lbs, Test) {
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TestLbs();
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}
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TEST(sumReduce, Test) {
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thrust::device_vector<float> data(100, 1.0f);
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dh::CubMemory temp;
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auto sum = dh::SumReduction(temp, dh::Raw(data), data.size());
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ASSERT_NEAR(sum, 100.0f, 1e-5);
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}
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void TestAllocator() {
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int n = 10;
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Span<float> a;
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Span<int> b;
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Span<size_t> c;
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dh::BulkAllocator ba;
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ba.Allocate(0, &a, n, &b, n, &c, n);
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// Should be no illegal memory accesses
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dh::LaunchN(0, n, [=] __device__(size_t idx) { c[idx] = a[idx] + b[idx]; });
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dh::safe_cuda(cudaDeviceSynchronize());
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}
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// Define the test in a function so we can use device lambda
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TEST(bulkAllocator, Test) {
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TestAllocator();
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}
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