Refactor out row partitioning logic from gpu_hist, introduce caching device vectors (#4554)
This commit is contained in:
Rory Mitchell
GitHub
@@ -206,16 +206,10 @@ void TestBuildHist(bool use_shared_memory_histograms) {
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dh::safe_cuda(cudaMemcpy(h_gidx_buffer.data(), d_gidx_buffer_ptr,
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sizeof(common::CompressedByteT) * shard.gidx_buffer.size(),
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cudaMemcpyDeviceToHost));
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auto gidx = common::CompressedIterator<uint32_t>(h_gidx_buffer.data(),
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num_symbols);
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shard.ridx_segments.resize(1);
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shard.ridx_segments[0] = Segment(0, kNRows);
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shard.row_partitioner.reset(new RowPartitioner(0, kNRows));
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shard.hist.AllocateHistogram(0);
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dh::CopyVectorToDeviceSpan(shard.gpair, h_gpair);
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thrust::sequence(
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thrust::device_pointer_cast(shard.ridx.Current()),
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thrust::device_pointer_cast(shard.ridx.Current() + shard.ridx.Size()));
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shard.use_shared_memory_histograms = use_shared_memory_histograms;
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shard.BuildHist(0);
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@@ -358,138 +352,6 @@ TEST(GpuHist, EvaluateSplits) {
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ASSERT_NEAR(res[1].fvalue, 0.26, xgboost::kRtEps);
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}
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TEST(GpuHist, ApplySplit) {
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int constexpr kNId = 0;
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int constexpr kNRows = 16;
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int constexpr kNCols = 8;
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TrainParam param;
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std::vector<std::pair<std::string, std::string>> args = {};
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param.InitAllowUnknown(args);
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// Initialize shard
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for (size_t i = 0; i < kNCols; ++i) {
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param.monotone_constraints.emplace_back(0);
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}
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std::unique_ptr<DeviceShard<GradientPairPrecise>> shard{
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new DeviceShard<GradientPairPrecise>(0, 0, 0, kNRows, param, kNCols,
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kNCols)};
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shard->ridx_segments.resize(3); // 3 nodes.
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shard->node_sum_gradients.resize(3);
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shard->ridx_segments[0] = Segment(0, kNRows);
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shard->ba.Allocate(0, &(shard->ridx), kNRows,
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&(shard->position), kNRows);
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shard->ellpack_matrix.row_stride = kNCols;
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thrust::sequence(
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thrust::device_pointer_cast(shard->ridx.Current()),
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thrust::device_pointer_cast(shard->ridx.Current() + shard->ridx.Size()));
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RegTree tree;
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DeviceSplitCandidate candidate;
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candidate.Update(2, kLeftDir,
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0.59, 4, // fvalue has to be equal to one of the cut field
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GradientPair(8.2, 2.8), GradientPair(6.3, 3.6),
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GPUTrainingParam(param));
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ExpandEntry candidate_entry {0, 0, candidate, 0};
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candidate_entry.nid = kNId;
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// Used to get bin_id in update position.
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common::HistCutMatrix cmat = GetHostCutMatrix();
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MetaInfo info;
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info.num_row_ = kNRows;
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info.num_col_ = kNCols;
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info.num_nonzero_ = kNRows * kNCols; // Dense
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// Initialize gidx
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int n_bins = 24;
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int row_stride = kNCols;
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int num_symbols = n_bins + 1;
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size_t compressed_size_bytes =
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common::CompressedBufferWriter::CalculateBufferSize(row_stride * kNRows,
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num_symbols);
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shard->ba.Allocate(0, &(shard->gidx_buffer), compressed_size_bytes,
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&(shard->feature_segments), cmat.row_ptr.size(),
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&(shard->min_fvalue), cmat.min_val.size(),
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&(shard->gidx_fvalue_map), 24);
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dh::CopyVectorToDeviceSpan(shard->feature_segments, cmat.row_ptr);
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dh::CopyVectorToDeviceSpan(shard->gidx_fvalue_map, cmat.cut);
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shard->ellpack_matrix.feature_segments = shard->feature_segments;
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shard->ellpack_matrix.gidx_fvalue_map = shard->gidx_fvalue_map;
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dh::CopyVectorToDeviceSpan(shard->min_fvalue, cmat.min_val);
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shard->ellpack_matrix.min_fvalue = shard->min_fvalue;
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shard->ellpack_matrix.is_dense = true;
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common::CompressedBufferWriter wr(num_symbols);
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// gidx 14 should go right, 12 goes left
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std::vector<int> h_gidx (kNRows * row_stride, 14);
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h_gidx[4] = 12;
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h_gidx[12] = 12;
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std::vector<common::CompressedByteT> h_gidx_compressed (compressed_size_bytes);
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wr.Write(h_gidx_compressed.data(), h_gidx.begin(), h_gidx.end());
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dh::CopyVectorToDeviceSpan(shard->gidx_buffer, h_gidx_compressed);
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shard->ellpack_matrix.gidx_iter = common::CompressedIterator<uint32_t>(
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shard->gidx_buffer.data(), num_symbols);
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shard->ApplySplit(candidate_entry, &tree);
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shard->UpdatePosition(candidate_entry.nid, tree[candidate_entry.nid]);
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ASSERT_FALSE(tree[kNId].IsLeaf());
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int left_nidx = tree[kNId].LeftChild();
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int right_nidx = tree[kNId].RightChild();
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ASSERT_EQ(shard->ridx_segments[left_nidx].begin, 0);
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ASSERT_EQ(shard->ridx_segments[left_nidx].end, 2);
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ASSERT_EQ(shard->ridx_segments[right_nidx].begin, 2);
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ASSERT_EQ(shard->ridx_segments[right_nidx].end, 16);
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}
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void TestSortPosition(const std::vector<int>& position_in, int left_idx,
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int right_idx) {
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std::vector<int64_t> left_count = {
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std::count(position_in.begin(), position_in.end(), left_idx)};
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thrust::device_vector<int64_t> d_left_count = left_count;
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thrust::device_vector<int> position = position_in;
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thrust::device_vector<int> position_out(position.size());
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thrust::device_vector<bst_uint> ridx(position.size());
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thrust::sequence(ridx.begin(), ridx.end());
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thrust::device_vector<bst_uint> ridx_out(ridx.size());
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dh::CubMemory tmp;
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SortPosition(
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&tmp, common::Span<int>(position.data().get(), position.size()),
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common::Span<int>(position_out.data().get(), position_out.size()),
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common::Span<bst_uint>(ridx.data().get(), ridx.size()),
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common::Span<bst_uint>(ridx_out.data().get(), ridx_out.size()), left_idx,
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right_idx, d_left_count.data().get(), nullptr);
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thrust::host_vector<int> position_result = position_out;
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thrust::host_vector<int> ridx_result = ridx_out;
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// Check position is sorted
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EXPECT_TRUE(std::is_sorted(position_result.begin(), position_result.end()));
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// Check row indices are sorted inside left and right segment
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EXPECT_TRUE(
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std::is_sorted(ridx_result.begin(), ridx_result.begin() + left_count[0]));
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EXPECT_TRUE(
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std::is_sorted(ridx_result.begin() + left_count[0], ridx_result.end()));
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// Check key value pairs are the same
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for (auto i = 0ull; i < ridx_result.size(); i++) {
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EXPECT_EQ(position_result[i], position_in[ridx_result[i]]);
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}
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}
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TEST(GpuHist, SortPosition) {
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TestSortPosition({1, 2, 1, 2, 1}, 1, 2);
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TestSortPosition({1, 1, 1, 1}, 1, 2);
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TestSortPosition({2, 2, 2, 2}, 1, 2);
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TestSortPosition({1, 2, 1, 2, 3}, 1, 2);
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}
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void TestHistogramIndexImpl(int n_gpus) {
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// Test if the compressed histogram index matches when using a sparse
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// dmatrix with and without using external memory
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