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[EM] Enable prediction cache for GPU. (#10707)

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- Use `UpdatePosition` for all nodes and skip `FinalizePosition` when external memory is used.
- Create `encode/decode` for node position, this is just as a refactor.
- Reuse code between update position and finalization.
This commit is contained in:
Jiaming Yuan
2024-08-15 21:41:59 +08:00
committed by GitHub
parent 0def8e0bae
commit 582ea104b5
20 changed files with 378 additions and 327 deletions
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6
tests/cpp/tree/gpu_hist/test_row_partitioner.cu
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@@ -67,9 +67,9 @@ void TestSortPositionBatch(const std::vector<int>& ridx_in, const std::vector<Se
h_batch_info.size() * sizeof(PerNodeData<int>), cudaMemcpyDefault,
nullptr));
dh::device_vector<int8_t> tmp;
SortPositionBatch<uint32_t, decltype(op), int>(dh::ToSpan(d_batch_info), dh::ToSpan(ridx),
dh::ToSpan(ridx_tmp), dh::ToSpan(counts),
total_rows, op, &tmp);
SortPositionBatch<decltype(op), int>(dh::ToSpan(d_batch_info), dh::ToSpan(ridx),
dh::ToSpan(ridx_tmp), dh::ToSpan(counts), total_rows, op,
&tmp);
auto op_without_data = [=] __device__(auto ridx) { return ridx % 2 == 0; };
for (size_t i = 0; i < segments.size(); i++) {

3
tests/cpp/tree/hist/test_expand_entry.cc
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@@ -1,10 +1,11 @@
/**
* Copyright 2023, XGBoost Contributors
* Copyright 2023-2024, XGBoost Contributors
*/
#include <gtest/gtest.h>
#include <xgboost/json.h> // for Json
#include <xgboost/tree_model.h> // for RegTree
#include "../../../../src/common/categorical.h" // for CatBitField
#include "../../../../src/tree/hist/expand_entry.h"
namespace xgboost::tree {

73
tests/cpp/tree/test_gpu_hist.cu
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@@ -5,7 +5,7 @@
#include <xgboost/base.h> // for Args
#include <xgboost/context.h> // for Context
#include <xgboost/host_device_vector.h> // for HostDeviceVector
#include <xgboost/json.h> // for Jons
#include <xgboost/json.h> // for Json
#include <xgboost/task.h> // for ObjInfo
#include <xgboost/tree_model.h> // for RegTree
#include <xgboost/tree_updater.h> // for TreeUpdater
@@ -14,32 +14,17 @@
#include <string> // for string
#include <vector> // for vector
#include "../../../src/common/random.h" // for GlobalRandom
#include "../../../src/data/ellpack_page.h" // for EllpackPage
#include "../../../src/tree/param.h" // for TrainParam
#include "../collective/test_worker.h" // for BaseMGPUTest
#include "../filesystem.h" // dmlc::TemporaryDirectory
#include "../../../src/common/random.h" // for GlobalRandom
#include "../../../src/tree/param.h" // for TrainParam
#include "../collective/test_worker.h" // for BaseMGPUTest
#include "../filesystem.h" // dmlc::TemporaryDirectory
#include "../helpers.h"
namespace xgboost::tree {
void UpdateTree(Context const* ctx, linalg::Matrix<GradientPair>* gpair, DMatrix* dmat,
size_t gpu_page_size, RegTree* tree, HostDeviceVector<bst_float>* preds,
float subsample = 1.0f, const std::string& sampling_method = "uniform",
int max_bin = 2) {
if (gpu_page_size > 0) {
// Loop over the batches and count the records
int64_t batch_count = 0;
int64_t row_count = 0;
for (const auto& batch : dmat->GetBatches<EllpackPage>(
ctx, BatchParam{max_bin, TrainParam::DftSparseThreshold()})) {
EXPECT_LT(batch.Size(), dmat->Info().num_row_);
batch_count++;
row_count += batch.Size();
}
EXPECT_GE(batch_count, 2);
EXPECT_EQ(row_count, dmat->Info().num_row_);
}
namespace {
void UpdateTree(Context const* ctx, linalg::Matrix<GradientPair>* gpair, DMatrix* dmat, bool is_ext,
RegTree* tree, HostDeviceVector<bst_float>* preds, float subsample,
const std::string& sampling_method, bst_bin_t max_bin) {
Args args{
{"max_depth", "2"},
{"max_bin", std::to_string(max_bin)},
@@ -60,8 +45,13 @@ void UpdateTree(Context const* ctx, linalg::Matrix<GradientPair>* gpair, DMatrix
hist_maker->Update(&param, gpair, dmat, common::Span<HostDeviceVector<bst_node_t>>{position},
{tree});
auto cache = linalg::MakeTensorView(ctx, preds->DeviceSpan(), preds->Size(), 1);
hist_maker->UpdatePredictionCache(dmat, cache);
if (subsample < 1.0 && is_ext) {
ASSERT_FALSE(hist_maker->UpdatePredictionCache(dmat, cache));
} else {
ASSERT_TRUE(hist_maker->UpdatePredictionCache(dmat, cache));
}
}
} // anonymous namespace
TEST(GpuHist, UniformSampling) {
constexpr size_t kRows = 4096;
@@ -79,11 +69,11 @@ TEST(GpuHist, UniformSampling) {
RegTree tree;
HostDeviceVector<bst_float> preds(kRows, 0.0, DeviceOrd::CUDA(0));
Context ctx(MakeCUDACtx(0));
UpdateTree(&ctx, &gpair, dmat.get(), 0, &tree, &preds, 1.0, "uniform", kRows);
UpdateTree(&ctx, &gpair, dmat.get(), false, &tree, &preds, 1.0, "uniform", kRows);
// Build another tree using sampling.
RegTree tree_sampling;
HostDeviceVector<bst_float> preds_sampling(kRows, 0.0, DeviceOrd::CUDA(0));
UpdateTree(&ctx, &gpair, dmat.get(), 0, &tree_sampling, &preds_sampling, kSubsample, "uniform",
UpdateTree(&ctx, &gpair, dmat.get(), false, &tree_sampling, &preds_sampling, kSubsample, "uniform",
kRows);
// Make sure the predictions are the same.
@@ -110,12 +100,12 @@ TEST(GpuHist, GradientBasedSampling) {
RegTree tree;
HostDeviceVector<bst_float> preds(kRows, 0.0, DeviceOrd::CUDA(0));
Context ctx(MakeCUDACtx(0));
UpdateTree(&ctx, &gpair, dmat.get(), 0, &tree, &preds, 1.0, "uniform", kRows);
UpdateTree(&ctx, &gpair, dmat.get(), false, &tree, &preds, 1.0, "uniform", kRows);
// Build another tree using sampling.
RegTree tree_sampling;
HostDeviceVector<bst_float> preds_sampling(kRows, 0.0, DeviceOrd::CUDA(0));
UpdateTree(&ctx, &gpair, dmat.get(), 0, &tree_sampling, &preds_sampling, kSubsample,
UpdateTree(&ctx, &gpair, dmat.get(), false, &tree_sampling, &preds_sampling, kSubsample,
"gradient_based", kRows);
// Make sure the predictions are the same.
@@ -147,11 +137,11 @@ TEST(GpuHist, ExternalMemory) {
// Build a tree using the in-memory DMatrix.
RegTree tree;
HostDeviceVector<bst_float> preds(kRows, 0.0, DeviceOrd::CUDA(0));
UpdateTree(&ctx, &gpair, dmat.get(), 0, &tree, &preds, 1.0, "uniform", kRows);
UpdateTree(&ctx, &gpair, dmat.get(), false, &tree, &preds, 1.0, "uniform", kRows);
// Build another tree using multiple ELLPACK pages.
RegTree tree_ext;
HostDeviceVector<bst_float> preds_ext(kRows, 0.0, DeviceOrd::CUDA(0));
UpdateTree(&ctx, &gpair, dmat_ext.get(), kPageSize, &tree_ext, &preds_ext, 1.0, "uniform", kRows);
UpdateTree(&ctx, &gpair, dmat_ext.get(), true, &tree_ext, &preds_ext, 1.0, "uniform", kRows);
// Make sure the predictions are the same.
auto preds_h = preds.ConstHostVector();
@@ -162,23 +152,26 @@ TEST(GpuHist, ExternalMemory) {
}
TEST(GpuHist, ExternalMemoryWithSampling) {
constexpr size_t kRows = 4096;
constexpr size_t kCols = 2;
constexpr size_t kPageSize = 1024;
constexpr size_t kRows = 4096, kCols = 2;
constexpr float kSubsample = 0.5;
const std::string kSamplingMethod = "gradient_based";
common::GlobalRandom().seed(0);
dmlc::TemporaryDirectory tmpdir;
Context ctx(MakeCUDACtx(0));
// Create a single batch DMatrix.
std::unique_ptr<DMatrix> dmat(CreateSparsePageDMatrix(kRows, kCols, 1, tmpdir.path + "/cache"));
auto p_fmat = RandomDataGenerator{kRows, kCols, 0.0f}
.Device(ctx.Device())
.Batches(1)
.GenerateSparsePageDMatrix("temp", true);
// Create a DMatrix with multiple batches.
std::unique_ptr<DMatrix> dmat_ext(
CreateSparsePageDMatrix(kRows, kCols, kRows / kPageSize, tmpdir.path + "/cache"));
auto p_fmat_ext = RandomDataGenerator{kRows, kCols, 0.0f}
.Device(ctx.Device())
.Batches(4)
.GenerateSparsePageDMatrix("temp", true);
Context ctx(MakeCUDACtx(0));
linalg::Matrix<GradientPair> gpair({kRows}, ctx.Device());
gpair.Data()->Copy(GenerateRandomGradients(kRows));
@@ -187,13 +180,13 @@ TEST(GpuHist, ExternalMemoryWithSampling) {
RegTree tree;
HostDeviceVector<bst_float> preds(kRows, 0.0, DeviceOrd::CUDA(0));
UpdateTree(&ctx, &gpair, dmat.get(), 0, &tree, &preds, kSubsample, kSamplingMethod, kRows);
UpdateTree(&ctx, &gpair, p_fmat.get(), true, &tree, &preds, kSubsample, kSamplingMethod, kRows);
// Build another tree using multiple ELLPACK pages.
common::GlobalRandom() = rng;
RegTree tree_ext;
HostDeviceVector<bst_float> preds_ext(kRows, 0.0, DeviceOrd::CUDA(0));
UpdateTree(&ctx, &gpair, dmat_ext.get(), kPageSize, &tree_ext, &preds_ext, kSubsample,
UpdateTree(&ctx, &gpair, p_fmat_ext.get(), true, &tree_ext, &preds_ext, kSubsample,
kSamplingMethod, kRows);
// Make sure the predictions are the same.