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[breaking] Remove the predictor param, allow fallback to prediction using DMatrix. (#9129)

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- A `DeviceOrd` struct is implemented to indicate the device. It will eventually replace the `gpu_id` parameter.
- The `predictor` parameter is removed.
- Fallback to `DMatrix` when `inplace_predict` is not available.
- The heuristic for choosing a predictor is only used during training.
This commit is contained in:
Jiaming Yuan
2023-07-03 19:23:54 +08:00
committed by GitHub
parent 3a0f787703
commit 39390cc2ee
54 changed files with 1049 additions and 778 deletions
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227
tests/cpp/predictor/test_predictor.cc
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@@ -8,9 +8,11 @@
#include <xgboost/data.h> // for DMatrix, BatchIterator, BatchSet, MetaInfo
#include <xgboost/host_device_vector.h> // for HostDeviceVector
#include <xgboost/predictor.h> // for PredictionCacheEntry, Predictor, Predic...
#include <xgboost/string_view.h> // for StringView
#include <algorithm> // for max
#include <limits> // for numeric_limits
#include <memory> // for shared_ptr
#include <unordered_map> // for unordered_map
#include "../../../src/common/bitfield.h" // for LBitField32
@@ -51,7 +53,7 @@ void TestTrainingPrediction(size_t rows, size_t bins,
size_t constexpr kIters = 3;
std::unique_ptr<Learner> learner;
auto train = [&](std::string predictor) {
auto train = [&](Context const& ctx) {
p_hist->Info().labels.Reshape(rows, 1);
auto &h_label = p_hist->Info().labels.Data()->HostVector();
@@ -65,7 +67,7 @@ void TestTrainingPrediction(size_t rows, size_t bins,
learner->SetParam("num_feature", std::to_string(kCols));
learner->SetParam("num_class", std::to_string(kClasses));
learner->SetParam("max_bin", std::to_string(bins));
learner->SetParam("predictor", predictor);
ConfigLearnerByCtx(&ctx, learner.get());
learner->Configure();
for (size_t i = 0; i < kIters; ++i) {
@@ -77,7 +79,7 @@ void TestTrainingPrediction(size_t rows, size_t bins,
learner.reset(Learner::Create({}));
learner->LoadModel(model);
learner->SetParam("predictor", predictor);
ConfigLearnerByCtx(&ctx, learner.get());
learner->Configure();
HostDeviceVector<float> from_full;
@@ -93,16 +95,16 @@ void TestTrainingPrediction(size_t rows, size_t bins,
};
if (tree_method == "gpu_hist") {
train("gpu_predictor");
train(MakeCUDACtx(0));
} else {
train("cpu_predictor");
train(Context{});
}
}
void TestInplacePrediction(std::shared_ptr<DMatrix> x, std::string predictor, bst_row_t rows,
bst_feature_t cols, int32_t device) {
size_t constexpr kClasses { 4 };
auto gen = RandomDataGenerator{rows, cols, 0.5}.Device(device);
void TestInplacePrediction(Context const *ctx, std::shared_ptr<DMatrix> x, bst_row_t rows,
bst_feature_t cols) {
std::size_t constexpr kClasses { 4 };
auto gen = RandomDataGenerator{rows, cols, 0.5}.Device(ctx->gpu_id);
std::shared_ptr<DMatrix> m = gen.GenerateDMatrix(true, false, kClasses);
std::unique_ptr<Learner> learner {
@@ -113,12 +115,14 @@ void TestInplacePrediction(std::shared_ptr<DMatrix> x, std::string predictor, bs
learner->SetParam("num_class", std::to_string(kClasses));
learner->SetParam("seed", "0");
learner->SetParam("subsample", "0.5");
learner->SetParam("gpu_id", std::to_string(device));
learner->SetParam("predictor", predictor);
learner->SetParam("tree_method", "hist");
for (int32_t it = 0; it < 4; ++it) {
learner->UpdateOneIter(it, m);
}
learner->SetParam("gpu_id", std::to_string(ctx->gpu_id));
learner->Configure();
HostDeviceVector<float> *p_out_predictions_0{nullptr};
learner->InplacePredict(x, PredictionType::kMargin, std::numeric_limits<float>::quiet_NaN(),
&p_out_predictions_0, 0, 2);
@@ -154,40 +158,79 @@ void TestInplacePrediction(std::shared_ptr<DMatrix> x, std::string predictor, bs
}
namespace {
std::unique_ptr<Learner> LearnerForTest(std::shared_ptr<DMatrix> dmat, size_t iters,
size_t forest = 1) {
std::unique_ptr<Learner> LearnerForTest(Context const *ctx, std::shared_ptr<DMatrix> dmat,
size_t iters, size_t forest = 1) {
std::unique_ptr<Learner> learner{Learner::Create({dmat})};
learner->SetParams(Args{{"num_parallel_tree", std::to_string(forest)}});
for (size_t i = 0; i < iters; ++i) {
learner->UpdateOneIter(i, dmat);
}
ConfigLearnerByCtx(ctx, learner.get());
return learner;
}
void VerifyPredictionWithLesserFeatures(Learner *learner, std::string const &predictor_name,
size_t rows, std::shared_ptr<DMatrix> const &m_test,
std::shared_ptr<DMatrix> const &m_invalid) {
void VerifyPredictionWithLesserFeatures(Learner *learner, bst_row_t kRows,
std::shared_ptr<DMatrix> m_test,
std::shared_ptr<DMatrix> m_invalid) {
HostDeviceVector<float> prediction;
learner->SetParam("predictor", predictor_name);
learner->Configure();
Json config{Object()};
learner->SaveConfig(&config);
ASSERT_EQ(get<String>(config["learner"]["gradient_booster"]["gbtree_train_param"]["predictor"]),
predictor_name);
learner->Predict(m_test, false, &prediction, 0, 0);
ASSERT_EQ(prediction.Size(), rows);
ASSERT_EQ(prediction.Size(), kRows);
ASSERT_THROW({ learner->Predict(m_invalid, false, &prediction, 0, 0); }, dmlc::Error);
}
void VerifyPredictionWithLesserFeaturesColumnSplit(Learner *learner, size_t rows,
std::shared_ptr<DMatrix> m_test,
std::shared_ptr<DMatrix> m_invalid) {
auto const world_size = collective::GetWorldSize();
auto const rank = collective::GetRank();
std::shared_ptr<DMatrix> sliced_test{m_test->SliceCol(world_size, rank)};
std::shared_ptr<DMatrix> sliced_invalid{m_invalid->SliceCol(world_size, rank)};
VerifyPredictionWithLesserFeatures(learner, rows, sliced_test, sliced_invalid);
}
} // anonymous namespace
void TestPredictionWithLesserFeatures(Context const *ctx) {
size_t constexpr kRows = 256, kTrainCols = 256, kTestCols = 4, kIters = 4;
auto m_train = RandomDataGenerator(kRows, kTrainCols, 0.5).GenerateDMatrix(true);
auto learner = LearnerForTest(ctx, m_train, kIters);
auto m_test = RandomDataGenerator(kRows, kTestCols, 0.5).GenerateDMatrix(false);
auto m_invalid = RandomDataGenerator(kRows, kTrainCols + 1, 0.5).GenerateDMatrix(false);
VerifyPredictionWithLesserFeatures(learner.get(), kRows, m_test, m_invalid);
}
void TestPredictionDeviceAccess() {
Context ctx;
size_t constexpr kRows = 256, kTrainCols = 256, kTestCols = 4, kIters = 4;
auto m_train = RandomDataGenerator(kRows, kTrainCols, 0.5).GenerateDMatrix(true);
auto m_test = RandomDataGenerator(kRows, kTestCols, 0.5).GenerateDMatrix(false);
auto learner = LearnerForTest(&ctx, m_train, kIters);
HostDeviceVector<float> from_cpu;
{
ASSERT_EQ(from_cpu.DeviceIdx(), Context::kCpuId);
Context cpu_ctx;
ConfigLearnerByCtx(&cpu_ctx, learner.get());
learner->Predict(m_test, false, &from_cpu, 0, 0);
ASSERT_TRUE(from_cpu.HostCanWrite());
ASSERT_FALSE(from_cpu.DeviceCanRead());
}
#if defined(XGBOOST_USE_CUDA)
HostDeviceVector<float> from_cpu;
learner->SetParam("predictor", "cpu_predictor");
learner->Predict(m_test, false, &from_cpu, 0, 0);
HostDeviceVector<float> from_cuda;
learner->SetParam("predictor", "gpu_predictor");
learner->Predict(m_test, false, &from_cuda, 0, 0);
{
Context cuda_ctx = MakeCUDACtx(0);
ConfigLearnerByCtx(&cuda_ctx, learner.get());
learner->Predict(m_test, false, &from_cuda, 0, 0);
ASSERT_EQ(from_cuda.DeviceIdx(), 0);
ASSERT_TRUE(from_cuda.DeviceCanWrite());
ASSERT_FALSE(from_cuda.HostCanRead());
}
auto const &h_cpu = from_cpu.ConstHostVector();
auto const &h_gpu = from_cuda.ConstHostVector();
@@ -196,41 +239,17 @@ void VerifyPredictionWithLesserFeatures(Learner *learner, std::string const &pre
}
#endif // defined(XGBOOST_USE_CUDA)
}
} // anonymous namespace
void TestPredictionWithLesserFeatures(std::string predictor_name) {
void TestPredictionWithLesserFeaturesColumnSplit(Context const *ctx) {
size_t constexpr kRows = 256, kTrainCols = 256, kTestCols = 4, kIters = 4;
auto m_train = RandomDataGenerator(kRows, kTrainCols, 0.5).GenerateDMatrix(true);
auto learner = LearnerForTest(m_train, kIters);
auto m_test = RandomDataGenerator(kRows, kTestCols, 0.5).GenerateDMatrix(false);
auto m_invalid = RandomDataGenerator(kRows, kTrainCols + 1, 0.5).GenerateDMatrix(false);
VerifyPredictionWithLesserFeatures(learner.get(), predictor_name, kRows, m_test, m_invalid);
}
namespace {
void VerifyPredictionWithLesserFeaturesColumnSplit(Learner *learner,
std::string const &predictor_name, size_t rows,
std::shared_ptr<DMatrix> m_test,
std::shared_ptr<DMatrix> m_invalid) {
auto const world_size = collective::GetWorldSize();
auto const rank = collective::GetRank();
std::shared_ptr<DMatrix> sliced_test{m_test->SliceCol(world_size, rank)};
std::shared_ptr<DMatrix> sliced_invalid{m_invalid->SliceCol(world_size, rank)};
VerifyPredictionWithLesserFeatures(learner, predictor_name, rows, sliced_test, sliced_invalid);
}
} // anonymous namespace
void TestPredictionWithLesserFeaturesColumnSplit(std::string predictor_name) {
size_t constexpr kRows = 256, kTrainCols = 256, kTestCols = 4, kIters = 4;
auto m_train = RandomDataGenerator(kRows, kTrainCols, 0.5).GenerateDMatrix(true);
auto learner = LearnerForTest(m_train, kIters);
auto learner = LearnerForTest(ctx, m_train, kIters);
auto m_test = RandomDataGenerator(kRows, kTestCols, 0.5).GenerateDMatrix(false);
auto m_invalid = RandomDataGenerator(kRows, kTrainCols + 1, 0.5).GenerateDMatrix(false);
auto constexpr kWorldSize = 2;
RunWithInMemoryCommunicator(kWorldSize, VerifyPredictionWithLesserFeaturesColumnSplit,
learner.get(), predictor_name, kRows, m_test, m_invalid);
learner.get(), kRows, m_test, m_invalid);
}
void GBTreeModelForTest(gbm::GBTreeModel *model, uint32_t split_ind,
@@ -252,7 +271,7 @@ void GBTreeModelForTest(gbm::GBTreeModel *model, uint32_t split_ind,
model->CommitModelGroup(std::move(trees), 0);
}
void TestCategoricalPrediction(std::string name, bool is_column_split) {
void TestCategoricalPrediction(Context const* ctx, bool is_column_split) {
size_t constexpr kCols = 10;
PredictionCacheEntry out_predictions;
@@ -262,13 +281,10 @@ void TestCategoricalPrediction(std::string name, bool is_column_split) {
float left_weight = 1.3f;
float right_weight = 1.7f;
Context ctx;
ctx.UpdateAllowUnknown(Args{});
gbm::GBTreeModel model(&mparam, &ctx);
gbm::GBTreeModel model(&mparam, ctx);
GBTreeModelForTest(&model, split_ind, split_cat, left_weight, right_weight);
ctx.UpdateAllowUnknown(Args{{"gpu_id", "0"}});
std::unique_ptr<Predictor> predictor{Predictor::Create(name.c_str(), &ctx)};
std::unique_ptr<Predictor> predictor{CreatePredictorForTest(ctx)};
std::vector<float> row(kCols);
row[split_ind] = split_cat;
@@ -298,12 +314,12 @@ void TestCategoricalPrediction(std::string name, bool is_column_split) {
ASSERT_EQ(out_predictions.predictions.HostVector()[0], left_weight + score);
}
void TestCategoricalPredictionColumnSplit(std::string name) {
void TestCategoricalPredictionColumnSplit(Context const *ctx) {
auto constexpr kWorldSize = 2;
RunWithInMemoryCommunicator(kWorldSize, TestCategoricalPrediction, name, true);
RunWithInMemoryCommunicator(kWorldSize, TestCategoricalPrediction, ctx, true);
}
void TestCategoricalPredictLeaf(StringView name, bool is_column_split) {
void TestCategoricalPredictLeaf(Context const *ctx, bool is_column_split) {
size_t constexpr kCols = 10;
PredictionCacheEntry out_predictions;
@@ -314,14 +330,10 @@ void TestCategoricalPredictLeaf(StringView name, bool is_column_split) {
float left_weight = 1.3f;
float right_weight = 1.7f;
Context ctx;
ctx.UpdateAllowUnknown(Args{});
gbm::GBTreeModel model(&mparam, &ctx);
gbm::GBTreeModel model(&mparam, ctx);
GBTreeModelForTest(&model, split_ind, split_cat, left_weight, right_weight);
ctx.gpu_id = 0;
std::unique_ptr<Predictor> predictor{Predictor::Create(name.c_str(), &ctx)};
std::unique_ptr<Predictor> predictor{CreatePredictorForTest(ctx)};
std::vector<float> row(kCols);
row[split_ind] = split_cat;
@@ -346,19 +358,21 @@ void TestCategoricalPredictLeaf(StringView name, bool is_column_split) {
ASSERT_EQ(out_predictions.predictions.HostVector()[0], 1);
}
void TestCategoricalPredictLeafColumnSplit(StringView name) {
void TestCategoricalPredictLeafColumnSplit(Context const *ctx) {
auto constexpr kWorldSize = 2;
RunWithInMemoryCommunicator(kWorldSize, TestCategoricalPredictLeaf, name, true);
RunWithInMemoryCommunicator(kWorldSize, TestCategoricalPredictLeaf, ctx, true);
}
void TestIterationRange(std::string name) {
void TestIterationRange(Context const* ctx) {
size_t constexpr kRows = 1000, kCols = 20, kClasses = 4, kForest = 3, kIters = 10;
auto dmat = RandomDataGenerator(kRows, kCols, 0).GenerateDMatrix(true, true, kClasses);
auto learner = LearnerForTest(dmat, kIters, kForest);
learner->SetParams(Args{{"predictor", name}});
auto dmat = RandomDataGenerator(kRows, kCols, 0)
.Device(ctx->gpu_id)
.GenerateDMatrix(true, true, kClasses);
auto learner = LearnerForTest(ctx, dmat, kIters, kForest);
bool bound = false;
std::unique_ptr<Learner> sliced {learner->Slice(0, 3, 1, &bound)};
bst_layer_t lend{3};
std::unique_ptr<Learner> sliced{learner->Slice(0, lend, 1, &bound)};
ASSERT_FALSE(bound);
HostDeviceVector<float> out_predt_sliced;
@@ -366,11 +380,8 @@ void TestIterationRange(std::string name) {
// margin
{
sliced->Predict(dmat, true, &out_predt_sliced, 0, 0, false, false, false,
false, false);
learner->Predict(dmat, true, &out_predt_ranged, 0, 3, false, false, false,
false, false);
sliced->Predict(dmat, true, &out_predt_sliced, 0, 0, false, false, false, false, false);
learner->Predict(dmat, true, &out_predt_ranged, 0, lend, false, false, false, false, false);
auto const &h_sliced = out_predt_sliced.HostVector();
auto const &h_range = out_predt_ranged.HostVector();
@@ -380,11 +391,8 @@ void TestIterationRange(std::string name) {
// SHAP
{
sliced->Predict(dmat, false, &out_predt_sliced, 0, 0, false, false,
true, false, false);
learner->Predict(dmat, false, &out_predt_ranged, 0, 3, false, false, true,
false, false);
sliced->Predict(dmat, false, &out_predt_sliced, 0, 0, false, false, true, false, false);
learner->Predict(dmat, false, &out_predt_ranged, 0, lend, false, false, true, false, false);
auto const &h_sliced = out_predt_sliced.HostVector();
auto const &h_range = out_predt_ranged.HostVector();
@@ -394,10 +402,8 @@ void TestIterationRange(std::string name) {
// SHAP interaction
{
sliced->Predict(dmat, false, &out_predt_sliced, 0, 0, false, false,
false, false, true);
learner->Predict(dmat, false, &out_predt_ranged, 0, 3, false, false, false,
false, true);
sliced->Predict(dmat, false, &out_predt_sliced, 0, 0, false, false, false, false, true);
learner->Predict(dmat, false, &out_predt_ranged, 0, lend, false, false, false, false, true);
auto const &h_sliced = out_predt_sliced.HostVector();
auto const &h_range = out_predt_ranged.HostVector();
ASSERT_EQ(h_sliced.size(), h_range.size());
@@ -406,10 +412,8 @@ void TestIterationRange(std::string name) {
// Leaf
{
sliced->Predict(dmat, false, &out_predt_sliced, 0, 0, false, true,
false, false, false);
learner->Predict(dmat, false, &out_predt_ranged, 0, 3, false, true, false,
false, false);
sliced->Predict(dmat, false, &out_predt_sliced, 0, 0, false, true, false, false, false);
learner->Predict(dmat, false, &out_predt_ranged, 0, lend, false, true, false, false, false);
auto const &h_sliced = out_predt_sliced.HostVector();
auto const &h_range = out_predt_ranged.HostVector();
ASSERT_EQ(h_sliced.size(), h_range.size());
@@ -456,11 +460,16 @@ void VerifyIterationRangeColumnSplit(DMatrix *dmat, Learner *learner, Learner *s
}
} // anonymous namespace
void TestIterationRangeColumnSplit(std::string name) {
void TestIterationRangeColumnSplit(Context const* ctx) {
size_t constexpr kRows = 1000, kCols = 20, kClasses = 4, kForest = 3, kIters = 10;
auto dmat = RandomDataGenerator(kRows, kCols, 0).GenerateDMatrix(true, true, kClasses);
auto learner = LearnerForTest(dmat, kIters, kForest);
learner->SetParams(Args{{"predictor", name}});
auto learner = LearnerForTest(ctx, dmat, kIters, kForest);
if (ctx->IsCPU()) {
learner->SetParams(Args{{"gpu_id", std::to_string(-1)}});
} else {
learner->SetParams(Args{{"gpu_id", std::to_string(0)}});
}
bool bound = false;
std::unique_ptr<Learner> sliced{learner->Slice(0, 3, 1, &bound)};
@@ -488,10 +497,10 @@ void TestIterationRangeColumnSplit(std::string name) {
leaf_ranged, leaf_sliced);
}
void TestSparsePrediction(float sparsity, std::string predictor) {
void TestSparsePrediction(Context const *ctx, float sparsity) {
size_t constexpr kRows = 512, kCols = 128, kIters = 4;
auto Xy = RandomDataGenerator(kRows, kCols, sparsity).GenerateDMatrix(true);
auto learner = LearnerForTest(Xy, kIters);
auto learner = LearnerForTest(ctx, Xy, kIters);
HostDeviceVector<float> sparse_predt;
@@ -501,11 +510,14 @@ void TestSparsePrediction(float sparsity, std::string predictor) {
learner.reset(Learner::Create({Xy}));
learner->LoadModel(model);
learner->SetParam("predictor", predictor);
if (ctx->IsCUDA()) {
learner->SetParam("tree_method", "gpu_hist");
learner->SetParam("gpu_id", std::to_string(ctx->gpu_id));
}
learner->Predict(Xy, false, &sparse_predt, 0, 0);
HostDeviceVector<float> with_nan(kRows * kCols, std::numeric_limits<float>::quiet_NaN());
auto& h_with_nan = with_nan.HostVector();
auto &h_with_nan = with_nan.HostVector();
for (auto const &page : Xy->GetBatches<SparsePage>()) {
auto batch = page.GetView();
for (size_t i = 0; i < batch.Size(); ++i) {
@@ -516,7 +528,8 @@ void TestSparsePrediction(float sparsity, std::string predictor) {
}
}
learner->SetParam("predictor", "cpu_predictor");
learner->SetParam("tree_method", "hist");
learner->SetParam("gpu_id", "-1");
// Xcode_12.4 doesn't compile with `std::make_shared`.
auto dense = std::shared_ptr<DMatrix>(new data::DMatrixProxy{});
auto array_interface = GetArrayInterface(&with_nan, kRows, kCols);
@@ -527,8 +540,8 @@ void TestSparsePrediction(float sparsity, std::string predictor) {
learner->InplacePredict(dense, PredictionType::kValue, std::numeric_limits<float>::quiet_NaN(),
&p_dense_predt, 0, 0);
auto const& dense_predt = *p_dense_predt;
if (predictor == "cpu_predictor") {
auto const &dense_predt = *p_dense_predt;
if (ctx->IsCPU()) {
ASSERT_EQ(dense_predt.HostVector(), sparse_predt.HostVector());
} else {
auto const &h_dense = dense_predt.HostVector();
@@ -556,10 +569,10 @@ void VerifySparsePredictionColumnSplit(DMatrix *dmat, Learner *learner,
}
} // anonymous namespace
void TestSparsePredictionColumnSplit(float sparsity, std::string predictor) {
void TestSparsePredictionColumnSplit(Context const* ctx, float sparsity) {
size_t constexpr kRows = 512, kCols = 128, kIters = 4;
auto Xy = RandomDataGenerator(kRows, kCols, sparsity).GenerateDMatrix(true);
auto learner = LearnerForTest(Xy, kIters);
auto learner = LearnerForTest(ctx, Xy, kIters);
HostDeviceVector<float> sparse_predt;
@@ -569,7 +582,7 @@ void TestSparsePredictionColumnSplit(float sparsity, std::string predictor) {
learner.reset(Learner::Create({Xy}));
learner->LoadModel(model);
learner->SetParam("predictor", predictor);
ConfigLearnerByCtx(ctx, learner.get());
learner->Predict(Xy, false, &sparse_predt, 0, 0);
auto constexpr kWorldSize = 2;