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[coll] Pass context to various functions. (#9772)

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* [coll] Pass context to various functions.

In the future, the `Context` object would be required for collective operations, this PR
passes the context object to some required functions to prepare for swapping out the
implementation.
This commit is contained in:
Jiaming Yuan
2023-11-08 09:54:05 +08:00
committed by GitHub
parent 6c0a190f6d
commit 06bdc15e9b
45 changed files with 275 additions and 255 deletions
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49
tests/cpp/common/test_hist_util.cu
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@@ -360,25 +360,27 @@ TEST(HistUtil, DeviceSketchExternalMemoryWithWeights) {
}
template <typename Adapter>
auto MakeUnweightedCutsForTest(Adapter adapter, int32_t num_bins, float missing, size_t batch_size = 0) {
auto MakeUnweightedCutsForTest(Context const* ctx, Adapter adapter, int32_t num_bins, float missing,
size_t batch_size = 0) {
common::HistogramCuts batched_cuts;
HostDeviceVector<FeatureType> ft;
SketchContainer sketch_container(ft, num_bins, adapter.NumColumns(), adapter.NumRows(),
DeviceOrd::CUDA(0));
MetaInfo info;
AdapterDeviceSketch(adapter.Value(), num_bins, info, missing, &sketch_container, batch_size);
sketch_container.MakeCuts(&batched_cuts, info.IsColumnSplit());
sketch_container.MakeCuts(ctx, &batched_cuts, info.IsColumnSplit());
return batched_cuts;
}
template <typename Adapter>
void ValidateBatchedCuts(Adapter adapter, int num_bins, DMatrix* dmat, size_t batch_size = 0) {
void ValidateBatchedCuts(Context const* ctx, Adapter adapter, int num_bins, DMatrix* dmat, size_t batch_size = 0) {
common::HistogramCuts batched_cuts = MakeUnweightedCutsForTest(
adapter, num_bins, std::numeric_limits<float>::quiet_NaN(), batch_size);
ctx, adapter, num_bins, std::numeric_limits<float>::quiet_NaN(), batch_size);
ValidateCuts(batched_cuts, dmat, num_bins);
}
TEST(HistUtil, AdapterDeviceSketch) {
auto ctx = MakeCUDACtx(0);
int rows = 5;
int cols = 1;
int num_bins = 4;
@@ -391,8 +393,8 @@ TEST(HistUtil, AdapterDeviceSketch) {
data::CupyAdapter adapter(str);
auto device_cuts = MakeUnweightedCutsForTest(adapter, num_bins, missing);
Context ctx;
auto device_cuts = MakeUnweightedCutsForTest(&ctx, adapter, num_bins, missing);
ctx = ctx.MakeCPU();
auto host_cuts = GetHostCuts(&ctx, &adapter, num_bins, missing);
EXPECT_EQ(device_cuts.Values(), host_cuts.Values());
@@ -401,6 +403,7 @@ TEST(HistUtil, AdapterDeviceSketch) {
}
TEST(HistUtil, AdapterDeviceSketchMemory) {
auto ctx = MakeCUDACtx(0);
int num_columns = 100;
int num_rows = 1000;
int num_bins = 256;
@@ -410,7 +413,8 @@ TEST(HistUtil, AdapterDeviceSketchMemory) {
dh::GlobalMemoryLogger().Clear();
ConsoleLogger::Configure({{"verbosity", "3"}});
auto cuts = MakeUnweightedCutsForTest(adapter, num_bins, std::numeric_limits<float>::quiet_NaN());
auto cuts =
MakeUnweightedCutsForTest(&ctx, adapter, num_bins, std::numeric_limits<float>::quiet_NaN());
ConsoleLogger::Configure({{"verbosity", "0"}});
size_t bytes_required = detail::RequiredMemory(
num_rows, num_columns, num_rows * num_columns, num_bins, false);
@@ -419,6 +423,7 @@ TEST(HistUtil, AdapterDeviceSketchMemory) {
}
TEST(HistUtil, AdapterSketchSlidingWindowMemory) {
auto ctx = MakeCUDACtx(0);
int num_columns = 100;
int num_rows = 1000;
int num_bins = 256;
@@ -435,7 +440,7 @@ TEST(HistUtil, AdapterSketchSlidingWindowMemory) {
AdapterDeviceSketch(adapter.Value(), num_bins, info, std::numeric_limits<float>::quiet_NaN(),
&sketch_container);
HistogramCuts cuts;
sketch_container.MakeCuts(&cuts, info.IsColumnSplit());
sketch_container.MakeCuts(&ctx, &cuts, info.IsColumnSplit());
size_t bytes_required = detail::RequiredMemory(
num_rows, num_columns, num_rows * num_columns, num_bins, false);
EXPECT_LE(dh::GlobalMemoryLogger().PeakMemory(), bytes_required * 1.05);
@@ -444,6 +449,7 @@ TEST(HistUtil, AdapterSketchSlidingWindowMemory) {
}
TEST(HistUtil, AdapterSketchSlidingWindowWeightedMemory) {
auto ctx = MakeCUDACtx(0);
int num_columns = 100;
int num_rows = 1000;
int num_bins = 256;
@@ -465,7 +471,7 @@ TEST(HistUtil, AdapterSketchSlidingWindowWeightedMemory) {
&sketch_container);
HistogramCuts cuts;
sketch_container.MakeCuts(&cuts, info.IsColumnSplit());
sketch_container.MakeCuts(&ctx, &cuts, info.IsColumnSplit());
ConsoleLogger::Configure({{"verbosity", "0"}});
size_t bytes_required = detail::RequiredMemory(
num_rows, num_columns, num_rows * num_columns, num_bins, true);
@@ -475,6 +481,7 @@ TEST(HistUtil, AdapterSketchSlidingWindowWeightedMemory) {
void TestCategoricalSketchAdapter(size_t n, size_t num_categories,
int32_t num_bins, bool weighted) {
auto ctx = MakeCUDACtx(0);
auto h_x = GenerateRandomCategoricalSingleColumn(n, num_categories);
thrust::device_vector<float> x(h_x);
auto adapter = AdapterFromData(x, n, 1);
@@ -498,7 +505,7 @@ void TestCategoricalSketchAdapter(size_t n, size_t num_categories,
AdapterDeviceSketch(adapter.Value(), num_bins, info,
std::numeric_limits<float>::quiet_NaN(), &container);
HistogramCuts cuts;
container.MakeCuts(&cuts, info.IsColumnSplit());
container.MakeCuts(&ctx, &cuts, info.IsColumnSplit());
thrust::sort(x.begin(), x.end());
auto n_uniques = thrust::unique(x.begin(), x.end()) - x.begin();
@@ -522,6 +529,7 @@ void TestCategoricalSketchAdapter(size_t n, size_t num_categories,
TEST(HistUtil, AdapterDeviceSketchCategorical) {
auto categorical_sizes = {2, 6, 8, 12};
int num_bins = 256;
auto ctx = MakeCUDACtx(0);
auto sizes = {25, 100, 1000};
for (auto n : sizes) {
for (auto num_categories : categorical_sizes) {
@@ -529,7 +537,7 @@ TEST(HistUtil, AdapterDeviceSketchCategorical) {
auto dmat = GetDMatrixFromData(x, n, 1);
auto x_device = thrust::device_vector<float>(x);
auto adapter = AdapterFromData(x_device, n, 1);
ValidateBatchedCuts(adapter, num_bins, dmat.get());
ValidateBatchedCuts(&ctx, adapter, num_bins, dmat.get());
TestCategoricalSketchAdapter(n, num_categories, num_bins, true);
TestCategoricalSketchAdapter(n, num_categories, num_bins, false);
}
@@ -540,13 +548,14 @@ TEST(HistUtil, AdapterDeviceSketchMultipleColumns) {
auto bin_sizes = {2, 16, 256, 512};
auto sizes = {100, 1000, 1500};
int num_columns = 5;
auto ctx = MakeCUDACtx(0);
for (auto num_rows : sizes) {
auto x = GenerateRandom(num_rows, num_columns);
auto dmat = GetDMatrixFromData(x, num_rows, num_columns);
auto x_device = thrust::device_vector<float>(x);
for (auto num_bins : bin_sizes) {
auto adapter = AdapterFromData(x_device, num_rows, num_columns);
ValidateBatchedCuts(adapter, num_bins, dmat.get());
ValidateBatchedCuts(&ctx, adapter, num_bins, dmat.get());
}
}
}
@@ -556,12 +565,13 @@ TEST(HistUtil, AdapterDeviceSketchBatches) {
int num_rows = 5000;
auto batch_sizes = {0, 100, 1500, 6000};
int num_columns = 5;
auto ctx = MakeCUDACtx(0);
for (auto batch_size : batch_sizes) {
auto x = GenerateRandom(num_rows, num_columns);
auto dmat = GetDMatrixFromData(x, num_rows, num_columns);
auto x_device = thrust::device_vector<float>(x);
auto adapter = AdapterFromData(x_device, num_rows, num_columns);
ValidateBatchedCuts(adapter, num_bins, dmat.get(), batch_size);
ValidateBatchedCuts(&ctx, adapter, num_bins, dmat.get(), batch_size);
}
}
@@ -647,12 +657,12 @@ TEST(HistUtil, SketchingEquivalent) {
auto x_device = thrust::device_vector<float>(x);
auto adapter = AdapterFromData(x_device, num_rows, num_columns);
common::HistogramCuts adapter_cuts = MakeUnweightedCutsForTest(
adapter, num_bins, std::numeric_limits<float>::quiet_NaN());
&ctx, adapter, num_bins, std::numeric_limits<float>::quiet_NaN());
EXPECT_EQ(dmat_cuts.Values(), adapter_cuts.Values());
EXPECT_EQ(dmat_cuts.Ptrs(), adapter_cuts.Ptrs());
EXPECT_EQ(dmat_cuts.MinValues(), adapter_cuts.MinValues());
ValidateBatchedCuts(adapter, num_bins, dmat.get());
ValidateBatchedCuts(&ctx, adapter, num_bins, dmat.get());
}
}
}
@@ -702,7 +712,7 @@ void TestAdapterSketchFromWeights(bool with_group) {
.Device(DeviceOrd::CUDA(0))
.GenerateArrayInterface(&storage);
MetaInfo info;
Context ctx;
auto ctx = MakeCUDACtx(0);
auto& h_weights = info.weights_.HostVector();
if (with_group) {
h_weights.resize(kGroups);
@@ -731,7 +741,7 @@ void TestAdapterSketchFromWeights(bool with_group) {
&sketch_container);
common::HistogramCuts cuts;
sketch_container.MakeCuts(&cuts, info.IsColumnSplit());
sketch_container.MakeCuts(&ctx, &cuts, info.IsColumnSplit());
auto dmat = GetDMatrixFromData(storage.HostVector(), kRows, kCols);
if (with_group) {
@@ -744,10 +754,9 @@ void TestAdapterSketchFromWeights(bool with_group) {
ASSERT_EQ(cuts.Ptrs().size(), kCols + 1);
ValidateCuts(cuts, dmat.get(), kBins);
auto cuda_ctx = MakeCUDACtx(0);
if (with_group) {
dmat->Info().weights_ = decltype(dmat->Info().weights_)(); // remove weight
HistogramCuts non_weighted = DeviceSketch(&cuda_ctx, dmat.get(), kBins, 0);
HistogramCuts non_weighted = DeviceSketch(&ctx, dmat.get(), kBins, 0);
for (size_t i = 0; i < cuts.Values().size(); ++i) {
ASSERT_EQ(cuts.Values()[i], non_weighted.Values()[i]);
}
@@ -773,7 +782,7 @@ void TestAdapterSketchFromWeights(bool with_group) {
SketchContainer sketch_container{ft, kBins, kCols, kRows, DeviceOrd::CUDA(0)};
AdapterDeviceSketch(adapter.Value(), kBins, info, std::numeric_limits<float>::quiet_NaN(),
&sketch_container);
sketch_container.MakeCuts(&weighted, info.IsColumnSplit());
sketch_container.MakeCuts(&ctx, &weighted, info.IsColumnSplit());
ValidateCuts(weighted, dmat.get(), kBins);
}
}