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Define CUDA Context. (#8604)

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We will transition to non-default and non-blocking CUDA stream.
This commit is contained in:
Jiaming Yuan
2022-12-20 15:15:07 +08:00
committed by GitHub
parent e01639548a
commit c6a8754c62
11 changed files with 120 additions and 62 deletions
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36
tests/cpp/tree/gpu_hist/test_histogram.cu
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@@ -11,6 +11,7 @@ namespace xgboost {
namespace tree {
void TestDeterministicHistogram(bool is_dense, int shm_size) {
Context ctx = CreateEmptyGenericParam(0);
size_t constexpr kBins = 256, kCols = 120, kRows = 16384, kRounds = 16;
float constexpr kLower = -1e-2, kUpper = 1e2;
@@ -34,9 +35,9 @@ void TestDeterministicHistogram(bool is_dense, int shm_size) {
sizeof(GradientPairInt64));
auto quantiser = GradientQuantiser(gpair.DeviceSpan());
BuildGradientHistogram(page->GetDeviceAccessor(0),
feature_groups.DeviceAccessor(0), gpair.DeviceSpan(),
ridx, d_histogram, quantiser);
BuildGradientHistogram(ctx.CUDACtx(), page->GetDeviceAccessor(0),
feature_groups.DeviceAccessor(0), gpair.DeviceSpan(), ridx, d_histogram,
quantiser);
std::vector<GradientPairInt64> histogram_h(num_bins);
dh::safe_cuda(cudaMemcpy(histogram_h.data(), d_histogram.data(),
@@ -48,10 +49,9 @@ void TestDeterministicHistogram(bool is_dense, int shm_size) {
auto d_new_histogram = dh::ToSpan(new_histogram);
auto quantiser = GradientQuantiser(gpair.DeviceSpan());
BuildGradientHistogram(page->GetDeviceAccessor(0),
feature_groups.DeviceAccessor(0),
gpair.DeviceSpan(), ridx, d_new_histogram,
quantiser);
BuildGradientHistogram(ctx.CUDACtx(), page->GetDeviceAccessor(0),
feature_groups.DeviceAccessor(0), gpair.DeviceSpan(), ridx,
d_new_histogram, quantiser);
std::vector<GradientPairInt64> new_histogram_h(num_bins);
dh::safe_cuda(cudaMemcpy(new_histogram_h.data(), d_new_histogram.data(),
@@ -71,10 +71,9 @@ void TestDeterministicHistogram(bool is_dense, int shm_size) {
FeatureGroups single_group(page->Cuts());
dh::device_vector<GradientPairInt64> baseline(num_bins);
BuildGradientHistogram(page->GetDeviceAccessor(0),
single_group.DeviceAccessor(0),
gpair.DeviceSpan(), ridx, dh::ToSpan(baseline),
quantiser);
BuildGradientHistogram(ctx.CUDACtx(), page->GetDeviceAccessor(0),
single_group.DeviceAccessor(0), gpair.DeviceSpan(), ridx,
dh::ToSpan(baseline), quantiser);
std::vector<GradientPairInt64> baseline_h(num_bins);
dh::safe_cuda(cudaMemcpy(baseline_h.data(), baseline.data().get(),
@@ -115,6 +114,7 @@ void ValidateCategoricalHistogram(size_t n_categories, common::Span<GradientPair
// Test 1 vs rest categorical histogram is equivalent to one hot encoded data.
void TestGPUHistogramCategorical(size_t num_categories) {
auto ctx = CreateEmptyGenericParam(0);
size_t constexpr kRows = 340;
size_t constexpr kBins = 256;
auto x = GenerateRandomCategoricalSingleColumn(kRows, num_categories);
@@ -133,10 +133,9 @@ void TestGPUHistogramCategorical(size_t num_categories) {
for (auto const &batch : cat_m->GetBatches<EllpackPage>(batch_param)) {
auto* page = batch.Impl();
FeatureGroups single_group(page->Cuts());
BuildGradientHistogram(page->GetDeviceAccessor(0),
single_group.DeviceAccessor(0),
gpair.DeviceSpan(), ridx, dh::ToSpan(cat_hist),
quantiser);
BuildGradientHistogram(ctx.CUDACtx(), page->GetDeviceAccessor(0),
single_group.DeviceAccessor(0), gpair.DeviceSpan(), ridx,
dh::ToSpan(cat_hist), quantiser);
}
/**
@@ -148,10 +147,9 @@ void TestGPUHistogramCategorical(size_t num_categories) {
for (auto const &batch : encode_m->GetBatches<EllpackPage>(batch_param)) {
auto* page = batch.Impl();
FeatureGroups single_group(page->Cuts());
BuildGradientHistogram(page->GetDeviceAccessor(0),
single_group.DeviceAccessor(0),
gpair.DeviceSpan(), ridx, dh::ToSpan(encode_hist),
quantiser);
BuildGradientHistogram(ctx.CUDACtx(), page->GetDeviceAccessor(0),
single_group.DeviceAccessor(0), gpair.DeviceSpan(), ridx,
dh::ToSpan(encode_hist), quantiser);
}
std::vector<GradientPairInt64> h_cat_hist(cat_hist.size());