Hendrik/xgboost
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Define CUDA Context. (#8604)

Browse Source 
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
Download Patch File Download Diff File Expand all files Collapse all files

38
src/data/data.cu
View File

@@ -1,18 +1,19 @@
/*!
* Copyright 2019-2021 by XGBoost Contributors
/**
* Copyright 2019-2022 by XGBoost Contributors
*
* \file data.cu
* \brief Handles setting metainfo from array interface.
*/
#include "xgboost/data.h"
#include "xgboost/logging.h"
#include "xgboost/json.h"
#include "array_interface.h"
#include "../common/cuda_context.cuh"
#include "../common/device_helpers.cuh"
#include "../common/linalg_op.cuh"
#include "array_interface.h"
#include "device_adapter.cuh"
#include "simple_dmatrix.h"
#include "validation.h"
#include "xgboost/data.h"
#include "xgboost/json.h"
#include "xgboost/logging.h"
namespace xgboost {
namespace {
@@ -25,7 +26,7 @@ auto SetDeviceToPtr(void const* ptr) {
}
template <typename T, int32_t D>
void CopyTensorInfoImpl(Json arr_interface, linalg::Tensor<T, D>* p_out) {
void CopyTensorInfoImpl(CUDAContext const* ctx, Json arr_interface, linalg::Tensor<T, D>* p_out) {
ArrayInterface<D> array(arr_interface);
if (array.n == 0) {
p_out->SetDevice(0);
@@ -43,15 +44,19 @@ void CopyTensorInfoImpl(Json arr_interface, linalg::Tensor<T, D>* p_out) {
// set data
data->Resize(array.n);
dh::safe_cuda(cudaMemcpyAsync(data->DevicePointer(), array.data, array.n * sizeof(T),
cudaMemcpyDefault));
cudaMemcpyDefault, ctx->Stream()));
});
return;
}
p_out->Reshape(array.shape);
auto t = p_out->View(ptr_device);
linalg::ElementWiseTransformDevice(t, [=] __device__(size_t i, T) {
return linalg::detail::Apply(TypedIndex<T, D>{array}, linalg::UnravelIndex<D>(i, array.shape));
});
linalg::ElementWiseTransformDevice(
t,
[=] __device__(size_t i, T) {
return linalg::detail::Apply(TypedIndex<T, D>{array},
linalg::UnravelIndex<D>(i, array.shape));
},
ctx->Stream());
}
void CopyGroupInfoImpl(ArrayInterface<1> column, std::vector<bst_group_t>* out) {
@@ -115,14 +120,13 @@ void CopyQidImpl(ArrayInterface<1> array_interface, std::vector<bst_group_t>* p_
}
} // namespace
// Context is not used until we have CUDA stream.
void MetaInfo::SetInfoFromCUDA(Context const&, StringView key, Json array) {
void MetaInfo::SetInfoFromCUDA(Context const& ctx, StringView key, Json array) {
// multi-dim float info
if (key == "base_margin") {
CopyTensorInfoImpl(array, &base_margin_);
CopyTensorInfoImpl(ctx.CUDACtx(), array, &base_margin_);
return;
} else if (key == "label") {
CopyTensorInfoImpl(array, &labels);
CopyTensorInfoImpl(ctx.CUDACtx(), array, &labels);
auto ptr = labels.Data()->ConstDevicePointer();
auto valid = thrust::none_of(thrust::device, ptr, ptr + labels.Size(), data::LabelsCheck{});
CHECK(valid) << "Label contains NaN, infinity or a value too large.";
@@ -142,7 +146,7 @@ void MetaInfo::SetInfoFromCUDA(Context const&, StringView key, Json array) {
}
// float info
linalg::Tensor<float, 1> t;
CopyTensorInfoImpl(array, &t);
CopyTensorInfoImpl(ctx.CUDACtx(), array, &t);
if (key == "weight") {
this->weights_ = std::move(*t.Data());
auto ptr = weights_.ConstDevicePointer();
@@ -156,7 +160,7 @@ void MetaInfo::SetInfoFromCUDA(Context const&, StringView key, Json array) {
this->feature_weights = std::move(*t.Data());
auto d_feature_weights = feature_weights.ConstDeviceSpan();
auto valid =
thrust::none_of(thrust::device, d_feature_weights.data(),
thrust::none_of(ctx.CUDACtx()->CTP(), d_feature_weights.data(),
d_feature_weights.data() + d_feature_weights.size(), data::WeightsCheck{});
CHECK(valid) << "Feature weight must be greater than 0.";
} else {