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Thread safe, inplace prediction. (#5389)

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Normal prediction with DMatrix is now thread safe with locks.  Added inplace prediction is lock free thread safe.

When data is on device (cupy, cudf), the returned data is also on device.

* Implementation for numpy, csr, cudf and cupy.

* Implementation for dask.

* Remove sync in simple dmatrix.
This commit is contained in:
Jiaming Yuan
2020-03-30 15:35:28 +08:00
committed by GitHub
parent 7f980e9f83
commit 6601a641d7
25 changed files with 1217 additions and 167 deletions
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90
src/c_api/c_api.cc
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@@ -12,6 +12,7 @@
#include "xgboost/base.h"
#include "xgboost/data.h"
#include "xgboost/host_device_vector.h"
#include "xgboost/learner.h"
#include "xgboost/c_api.h"
#include "xgboost/logging.h"
@@ -450,6 +451,95 @@ XGB_DLL int XGBoosterPredict(BoosterHandle handle,
API_END();
}
// A hidden API as cache id is not being supported yet.
XGB_DLL int XGBoosterPredictFromDense(BoosterHandle handle, float *values,
xgboost::bst_ulong n_rows,
xgboost::bst_ulong n_cols,
float missing,
unsigned iteration_begin,
unsigned iteration_end,
char const* c_type,
xgboost::bst_ulong cache_id,
xgboost::bst_ulong *out_len,
const float **out_result) {
API_BEGIN();
CHECK_HANDLE();
CHECK_EQ(cache_id, 0) << "Cache ID is not supported yet";
auto *learner = static_cast<xgboost::Learner *>(handle);
auto x = xgboost::data::DenseAdapter(values, n_rows, n_cols);
HostDeviceVector<float>* p_predt { nullptr };
std::string type { c_type };
learner->InplacePredict(x, type, missing, &p_predt);
CHECK(p_predt);
*out_result = dmlc::BeginPtr(p_predt->HostVector());
*out_len = static_cast<xgboost::bst_ulong>(p_predt->Size());
API_END();
}
// A hidden API as cache id is not being supported yet.
XGB_DLL int XGBoosterPredictFromCSR(BoosterHandle handle,
const size_t* indptr,
const unsigned* indices,
const bst_float* data,
size_t nindptr,
size_t nelem,
size_t num_col,
float missing,
unsigned iteration_begin,
unsigned iteration_end,
char const *c_type,
xgboost::bst_ulong cache_id,
xgboost::bst_ulong *out_len,
const float **out_result) {
API_BEGIN();
CHECK_HANDLE();
CHECK_EQ(cache_id, 0) << "Cache ID is not supported yet";
auto *learner = static_cast<xgboost::Learner *>(handle);
auto x = data::CSRAdapter(indptr, indices, data, nindptr - 1, nelem, num_col);
HostDeviceVector<float>* p_predt { nullptr };
std::string type { c_type };
learner->InplacePredict(x, type, missing, &p_predt);
CHECK(p_predt);
*out_result = dmlc::BeginPtr(p_predt->HostVector());
*out_len = static_cast<xgboost::bst_ulong>(p_predt->Size());
API_END();
}
#if !defined(XGBOOST_USE_CUDA)
XGB_DLL int XGBoosterPredictFromArrayInterfaceColumns(BoosterHandle handle,
char const* c_json_strs,
float missing,
unsigned iteration_begin,
unsigned iteration_end,
char const* c_type,
xgboost::bst_ulong cache_id,
xgboost::bst_ulong *out_len,
float const** out_result) {
API_BEGIN();
CHECK_HANDLE();
LOG(FATAL) << "XGBoost not compiled with CUDA.";
API_END();
}
XGB_DLL int XGBoosterPredictFromArrayInterface(BoosterHandle handle,
char const* c_json_strs,
float missing,
unsigned iteration_begin,
unsigned iteration_end,
char const* c_type,
xgboost::bst_ulong cache_id,
xgboost::bst_ulong *out_len,
const float **out_result) {
API_BEGIN();
CHECK_HANDLE();
LOG(FATAL) << "XGBoost not compiled with CUDA.";
API_END();
}
#endif // !defined(XGBOOST_USE_CUDA)
XGB_DLL int XGBoosterLoadModel(BoosterHandle handle, const char* fname) {
API_BEGIN();
CHECK_HANDLE();