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Remove old cudf constructor code (#5194)

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This commit is contained in:
Rory Mitchell
2020-01-10 16:35:23 +13:00
committed by GitHub
parent 87ebfc1315
commit 8cbcc53ccb
5 changed files with 47 additions and 677 deletions
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47
src/data/device_adapter.cuh
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@@ -39,6 +39,53 @@ class CudfAdapterBatch : public detail::NoMetaInfo {
size_t num_elements;
};
/*!
* Please be careful that, in official specification, the only three required fields are
* `shape', `version' and `typestr'. Any other is optional, including `data'. But here
* we have one additional requirements for input data:
*
* - `data' field is required, passing in an empty dataset is not accepted, as most (if
* not all) of our algorithms don't have test for empty dataset. An error is better
* than a crash.
*
* What if invalid value from dataframe is 0 but I specify missing=NaN in XGBoost? Since
* validity mask is ignored, all 0s are preserved in XGBoost.
*
* FIXME(trivialfis): Put above into document after we have a consistent way for
* processing input data.
*
* Sample input:
* [
* {
* "shape": [
* 10
* ],
* "strides": [
* 4
* ],
* "data": [
* 30074864128,
* false
* ],
* "typestr": "<f4",
* "version": 1,
* "mask": {
* "shape": [
* 64
* ],
* "strides": [
* 1
* ],
* "data": [
* 30074864640,
* false
* ],
* "typestr": "|i1",
* "version": 1
* }
* }
* ]
*/
class CudfAdapter : public detail::SingleBatchDataIter<CudfAdapterBatch> {
public:
explicit CudfAdapter(std::string cuda_interfaces_str) {

81
src/data/simple_csr_source.cc
View File

@@ -56,86 +56,5 @@ const SparsePage& SimpleCSRSource::Value() const {
return page_;
}
/*!
* Please be careful that, in official specification, the only three required fields are
* `shape', `version' and `typestr'. Any other is optional, including `data'. But here
* we have one additional requirements for input data:
*
* - `data' field is required, passing in an empty dataset is not accepted, as most (if
* not all) of our algorithms don't have test for empty dataset. An error is better
* than a crash.
*
* Missing value handling:
* Missing value is specified:
* - Ignore the validity mask from columnar format.
* - Remove entries that equals to missing value.
* - missing = NaN:
* - Remove entries that is NaN
* - missing != NaN:
* - Check for NaN entries, throw an error if found.
* Missing value is not specified:
* - Remove entries that is specifed as by validity mask.
* - Remove NaN entries.
*
* What if invalid value from dataframe is 0 but I specify missing=NaN in XGBoost? Since
* validity mask is ignored, all 0s are preserved in XGBoost.
*
* FIXME(trivialfis): Put above into document after we have a consistent way for
* processing input data.
*
* Sample input:
* [
* {
* "shape": [
* 10
* ],
* "strides": [
* 4
* ],
* "data": [
* 30074864128,
* false
* ],
* "typestr": "<f4",
* "version": 1,
* "mask": {
* "shape": [
* 64
* ],
* "strides": [
* 1
* ],
* "data": [
* 30074864640,
* false
* ],
* "typestr": "|i1",
* "version": 1
* }
* }
* ]
*/
void SimpleCSRSource::CopyFrom(std::string const& cuda_interfaces_str,
bool has_missing, float missing) {
Json interfaces = Json::Load({cuda_interfaces_str.c_str(),
cuda_interfaces_str.size()});
std::vector<Json> const& columns = get<Array>(interfaces);
size_t n_columns = columns.size();
CHECK_GT(n_columns, 0) << "Number of columns must not eqaul to 0.";
auto const& typestr = get<String const>(columns[0]["typestr"]);
CHECK_EQ(typestr.size(), 3) << ColumnarErrors::TypestrFormat();
CHECK_NE(typestr.front(), '>') << ColumnarErrors::BigEndian();
this->FromDeviceColumnar(columns, has_missing, missing);
}
#if !defined(XGBOOST_USE_CUDA)
void SimpleCSRSource::FromDeviceColumnar(std::vector<Json> const& columns,
bool has_missing, float missing) {
LOG(FATAL) << "XGBoost version is not compiled with GPU support";
}
#endif // !defined(XGBOOST_USE_CUDA)
} // namespace data
} // namespace xgboost

200
src/data/simple_csr_source.cu
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@@ -1,200 +0,0 @@
/*!
* Copyright 2019 by XGBoost Contributors
*
* \file simple_csr_source.cuh
* \brief An extension for the simple CSR source in-memory data structure to accept
* foreign columnar.
*/
#include <thrust/device_ptr.h>
#include <thrust/device_vector.h>
#include <thrust/execution_policy.h>
#include <thrust/scan.h>
#include <xgboost/base.h>
#include <xgboost/data.h>
#include <cmath>
#include <vector>
#include <algorithm>
#include "simple_csr_source.h"
#include "columnar.h"
#include "../common/math.h"
#include "../common/bitfield.h"
#include "../common/device_helpers.cuh"
namespace xgboost {
namespace data {
__global__ void CountValidKernel(Columnar const column,
bool has_missing, float missing,
int32_t* flag, common::Span<bst_row_t> offsets) {
auto const tid = threadIdx.x + blockDim.x * blockIdx.x;
bool const missing_is_nan = common::CheckNAN(missing);
if (tid >= column.size) {
return;
}
RBitField8 const mask = column.valid;
if (!has_missing) {
if ((mask.Data() == nullptr || mask.Check(tid)) &&
!common::CheckNAN(column.GetElement(tid))) {
offsets[tid+1] += 1;
}
} else if (missing_is_nan) {
if (!common::CheckNAN(column.GetElement(tid))) {
offsets[tid+1] += 1;
}
} else {
if (!common::CloseTo(column.GetElement(tid), missing)) {
offsets[tid+1] += 1;
}
if (common::CheckNAN(column.GetElement(tid))) {
*flag = 1;
}
}
}
template <typename T>
__device__ void AssignValue(T fvalue, int32_t colid,
common::Span<bst_row_t> out_offsets, common::Span<Entry> out_data) {
auto const tid = threadIdx.x + blockDim.x * blockIdx.x;
int32_t oid = out_offsets[tid];
out_data[oid].fvalue = fvalue;
out_data[oid].index = colid;
out_offsets[tid] += 1;
}
__global__ void CreateCSRKernel(Columnar const column,
int32_t colid, bool has_missing, float missing,
common::Span<bst_row_t> offsets, common::Span<Entry> out_data) {
auto const tid = threadIdx.x + blockDim.x * blockIdx.x;
if (column.size <= tid) {
return;
}
bool const missing_is_nan = common::CheckNAN(missing);
if (!has_missing) {
// no missing value is specified
if ((column.valid.Data() == nullptr || column.valid.Check(tid)) &&
!common::CheckNAN(column.GetElement(tid))) {
AssignValue(column.GetElement(tid), colid, offsets, out_data);
}
} else if (missing_is_nan) {
// specified missing value, but it's NaN
if (!common::CheckNAN(column.GetElement(tid))) {
AssignValue(column.GetElement(tid), colid, offsets, out_data);
}
} else {
// specified missing value, and it's not NaN
if (!common::CloseTo(column.GetElement(tid), missing)) {
AssignValue(column.GetElement(tid), colid, offsets, out_data);
}
}
}
void CountValid(std::vector<Json> const& j_columns, uint32_t column_id,
bool has_missing, float missing,
HostDeviceVector<bst_row_t>* out_offset,
dh::caching_device_vector<int32_t>* out_d_flag,
uint32_t* out_n_rows) {
uint32_t constexpr kThreads = 256;
auto const& j_column = j_columns[column_id];
auto const& column_obj = get<Object const>(j_column);
Columnar foreign_column(column_obj);
uint32_t const n_rows = foreign_column.size;
auto ptr = foreign_column.data;
int32_t device = dh::CudaGetPointerDevice(ptr);
CHECK_NE(device, -1);
dh::safe_cuda(cudaSetDevice(device));
if (column_id == 0) {
out_offset->SetDevice(device);
out_offset->Resize(n_rows + 1);
}
CHECK_EQ(out_offset->DeviceIdx(), device)
<< "All columns should use the same device.";
CHECK_EQ(out_offset->Size(), n_rows + 1)
<< "All columns should have same number of rows.";
common::Span<bst_row_t> s_offsets = out_offset->DeviceSpan();
uint32_t const kBlocks = common::DivRoundUp(n_rows, kThreads);
dh::LaunchKernel {kBlocks, kThreads} (
CountValidKernel,
foreign_column,
has_missing, missing,
out_d_flag->data().get(), s_offsets);
*out_n_rows = n_rows;
}
void CreateCSR(std::vector<Json> const& j_columns, uint32_t column_id, uint32_t n_rows,
bool has_missing, float missing,
dh::device_vector<bst_row_t>* tmp_offset, common::Span<Entry> s_data) {
uint32_t constexpr kThreads = 256;
auto const& j_column = j_columns[column_id];
auto const& column_obj = get<Object const>(j_column);
Columnar foreign_column(column_obj);
uint32_t kBlocks = common::DivRoundUp(n_rows, kThreads);
dh::LaunchKernel {kBlocks, kThreads} (
CreateCSRKernel,
foreign_column, column_id, has_missing, missing,
dh::ToSpan(*tmp_offset), s_data);
}
void SimpleCSRSource::FromDeviceColumnar(std::vector<Json> const& columns,
bool has_missing, float missing) {
auto const n_cols = columns.size();
int32_t constexpr kThreads = 256;
dh::caching_device_vector<int32_t> d_flag;
if (!common::CheckNAN(missing)) {
d_flag.resize(1);
thrust::fill(d_flag.begin(), d_flag.end(), 0);
}
uint32_t n_rows {0};
for (size_t i = 0; i < n_cols; ++i) {
CountValid(columns, i, has_missing, missing, &(this->page_.offset), &d_flag,
&n_rows);
}
// don't pay for what you don't use.
if (!common::CheckNAN(missing)) {
int32_t flag {0};
dh::safe_cuda(cudaMemcpy(&flag, d_flag.data().get(), sizeof(int32_t), cudaMemcpyDeviceToHost));
CHECK_EQ(flag, 0) << "missing value is specifed but input data contains NaN.";
}
info.num_col_ = n_cols;
info.num_row_ = n_rows;
auto s_offsets = this->page_.offset.DeviceSpan();
thrust::device_ptr<bst_row_t> p_offsets(s_offsets.data());
CHECK_GE(s_offsets.size(), n_rows + 1);
thrust::inclusive_scan(p_offsets, p_offsets + n_rows + 1, p_offsets);
// Created for building csr matrix, where we need to change index after processing each
// column.
dh::device_vector<bst_row_t> tmp_offset(this->page_.offset.Size());
dh::safe_cuda(cudaMemcpy(tmp_offset.data().get(), s_offsets.data(),
s_offsets.size_bytes(), cudaMemcpyDeviceToDevice));
// We can use null_count from columnar data format, but that will add a non-standard
// entry in the array interface, also involves accumulating from all columns. Invoking
// one copy seems easier.
this->info.num_nonzero_ = tmp_offset.back();
// Device is obtained and set in `CountValid'
int32_t const device = this->page_.offset.DeviceIdx();
this->page_.data.SetDevice(device);
this->page_.data.Resize(this->info.num_nonzero_);
auto s_data = this->page_.data.DeviceSpan();
int32_t kBlocks = common::DivRoundUp(n_rows, kThreads);
for (size_t i = 0; i < n_cols; ++i) {
CreateCSR(columns, i, n_rows, has_missing, missing, &tmp_offset, s_data);
}
}
} // namespace data
} // namespace xgboost

16
src/data/simple_csr_source.h
View File

@@ -46,14 +46,6 @@ class SimpleCSRSource : public DataSource<SparsePage> {
*/
void CopyFrom(DMatrix* src);
/*!
* \brief copy content of data from foreign **GPU** columnar buffer.
* \param interfaces_str JSON representation of cuda array interfaces.
* \param has_missing Whether did users supply their own missing value.
* \param missing The missing value set by users.
*/
void CopyFrom(std::string const& cuda_interfaces_str, bool has_missing,
bst_float missing = std::numeric_limits<float>::quiet_NaN());
/*!
* \brief Load data from binary stream.
* \param fi the pointer to load data from.
@@ -74,14 +66,6 @@ class SimpleCSRSource : public DataSource<SparsePage> {
static const int kMagic = 0xffffab01;
private:
/*!
* \brief copy content of data from foreign GPU columnar buffer.
* \param columns JSON representation of array interfaces.
* \param missing specifed missing value
*/
void FromDeviceColumnar(std::vector<Json> const& columns,
bool has_missing = false,
float missing = std::numeric_limits<float>::quiet_NaN());
/*! \brief internal variable, used to support iterator interface */
bool at_first_{true};
};