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Further address Daoyuan's comments, clean the code.

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avx_docs
xutianbing 8 years ago
parent b3be735807
commit 999cd14a6e
9 changed files with 147 additions and 131 deletions
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8
paddle/function/BufferArg.cpp
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@ -34,8 +34,8 @@ SparseMatrixArg::SparseMatrixArg(const CpuSparseMatrix& sparse, ArgType argType)
row_(reinterpret_cast<void*>(sparse.getRows()), VALUE_TYPE_INT32),
col_(reinterpret_cast<void*>(sparse.getCols()), VALUE_TYPE_INT32),
nnz_(sparse.getElementCnt()),
format_(sparse.getFormat()),
type_(sparse.getValueType()) {
format_(static_cast<SparseDataFormat>(sparse.getFormat())),
type_(static_cast<SparseDataType>(sparse.getValueType())) {
bufferType_ = TENSOR_SPARSE;
}
@ -44,8 +44,8 @@ SparseMatrixArg::SparseMatrixArg(const GpuSparseMatrix& sparse, ArgType argType)
row_(reinterpret_cast<void*>(sparse.getRows()), VALUE_TYPE_INT32),
col_(reinterpret_cast<void*>(sparse.getCols()), VALUE_TYPE_INT32),
nnz_(sparse.getElementCnt()),
format_(sparse.getFormat()),
type_(sparse.getValueType()) {
format_(static_cast<SparseDataFormat>(sparse.getFormat())),
type_(static_cast<SparseDataType>(sparse.getValueType())) {
bufferType_ = TENSOR_SPARSE;
}

69
paddle/function/BufferArg.h
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@ -72,19 +72,21 @@ public:
BufferArg(ValueType valueType,
const TensorShape& shape,
ArgType argType = UNSPECIFIED)
: buf_(nullptr),
valueType_(valueType),
shape_(shape),
argType_(argType) {}
: buf_(nullptr), valueType_(valueType), shape_(shape), argType_(argType) {
bufferType_ = TENSOR_NORMAL;
}
BufferArg(void* buf,
ValueType valueType,
const TensorShape& shape,
ArgType argType = UNSPECIFIED)
: buf_(buf), valueType_(valueType), shape_(shape), argType_(argType) {}
: buf_(buf), valueType_(valueType), shape_(shape), argType_(argType) {
bufferType_ = TENSOR_NORMAL;
}
BufferArg(void* buf, ValueType valueType)
: buf_(buf), valueType_(valueType) {}
BufferArg(void* buf, ValueType valueType) : buf_(buf), valueType_(valueType) {
bufferType_ = TENSOR_NORMAL;
}
BufferArg(const Matrix& matrix, ArgType argType = UNSPECIFIED)
: buf_(
@ -173,7 +175,7 @@ protected:
TensorShape shape_;
BufferType bufferType_{TENSOR_UNKNOWN};
ArgType argType_{UNSPECIFIED};
// todo(tianbing), add deviceType_
// TODO(tianbing), add deviceType_
// leading dimensions. The size is dims_.size()
// Dims lds_;
};
@ -186,6 +188,7 @@ class SequenceIdArg : public BufferArg {
public:
SequenceIdArg(const TensorShape& shape, ArgType argType = UNSPECIFIED)
: BufferArg(VALUE_TYPE_INT32, shape, argType) {
bufferType_ = TENSOR_SEQUENCE_ID;
CHECK_EQ(shape_.ndims(), (size_t)1);
CHECK_GT(shape_[0], 1);
numSeqs_ = shape_[0] - 1;
@ -223,7 +226,9 @@ public:
SequenceArg(ValueType valueType,
const TensorShape& shape,
ArgType argType = UNSPECIFIED)
: BufferArg(valueType, shape, argType), startPositions_(TensorShape()) {}
: BufferArg(valueType, shape, argType), startPositions_(TensorShape()) {
bufferType_ = TENSOR_SEQUENCE_DATA;
}
SequenceArg(void* buf,
ValueType valueType,
@ -271,16 +276,16 @@ public:
row_(row),
col_(col),
nnz_(nnz),
format_(format),
type_(type) {
format_(static_cast<SparseDataFormat>(format)),
type_(static_cast<SparseDataType>(type)) {
bufferType_ = TENSOR_SPARSE;
CHECK((valueType == VALUE_TYPE_FLOAT) || (valueType == VALUE_TYPE_DOUBLE));
CHECK_EQ(shape_.ndims(), (size_t)2);
CHECK_EQ(row_.shape().ndims(), (size_t)1);
CHECK_EQ(col_.shape().ndims(), (size_t)1);
if (format == SPARSE_CSR) {
if (format_ == T_SPARSE_CSR) {
CHECK_EQ(nnz, col.shape()[0]);
} else if (format == SPARSE_CSC) {
} else if (format_ == T_SPARSE_CSC) {
CHECK_EQ(nnz, row.shape()[0]);
}
}
@ -292,23 +297,23 @@ public:
SparseValueType type,
ArgType argType = UNSPECIFIED)
: BufferArg(valueType, shape, argType),
/// len of row_ : height + 1 (CSR), buf_ == nullptr
row_(format == SPARSE_CSR
? BufferArg(VALUE_TYPE_INT32, TensorShape{shape[0] + 1})
: BufferArg(VALUE_TYPE_INT32, TensorShape{nnz})),
/// len of col_ : width + 1 (CSC), buf_ == nullptr
col_(format == SPARSE_CSR
? BufferArg(VALUE_TYPE_INT32, TensorShape{nnz})
: BufferArg(VALUE_TYPE_INT32, TensorShape{shape[1] + 1})),
row_(BufferArg(nullptr, VALUE_TYPE_INT32)),
col_(BufferArg(nullptr, VALUE_TYPE_INT32)),
nnz_(nnz),
format_(format),
type_(type) {
format_(static_cast<SparseDataFormat>(format)),
type_(static_cast<SparseDataType>(type)) {
bufferType_ = TENSOR_SPARSE;
/// todo(tianbing)
/// valueType and shape_.ndims() == 2 need to check before
/// this constructor to make sure row_ and col_ are right
CHECK((valueType == VALUE_TYPE_FLOAT) || (valueType == VALUE_TYPE_DOUBLE));
CHECK_EQ(shape_.ndims(), (size_t)2);
/// len of row_ : height + 1 (CSR) or nnz (CSC), buf_ == nullptr
row_ = (format_ == T_SPARSE_CSR
? BufferArg(VALUE_TYPE_INT32, TensorShape{shape_[0] + 1})
: BufferArg(VALUE_TYPE_INT32, TensorShape{nnz}));
/// len of col_ : width + 1 (CSC) or nnz (CSR), buf_ == nullptr
col_ = (format_ == T_SPARSE_CSR
? BufferArg(VALUE_TYPE_INT32, TensorShape{nnz})
: BufferArg(VALUE_TYPE_INT32, TensorShape{shape_[1] + 1}));
}
SparseMatrixArg(const CpuSparseMatrix& sparse, ArgType argType = UNSPECIFIED);
@ -328,8 +333,8 @@ public:
shape_[0],
shape_[1],
nnz_,
type_,
format_,
static_cast<SparseValueType>(type_),
static_cast<SparseFormat>(format_),
false);
}
@ -343,16 +348,16 @@ public:
size_t numElements() const override { return nnz_; }
SparseFormat dataFormat() const { return format_; }
SparseDataFormat dataFormat() const { return format_; }
SparseValueType dataType() const { return type_; }
SparseDataType dataType() const { return type_; }
private:
BufferArg row_;
BufferArg col_;
size_t nnz_;
SparseFormat format_;
SparseValueType type_;
SparseDataFormat format_;
SparseDataType type_;
};
} // namespace paddle

71
paddle/function/FunctionTest.h
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@ -15,7 +15,6 @@ limitations under the License. */
#include "Function.h"
#include "paddle/math/Matrix.h"
#include "paddle/math/SparseMatrix.h"
#include "paddle/math/Vector.h"
#include "paddle/math/tests/TensorCheck.h"
#include "paddle/testing/TestUtil.h"
@ -77,33 +76,33 @@ public:
cpuMemory_.emplace_back(std::make_shared<CpuMemoryHandle>(size));
gpuMemory_.emplace_back(std::make_shared<GpuMemoryHandle>(size));
cpuOutputs_.emplace_back(std::make_shared<BufferArg>(
cpuMemory_.back()->getBuf(),
output.valueType(),
output.shape(),
// todo(tianbing), argType = output.getArgType(), but default ADD_TO
argType));
gpuOutputs_.emplace_back(std::make_shared<BufferArg>(
gpuMemory_.back()->getBuf(),
output.valueType(),
output.shape(),
// todo(tianbing), argType = output.getArgType(), but default ADD_TO
argType));
cpuOutputs_.emplace_back(
std::make_shared<BufferArg>(cpuMemory_.back()->getBuf(),
output.valueType(),
output.shape(),
argType));
gpuOutputs_.emplace_back(
std::make_shared<BufferArg>(gpuMemory_.back()->getBuf(),
output.valueType(),
output.shape(),
argType));
}
/// add and init output sparse matrix
void addOutputs(const SparseMatrixArg& output, ArgType argType = ASSIGN_TO) {
cpuSparse_ = std::make_shared<CpuSparseMatrix>(output.shape()[0],
output.shape()[1],
output.nnz(),
output.dataType(),
output.dataFormat());
gpuSparse_ = std::make_shared<GpuSparseMatrix>(output.shape()[0],
output.shape()[1],
output.nnz(),
output.dataType(),
output.dataFormat());
cpuSparse_ = std::make_shared<CpuSparseMatrix>(
output.shape()[0],
output.shape()[1],
output.nnz(),
static_cast<SparseValueType>(output.dataType()),
static_cast<SparseFormat>(output.dataFormat()));
gpuSparse_ = std::make_shared<GpuSparseMatrix>(
output.shape()[0],
output.shape()[1],
output.nnz(),
static_cast<SparseValueType>(output.dataType()),
static_cast<SparseFormat>(output.dataFormat()));
/// init sparse matrix
hl_stream_t stream(HPPL_STREAM_1);
@ -138,17 +137,19 @@ public:
}
void addInputs(const SparseMatrixArg& input) {
cpuSparse_ = std::make_shared<CpuSparseMatrix>(input.shape()[0],
input.shape()[1],
input.nnz(),
input.dataType(),
input.dataFormat());
gpuSparse_ = std::make_shared<GpuSparseMatrix>(input.shape()[0],
input.shape()[1],
input.nnz(),
input.dataType(),
input.dataFormat());
cpuSparse_ = std::make_shared<CpuSparseMatrix>(
input.shape()[0],
input.shape()[1],
input.nnz(),
static_cast<SparseValueType>(input.dataType()),
static_cast<SparseFormat>(input.dataFormat()));
gpuSparse_ = std::make_shared<GpuSparseMatrix>(
input.shape()[0],
input.shape()[1],
input.nnz(),
static_cast<SparseValueType>(input.dataType()),
static_cast<SparseFormat>(input.dataFormat()));
/// init sparse matrix
hl_stream_t stream(HPPL_STREAM_1);

49
paddle/function/MulOp.cpp
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@ -41,6 +41,7 @@ inline void colVecAddTo(
} // namespace
namespace paddle {
/// sparse matrix (+)= dense matrix * dense matrix
template <>
void MulOp<DEVICE_TYPE_CPU>(CpuSparseMatrix& out,
const CpuMatrix& a,
@ -105,6 +106,7 @@ void MulOp<DEVICE_TYPE_CPU>(CpuSparseMatrix& out,
}
}
/// dense matrix (+)= dense matrix * dense matrix
template <>
void MulOp<DEVICE_TYPE_CPU>(CpuMatrix& out,
const CpuMatrix& a,
@ -129,6 +131,7 @@ void MulOp<DEVICE_TYPE_CPU>(CpuMatrix& out,
out.getStride());
}
/// dense matrix (+)= sparse matrix * dense matrix
template <>
void MulOp<DEVICE_TYPE_CPU>(CpuMatrix& out,
const CpuSparseMatrix& a,
@ -138,8 +141,6 @@ void MulOp<DEVICE_TYPE_CPU>(CpuMatrix& out,
bool aTrans,
bool bTrans,
bool cTrans) {
CHECK_EQ(a.getFormat(), SPARSE_CSR)
<< "Not supported SPARSE_CSR format for a";
if (scaleT == 0) {
out.zeroMem();
}
@ -165,6 +166,7 @@ void MulOp<DEVICE_TYPE_CPU>(CpuMatrix& out,
}
}
/// dense matrix (+)= dense matrix * sparse matrix
template <>
void MulOp<DEVICE_TYPE_CPU>(CpuMatrix& out,
const CpuMatrix& a,
@ -183,7 +185,7 @@ void MulOp<DEVICE_TYPE_CPU>(CpuMatrix& out,
int* rows = b.getRows();
int* cols = b.getCols();
/// b.getFormat() == SPARSE_CSC
/// SPARSE_CSC format
if (b.getFormat() == SPARSE_CSC) {
for (size_t j = 0; j < b.getWidth(); ++j) {
int start = b.getColStartIdx(j);
@ -200,7 +202,7 @@ void MulOp<DEVICE_TYPE_CPU>(CpuMatrix& out,
return;
}
/// b.getFormat() == SPARSE_CSR
/// SPARSE_CSR format
if (b.getFormat() == SPARSE_CSR) {
for (size_t j = 0; j < b.getHeight(); ++j) {
int start = b.getRowStartIdx(j);
@ -220,11 +222,32 @@ void MulOp<DEVICE_TYPE_CPU>(CpuMatrix& out,
/**
* mul operator
* out = scaleT * out + scaleAB*(in1 * in2)
* out = scaleT * out + scaleAB * (in1 * in2)
* here, scaleT in {0, 1}, scaleAB == 1,
* out = in1 (A) * in2 (B), ASSIGN_TO
* out += in1 (A) * in2 (B), ADD_TO
*
*
* \param outputs[0] output matrix (out), M * N,
* could be either Sparse or Dense Matrix
* M is num of rows, N is num of columns
* \param inputs[0] first input matrix (A), M * K (if non-trans)
* could be either Sparse or Dense Matrix
* M is num of rows, K is num of columns
* \param inputs[1] second input matrix (B), K * N (if non-trans)
* could be either Sparse or Dense Matrix
* K is num of rows, N is num of columns
*
* Support eight Mul operators, with both GPU and CPU devices
* For each device, four Mul operators are supported:
* 1. dense (out) = dense (A) * dense (B)
* 2. dense (out) = sparse (A) * dense (B)
* sparse matrix only support SPARSE_CSR format
* 3. dense (out) = dense (A) * sparse (B)
* sparse matrix support SPARSE_CSC and SPARSE_CSR formats
* 4. sparse (out) = dense (A) * dense (B)
* sparse matrix support SPARSE_CSC and SPARSE_CSR formats
*
* \param outputs[0] output matrix, M * N
* \param inputs[0] first input (sparse) matrix, M * K (if non-trans)
* \param inputs[1] second input matrix, K * N (if non-trans)
*/
template <DeviceType Device>
class MulFunc : public FunctionBase {
@ -271,7 +294,7 @@ public:
!inputs[1].isSparseArg()));
auto outMat = outputs[0].matrix<Device>();
/// matrix = matrix * matrix
/// dense matrix = dense matrix * dense matrix
if (!inputs[0].isSparseArg() && !inputs[1].isSparseArg() &&
!outputs[0].isSparseArg()) {
MulOp<Device>(outMat,
@ -285,7 +308,7 @@ public:
return;
}
/// matrix = matrix * sparse matrix
/// dense matrix = dense matrix * sparse matrix
if (!inputs[0].isSparseArg() && inputs[1].isSparseArg() &&
!outputs[0].isSparseArg()) {
CHECK(!aTrans_) << "Not supported a transpose";
@ -300,10 +323,12 @@ public:
return;
}
/// matrix = sparse matrix * matrix
/// dense matrix = sparse matrix * dense matrix
if (inputs[0].isSparseArg() && !inputs[1].isSparseArg() &&
!outputs[0].isSparseArg()) {
CHECK(!bTrans_) << "Not supported b transpose";
CHECK_EQ(inputs[0].sparse().dataFormat(), T_SPARSE_CSR)
<< "Only supported SPARSE_CSR format for sparse matrix a";
MulOp<Device>(outMat,
inputs[0].sparse().SparseMatrix<Device>(),
inputs[1].matrix<Device>(),
@ -315,7 +340,7 @@ public:
return;
}
/// sparse matrix = matrix * matrix
/// sparse matrix = dense matrix * dense matrix
auto outSparseMat = outputs[0].sparse().SparseMatrix<Device>();
if (!inputs[0].isSparseArg() && !inputs[1].isSparseArg() &&
outputs[0].isSparseArg()) {

11
paddle/function/MulOp.h
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@ -15,12 +15,11 @@ limitations under the License. */
#pragma once
#include "Function.h"
/// todo(tianbing), delete it
#include <iostream>
#include "paddle/math/Matrix.h"
#include "paddle/math/SparseMatrix.h"
namespace paddle {
/// CPU, dense matrix (+)= dense matrix * dense matrix
template <DeviceType DType>
void MulOp(CpuMatrix& out,
const CpuMatrix& a,
@ -31,6 +30,7 @@ void MulOp(CpuMatrix& out,
bool bTrans,
bool cTrans);
/// CPU, dense matrix (+)= sparse matrix * dense matrix
template <DeviceType DType>
void MulOp(CpuMatrix& out,
const CpuSparseMatrix& a,
@ -41,6 +41,7 @@ void MulOp(CpuMatrix& out,
bool bTrans,
bool cTrans);
/// CPU, dense matrix (+)= dense matrix * sparse matrix
template <DeviceType DType>
void MulOp(CpuMatrix& out,
const CpuMatrix& a,
@ -51,6 +52,7 @@ void MulOp(CpuMatrix& out,
bool bTrans,
bool cTrans);
/// CPU, sparse matrix (+)= dense matrix * dense matrix
template <DeviceType DType>
void MulOp(CpuSparseMatrix& out,
const CpuMatrix& a,
@ -61,6 +63,7 @@ void MulOp(CpuSparseMatrix& out,
bool bTrans,
bool cTrans);
/// GPU, dense matrix (+)= dense matrix * dense matrix
template <DeviceType DType>
void MulOp(GpuMatrix& out,
const GpuMatrix& a,
@ -71,6 +74,7 @@ void MulOp(GpuMatrix& out,
bool bTrans,
bool cTrans);
/// GPU, dense matrix (+)= sparse matrix * dense matrix
template <DeviceType DType>
void MulOp(GpuMatrix& out,
const GpuSparseMatrix& a,
@ -81,6 +85,7 @@ void MulOp(GpuMatrix& out,
bool bTrans,
bool cTrans);
/// GPU, dense matrix (+)= dense matrix * sparse matrix
template <DeviceType DType>
void MulOp(GpuMatrix& out,
const GpuMatrix& a,
@ -90,7 +95,7 @@ void MulOp(GpuMatrix& out,
bool aTrans,
bool bTrans,
bool cTrans);
/// GPU, sparse matrix (+)= dense matrix * dense matrix
template <DeviceType DType>
void MulOp(GpuSparseMatrix& out,
const GpuMatrix& a,

62
paddle/function/MulOpGpu.cu
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@ -18,10 +18,7 @@ limitations under the License. */
#include "paddle/math/SparseMatrix.h"
namespace paddle {
/**
* out = scaleT * out + scaleAB * (a * b)
* out : output matrix, M * N
*/
/// dense matrix (+)= dense matrix * dense matrix
template <>
void MulOp<DEVICE_TYPE_GPU>(GpuMatrix& out,
const GpuMatrix& a,
@ -32,14 +29,11 @@ void MulOp<DEVICE_TYPE_GPU>(GpuMatrix& out,
bool bTrans,
bool cTrans) {
CHECK(a.useGpu_ && b.useGpu_) << "matrix device type not match";
real* aData = const_cast<real*>(a.getData());
real* bData = const_cast<real*>(b.getData());
real* outData = const_cast<real*>(out.getData());
hl_matrix_mul(aData,
hl_matrix_mul(const_cast<real*>(a.getData()),
!aTrans ? HPPL_OP_N : HPPL_OP_T,
bData,
const_cast<real*>(b.getData()),
!bTrans ? HPPL_OP_N : HPPL_OP_T,
outData,
const_cast<real*>(out.getData()),
out.getHeight(),
out.getWidth(),
!aTrans ? a.getWidth() : a.getHeight(),
@ -50,10 +44,7 @@ void MulOp<DEVICE_TYPE_GPU>(GpuMatrix& out,
out.getStride());
}
/**
* out = scaleT * out + scaleAB * (a * b)
* out : M * N
*/
/// dense matrix (+)= sparse matrix * dense matrix
template <>
void MulOp<DEVICE_TYPE_GPU>(GpuMatrix& out,
const GpuSparseMatrix& a,
@ -66,15 +57,11 @@ void MulOp<DEVICE_TYPE_GPU>(GpuMatrix& out,
CHECK(out.isContiguous());
CHECK(b.isContiguous());
CHECK(a.useGpu_ && b.useGpu_) << "matrix device type not match";
hl_sparse_matrix_s aData = a.sMatrix_.get();
real* bData = const_cast<real*>(b.getData());
real* outData = const_cast<real*>(out.getData());
hl_matrix_csr_mul_dense(aData,
hl_matrix_csr_mul_dense(a.sMatrix_.get(),
aTrans ? HPPL_OP_T : HPPL_OP_N,
bData,
const_cast<real*>(b.getData()),
HPPL_OP_N,
outData,
const_cast<real*>(out.getData()),
out.getHeight(),
out.getWidth(),
b.getHeight(),
@ -82,10 +69,7 @@ void MulOp<DEVICE_TYPE_GPU>(GpuMatrix& out,
scaleT);
}
/**
* out = scaleT * out + scaleAB * (a * b)
* out : M * N
*/
/// dense matrix (+)= dense matrix * sparse matrix
template <>
void MulOp<DEVICE_TYPE_GPU>(GpuMatrix& out,
const GpuMatrix& a,
@ -99,27 +83,23 @@ void MulOp<DEVICE_TYPE_GPU>(GpuMatrix& out,
CHECK(a.isContiguous());
CHECK(a.useGpu_ && b.useGpu_) << "matrix device type not match";
hl_sparse_matrix_s bData = b.sMatrix_.get();
real* aData = const_cast<real*>(a.getData());
real* outData = const_cast<real*>(out.getData());
if (b.format_ == SPARSE_CSC) {
hl_matrix_dense_mul_csc(aData,
hl_matrix_dense_mul_csc(const_cast<real*>(a.getData()),
HPPL_OP_N,
bData,
b.sMatrix_.get(),
bTrans ? HPPL_OP_T : HPPL_OP_N,
outData,
const_cast<real*>(out.getData()),
out.getHeight(),
out.getWidth(),
a.getWidth(),
scaleAB,
scaleT);
} else {
hl_matrix_dense_mul_csr(aData,
hl_matrix_dense_mul_csr(const_cast<real*>(a.getData()),
HPPL_OP_N,
bData,
b.sMatrix_.get(),
bTrans ? HPPL_OP_T : HPPL_OP_N,
outData,
const_cast<real*>(out.getData()),
out.getHeight(),
out.getWidth(),
a.getWidth(),
@ -128,6 +108,7 @@ void MulOp<DEVICE_TYPE_GPU>(GpuMatrix& out,
}
}
/// sparse matrix (+)= dense matrix * dense matrix
template <>
void MulOp<DEVICE_TYPE_GPU>(GpuSparseMatrix& out,
const GpuMatrix& a,
@ -138,16 +119,11 @@ void MulOp<DEVICE_TYPE_GPU>(GpuSparseMatrix& out,
bool bTrans,
bool cTrans) {
CHECK(a.useGpu_ && b.useGpu_) << "matrix device type not match";
real* aData = const_cast<real*>(a.getData());
real* bData = const_cast<real*>(b.getData());
hl_sparse_matrix_s outData = out.sMatrix_.get();
hl_sparse_matrix_mul(aData,
hl_sparse_matrix_mul(const_cast<real*>(a.getData()),
aTrans ? HPPL_OP_T : HPPL_OP_N,
bData,
const_cast<real*>(b.getData()),
bTrans ? HPPL_OP_T : HPPL_OP_N,
outData,
out.sMatrix_.get(),
out.getHeight(),
out.getWidth(),
!bTrans ? b.getHeight() : b.getWidth(),

2
paddle/function/MulOpTest.cpp
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@ -13,8 +13,6 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include <gtest/gtest.h>
/// todo(tianbing), delete
#include <iostream>
#include "FunctionTest.h"
#include "paddle/math/Matrix.h"
#include "paddle/math/SparseMatrix.h"

4
paddle/function/TensorType.h
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@ -31,6 +31,10 @@ enum DeviceType {
DEVICE_TYPE_GPU = 2
};
enum SparseDataType { T_NO_VALUE = 0, T_FLOAT_VALUE = 1 };
enum SparseDataFormat { T_SPARSE_CSR = 0, T_SPARSE_CSC = 1 };
inline int sizeOfValuType(ValueType valueType) {
if (valueType == VALUE_TYPE_INT32) {
return 4;

2
paddle/math/Matrix.h
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@ -31,6 +31,7 @@ limitations under the License. */
namespace paddle {
/// TODO(tianbing), move to paddle/function/TensorType.h
enum SparseValueType { NO_VALUE = 0, FLOAT_VALUE = 1 };
/**
@ -56,6 +57,7 @@ enum SparseValueType { NO_VALUE = 0, FLOAT_VALUE = 1 };
* value [1, 1, 2, 2, 5]
* @endcode
*/
/// TODO(tianbing), move to paddle/function/TensorType.h
enum SparseFormat { SPARSE_CSR = 0, SPARSE_CSC = 1 };
class Matrix;

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