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LinalgBackendViennaCL.h
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LinalgBackendViennaCL.h
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/*
* Copyright (c) 2016, Shogun-Toolbox e.V. <shogun-team@shogun-toolbox.org>
* All rights reserved.
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
*
* 1. Redistributions of source code must retain the above copyright notice,
* this list of conditions and the following disclaimer.
*
* 2. Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
*
* 3. Neither the name of the copyright holder nor the names of its
* contributors may be used to endorse or promote products derived from
* this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
* ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
* LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
* CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
* SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
* INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
* CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
* ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*
* Authors: 2016 Pan Deng, Soumyajit De, Heiko Strathmann, Viktor Gal
*/
#ifndef LINALG_BACKEND_VIENNACL_H__
#define LINALG_BACKEND_VIENNACL_H__
#include <shogun/mathematics/linalg/LinalgBackendGPUBase.h>
#include <shogun/mathematics/linalg/LinalgBackendViennaclKernels.h>
#ifdef HAVE_VIENNACL
#include <shogun/mathematics/linalg/GPUMemoryViennaCL.h>
#include <viennacl/linalg/inner_prod.hpp>
#include <viennacl/linalg/prod.hpp>
#include <viennacl/matrix.hpp>
#include <viennacl/vector.hpp>
#if VIENNACL_VERSION >= 10700
#include <viennacl/linalg/sum.hpp>
#endif
namespace shogun
{
/** @brief linalg methods with ViennaCL backend
* implementation of @see LinalgBackendGPUBase
*/
class LinalgBackendViennaCL : public LinalgBackendGPUBase
{
template <typename T>
friend struct GPUMemoryViennaCL;
public:
// clang-format off
#define DEFINE_FOR_ALL_PTYPE(METHODNAME, Container) \
METHODNAME(char, Container); \
METHODNAME(uint8_t, Container); \
METHODNAME(int16_t, Container); \
METHODNAME(uint16_t, Container); \
METHODNAME(int32_t, Container); \
METHODNAME(uint32_t, Container); \
METHODNAME(float32_t, Container); \
METHODNAME(float64_t, Container); \
#define DEFINE_FOR_NON_INTEGER_PTYPE(METHODNAME, Container) \
METHODNAME(float32_t, Container); \
METHODNAME(float64_t, Container); \
/** Implementation of @see LinalgBackendBase::add */
#define BACKEND_GENERIC_IN_PLACE_ADD(Type, Container) \
virtual void add(const Container<Type>& a, const Container<Type>& b, Type alpha, \
Type beta, Container<Type>& result) const \
{ \
add_impl(a, b, alpha, beta, result); \
}
DEFINE_FOR_ALL_PTYPE(BACKEND_GENERIC_IN_PLACE_ADD, SGVector)
DEFINE_FOR_ALL_PTYPE(BACKEND_GENERIC_IN_PLACE_ADD, SGMatrix)
#undef BACKEND_GENERIC_ADD
/** Implementation of @see linalg::cross_entropy */
#define BACKEND_GENERIC_CROSS_ENTROPY(Type, Container) \
virtual Type cross_entropy(const Container<Type>& P, \
const Container<Type>& Q) const \
{ \
return cross_entropy_impl(P, Q); \
}
DEFINE_FOR_NON_INTEGER_PTYPE(BACKEND_GENERIC_CROSS_ENTROPY, SGMatrix)
#undef BACKEND_GENERIC_CROSS_ENTROPY
/** Implementation of @see LinalgBackendBase::dot */
#define BACKEND_GENERIC_DOT(Type, Container) \
virtual Type dot(const Container<Type>& a, const Container<Type>& b) const \
{ \
return dot_impl(a, b); \
}
DEFINE_FOR_ALL_PTYPE(BACKEND_GENERIC_DOT, SGVector)
#undef BACKEND_GENERIC_DOT
/** Implementation of @see LinalgBackendBase::element_prod */
#define BACKEND_GENERIC_IN_PLACE_ELEMENT_PROD(Type, Container) \
virtual void element_prod(const Container<Type>& a, const Container<Type>& b,\
Container<Type>& result) const \
{ \
element_prod_impl(a, b, result); \
}
DEFINE_FOR_ALL_PTYPE(BACKEND_GENERIC_IN_PLACE_ELEMENT_PROD, SGMatrix)
#undef BACKEND_GENERIC_IN_PLACE_ELEMENT_PROD
/** Implementation of @see LinalgBackendBase::logistic */
#define BACKEND_GENERIC_LOGISTIC(Type, Container) \
virtual void logistic(const Container<Type>& a, Container<Type>& result) const \
{ \
logistic_impl(a, result); \
}
DEFINE_FOR_ALL_PTYPE(BACKEND_GENERIC_LOGISTIC, SGMatrix)
#undef BACKEND_GENERIC_LOGISTIC
/** Implementation of @see LinalgBackendBase::matrix_prod */
#define BACKEND_GENERIC_IN_PLACE_MATRIX_PROD(Type, Container) \
virtual void matrix_prod(const SGMatrix<Type>& a, const Container<Type>& b,\
Container<Type>& result, bool transpose_A, bool transpose_B) const \
{ \
matrix_prod_impl(a, b, result, transpose_A, transpose_B); \
}
DEFINE_FOR_NON_INTEGER_PTYPE(BACKEND_GENERIC_IN_PLACE_MATRIX_PROD, SGVector)
DEFINE_FOR_NON_INTEGER_PTYPE(BACKEND_GENERIC_IN_PLACE_MATRIX_PROD, SGMatrix)
#undef BACKEND_GENERIC_IN_PLACE_MATRIX_PROD
/** Implementation of @see LinalgBackendBase::max */
#define BACKEND_GENERIC_MAX(Type, Container) \
virtual Type max(const Container<Type>& a) const \
{ \
return max_impl(a); \
}
DEFINE_FOR_ALL_PTYPE(BACKEND_GENERIC_MAX, SGVector)
DEFINE_FOR_ALL_PTYPE(BACKEND_GENERIC_MAX, SGMatrix)
#undef BACKEND_GENERIC_MAX
/** Implementation of @see LinalgBackendBase::mean */
#define BACKEND_GENERIC_MEAN(Type, Container) \
virtual float64_t mean(const Container<Type>& a) const \
{ \
return mean_impl(a); \
}
DEFINE_FOR_ALL_PTYPE(BACKEND_GENERIC_MEAN, SGVector)
DEFINE_FOR_ALL_PTYPE(BACKEND_GENERIC_MEAN, SGMatrix)
#undef BACKEND_GENERIC_MEAN
/** Implementation of @see linalg::multiply_by_logistic_derivative */
#define BACKEND_GENERIC_MULTIPLY_BY_LOGISTIC_DERIV(Type, Container) \
virtual void multiply_by_logistic_derivative(const Container<Type>& a,\
Container<Type>& result) const \
{ \
multiply_by_logistic_derivative_impl(a, result); \
}
DEFINE_FOR_ALL_PTYPE(BACKEND_GENERIC_MULTIPLY_BY_LOGISTIC_DERIV, SGMatrix)
#undef BACKEND_GENERIC_MULTIPLY_BY_LOGISTIC_DERIV
/** Implementation of @see linalg::multiply_by_rectified_linear_derivative */
#define BACKEND_GENERIC_MULTIPLY_BY_RECTIFIED_LINEAR_DERIV(Type, Container) \
virtual void multiply_by_rectified_linear_derivative(const Container<Type>& a,\
Container<Type>& result) const \
{ \
multiply_by_rectified_linear_derivative_impl(a, result); \
}
DEFINE_FOR_ALL_PTYPE(BACKEND_GENERIC_MULTIPLY_BY_RECTIFIED_LINEAR_DERIV, SGMatrix)
#undef BACKEND_GENERIC_MULTIPLY_BY_RECTIFIED_LINEAR_DERIV
/** Implementation of @see linalg::rectified_linear */
#define BACKEND_GENERIC_RECTIFIED_LINEAR(Type, Container) \
virtual void rectified_linear(const Container<Type>& a, Container<Type>& result) const \
{ \
rectified_linear_impl(a, result); \
}
DEFINE_FOR_ALL_PTYPE(BACKEND_GENERIC_RECTIFIED_LINEAR, SGMatrix)
#undef BACKEND_GENERIC_RECTIFIED_LINEAR
/** Implementation of @see LinalgBackendBase::scale */
#define BACKEND_GENERIC_IN_PLACE_SCALE(Type, Container) \
virtual void scale(Container<Type>& a, Type alpha, Container<Type>& result) const \
{ \
scale_impl(a, result, alpha); \
}
DEFINE_FOR_ALL_PTYPE(BACKEND_GENERIC_IN_PLACE_SCALE, SGVector)
DEFINE_FOR_ALL_PTYPE(BACKEND_GENERIC_IN_PLACE_SCALE, SGMatrix)
#undef BACKEND_GENERIC_IN_PLACE_SCALE
/** Implementation of @see LinalgBackendBase::set_const */
#define BACKEND_GENERIC_SET_CONST(Type, Container) \
virtual void set_const(Container<Type>& a, const Type value) const \
{ \
set_const_impl(a, value); \
}
DEFINE_FOR_ALL_PTYPE(BACKEND_GENERIC_SET_CONST, SGVector)
DEFINE_FOR_ALL_PTYPE(BACKEND_GENERIC_SET_CONST, SGMatrix)
#undef BACKEND_GENERIC_SET_CONST
/** Implementation of @see linalg::softmax */
#define BACKEND_GENERIC_SOFTMAX(Type, Container) \
virtual void softmax(Container<Type>& a) const \
{ \
softmax_impl(a); \
}
DEFINE_FOR_ALL_PTYPE(BACKEND_GENERIC_SOFTMAX, SGMatrix)
#undef BACKEND_GENERIC_SOFTMAX
/** Implementation of @see linalg::squared_error */
#define BACKEND_GENERIC_SQUARED_ERROR(Type, Container) \
virtual Type squared_error(const Container<Type>& P, const Container<Type>& Q) const \
{ \
return squared_error_impl(P, Q); \
}
DEFINE_FOR_NON_INTEGER_PTYPE(BACKEND_GENERIC_SQUARED_ERROR, SGMatrix)
#undef BACKEND_GENERIC_SQUARED_ERROR
/** Implementation of @see LinalgBackendBase::sum */
#define BACKEND_GENERIC_SUM(Type, Container) \
virtual Type sum(const Container<Type>& a, bool no_diag) const \
{ \
return sum_impl(a, no_diag); \
}
DEFINE_FOR_ALL_PTYPE(BACKEND_GENERIC_SUM, SGVector)
DEFINE_FOR_ALL_PTYPE(BACKEND_GENERIC_SUM, SGMatrix)
#undef BACKEND_GENERIC_SUM
/** Implementation of @see LinalgBackendBase::sum_symmetric */
#define BACKEND_GENERIC_SYMMETRIC_SUM(Type, Container) \
virtual Type sum_symmetric(const Container<Type>& a, bool no_diag) const \
{ \
return sum_symmetric_impl(a, no_diag); \
}
DEFINE_FOR_ALL_PTYPE(BACKEND_GENERIC_SYMMETRIC_SUM, SGMatrix)
#undef BACKEND_GENERIC_SYMMETRIC_SUM
/** Implementation of @see LinalgBackendBase::colwise_sum */
#define BACKEND_GENERIC_COLWISE_SUM(Type, Container) \
virtual SGVector<Type> colwise_sum(const Container<Type>& a, bool no_diag) const \
{ \
return colwise_sum_impl(a, no_diag); \
}
DEFINE_FOR_ALL_PTYPE(BACKEND_GENERIC_COLWISE_SUM, SGMatrix)
#undef BACKEND_GENERIC_COLWISE_SUM
/** Implementation of @see LinalgBackendBase::rowwise_sum */
#define BACKEND_GENERIC_ROWWISE_SUM(Type, Container) \
virtual SGVector<Type> rowwise_sum(const Container<Type>& a, bool no_diag) const \
{ \
return rowwise_sum_impl(a, no_diag); \
}
DEFINE_FOR_ALL_PTYPE(BACKEND_GENERIC_ROWWISE_SUM, SGMatrix)
#undef BACKEND_GENERIC_ROWWISE_SUM
/** Implementation of @see LinalgBackendBase::to_gpu */
#define BACKEND_GENERIC_TO_GPU(Type, Container) \
virtual GPUMemoryBase<Type>* to_gpu(const Container<Type>& a) const \
{ \
return to_gpu_impl(a); \
}
DEFINE_FOR_ALL_PTYPE(BACKEND_GENERIC_TO_GPU, SGVector)
DEFINE_FOR_ALL_PTYPE(BACKEND_GENERIC_TO_GPU, SGMatrix)
#undef BACKEND_GENERIC_TO_GPU
/** Implementation of @see LinalgBackendGPUBase::from_gpu */
#define BACKEND_GENERIC_FROM_GPU(Type, Container) \
virtual void from_gpu(const Container<Type>& a, Type* data) const \
{ \
return from_gpu_impl(a, data); \
}
DEFINE_FOR_ALL_PTYPE(BACKEND_GENERIC_FROM_GPU, SGVector)
DEFINE_FOR_ALL_PTYPE(BACKEND_GENERIC_FROM_GPU, SGMatrix)
#undef BACKEND_GENERIC_FROM_GPU
#undef DEFINE_FOR_ALL_PTYPE
// clang-format on
private:
/** Static cast @see GPUMemoryBase class to @see GPUMemoryViennaCL */
template <typename T, template <typename> class Container>
GPUMemoryViennaCL<T>* cast_to_viennacl(const Container<T>& a) const
{
return static_cast<GPUMemoryViennaCL<T>*>(a.gpu_ptr.get());
}
/** ViennaCL vector result = alpha * A + beta * B method */
template <typename T>
void add_impl(
const SGVector<T>& a, const SGVector<T>& b, T alpha, T beta,
SGVector<T>& result) const
{
GPUMemoryViennaCL<T>* a_gpu = cast_to_viennacl(a);
GPUMemoryViennaCL<T>* b_gpu = cast_to_viennacl(b);
GPUMemoryViennaCL<T>* result_gpu = cast_to_viennacl(result);
result_gpu->data_vector(a.size()) =
alpha * a_gpu->data_vector(a.size()) +
beta * b_gpu->data_vector(b.size());
}
/** ViennaCL matrix result = alpha * A + beta * B method */
template <typename T>
void add_impl(
const SGMatrix<T>& a, const SGMatrix<T>& b, T alpha, T beta,
SGMatrix<T>& result) const
{
GPUMemoryViennaCL<T>* a_gpu = cast_to_viennacl(a);
GPUMemoryViennaCL<T>* b_gpu = cast_to_viennacl(b);
GPUMemoryViennaCL<T>* result_gpu = cast_to_viennacl(result);
result_gpu->data_matrix(a.num_rows, a.num_cols) =
alpha * a_gpu->data_matrix(a.num_rows, a.num_cols) +
beta * b_gpu->data_matrix(b.num_rows, b.num_cols);
}
/** ViennaCL cross_entropy method
* The cross entropy is defined as \f$ H(P,Q) = - \sum_{ij}
* P[i,j]log(Q[i,j]) \f$
*/
template <typename T>
T cross_entropy_impl(const SGMatrix<T>& p, const SGMatrix<T>& q) const
{
typedef typename std::aligned_storage<sizeof(T), alignof(T)>::type
aligned_t;
GPUMemoryViennaCL<T>* p_gpu = cast_to_viennacl(p);
GPUMemoryViennaCL<T>* q_gpu = cast_to_viennacl(q);
GPUMemoryViennaCL<T>* result_gpu = new GPUMemoryViennaCL<T>(1);
viennacl::ocl::kernel& kernel = generate_cross_entropy_kernel<T>();
viennacl::ocl::enqueue(
kernel(
p_gpu->data_matrix(p.num_rows, p.num_cols),
cl_int(p.num_rows * p.num_cols), cl_int(p_gpu->m_offset),
q_gpu->data_matrix(q.num_rows, q.num_cols),
cl_int(q_gpu->m_offset), result_gpu->data_vector(1)));
T* result = reinterpret_cast<T*>(SG_MALLOC(aligned_t, 1));
viennacl::backend::memory_read(
*(result_gpu->m_data), result_gpu->m_offset * sizeof(T),
sizeof(T), result);
return result[0];
}
/** ViennaCL vector dot-product method. */
template <typename T>
T dot_impl(const SGVector<T>& a, const SGVector<T>& b) const
{
GPUMemoryViennaCL<T>* a_gpu = cast_to_viennacl(a);
GPUMemoryViennaCL<T>* b_gpu = cast_to_viennacl(b);
return viennacl::linalg::inner_prod(
a_gpu->data_vector(a.size()), b_gpu->data_vector(b.size()));
}
/** ViennaCL matrix in-place elementwise product method */
template <typename T>
void element_prod_impl(
const SGMatrix<T>& a, const SGMatrix<T>& b, SGMatrix<T>& result) const
{
GPUMemoryViennaCL<T>* a_gpu = cast_to_viennacl(a);
GPUMemoryViennaCL<T>* b_gpu = cast_to_viennacl(b);
GPUMemoryViennaCL<T>* result_gpu = cast_to_viennacl(result);
result_gpu->data_matrix(a.num_rows, a.num_cols) =
viennacl::linalg::element_prod(
a_gpu->data_matrix(a.num_rows, a.num_cols),
b_gpu->data_matrix(a.num_rows, a.num_cols));
}
/** ViennaCL logistic method. Calculates f(x) = 1/(1+exp(-x)) */
template <typename T>
void logistic_impl(const SGMatrix<T>& a, SGMatrix<T>& result) const
{
GPUMemoryViennaCL<T>* a_gpu = cast_to_viennacl(a);
GPUMemoryViennaCL<T>* result_gpu = cast_to_viennacl(result);
const std::string operation = "return 1.0/(1+exp(-1*element));";
std::string kernel_name =
"logistic_" + linalg::implementation::ocl::get_type_string<T>();
viennacl::ocl::kernel& kernel = linalg::implementation::ocl::
generate_single_arg_elementwise_kernel<T>(
kernel_name, operation);
kernel.global_work_size(
0, linalg::implementation::ocl::align_to_multiple_1d(
a.num_rows * a.num_cols));
viennacl::ocl::enqueue(
kernel(
a_gpu->data_matrix(a.num_rows, a.num_cols),
cl_int(a.num_rows * a.num_cols), cl_int(a_gpu->m_offset),
result_gpu->data_matrix(a.num_rows, a.num_cols),
cl_int(result_gpu->m_offset)));
result.gpu_ptr = std::shared_ptr<GPUMemoryBase<T>>(
result_gpu->clone_vector(result_gpu, a.num_rows * a.num_cols));
}
/** ViennaCL matrix * vector in-place product method */
template <typename T>
void matrix_prod_impl(
const SGMatrix<T>& a, const SGVector<T>& b, SGVector<T>& result,
bool transpose, bool transpose_B = false) const
{
GPUMemoryViennaCL<T>* a_gpu = cast_to_viennacl(a);
GPUMemoryViennaCL<T>* b_gpu = cast_to_viennacl(b);
GPUMemoryViennaCL<T>* result_gpu = cast_to_viennacl(result);
if (transpose)
result_gpu->data_vector(result.vlen) = viennacl::linalg::prod(
viennacl::trans(a_gpu->data_matrix(a.num_rows, a.num_cols)),
b_gpu->data_vector(b.vlen));
else
result_gpu->data_vector(result.vlen) = viennacl::linalg::prod(
a_gpu->data_matrix(a.num_rows, a.num_cols),
b_gpu->data_vector(b.vlen));
}
/** ViennaCL matrices in-place product method */
template <typename T>
void matrix_prod_impl(
const SGMatrix<T>& a, const SGMatrix<T>& b, SGMatrix<T>& result,
bool transpose_A, bool transpose_B) const
{
GPUMemoryViennaCL<T>* a_gpu = cast_to_viennacl(a);
GPUMemoryViennaCL<T>* b_gpu = cast_to_viennacl(b);
GPUMemoryViennaCL<T>* result_gpu = cast_to_viennacl(result);
if (transpose_A && transpose_B)
result_gpu->data_matrix(result.num_rows, result.num_cols) =
viennacl::linalg::prod(
viennacl::trans(
a_gpu->data_matrix(a.num_rows, a.num_cols)),
viennacl::trans(
b_gpu->data_matrix(b.num_rows, b.num_cols)));
else if (transpose_A)
result_gpu->data_matrix(result.num_rows, result.num_cols) =
viennacl::linalg::prod(
viennacl::trans(
a_gpu->data_matrix(a.num_rows, a.num_cols)),
b_gpu->data_matrix(b.num_rows, b.num_cols));
else if (transpose_B)
result_gpu->data_matrix(result.num_rows, result.num_cols) =
viennacl::linalg::prod(
a_gpu->data_matrix(a.num_rows, a.num_cols),
viennacl::trans(
b_gpu->data_matrix(b.num_rows, b.num_cols)));
else
result_gpu->data_matrix(result.num_rows, result.num_cols) =
viennacl::linalg::prod(
a_gpu->data_matrix(a.num_rows, a.num_cols),
b_gpu->data_matrix(b.num_rows, b.num_cols));
}
/** ViennaCL max method */
template <typename T, template <typename> class Container>
T max_impl(const Container<T>& a) const
{
typedef typename std::aligned_storage<sizeof(T), alignof(T)>::type
aligned_t;
GPUMemoryViennaCL<T>* a_gpu = cast_to_viennacl(a);
GPUMemoryViennaCL<T>* result_gpu = new GPUMemoryViennaCL<T>(1);
viennacl::ocl::kernel& kernel = generate_max_kernel<T>();
viennacl::ocl::enqueue(
kernel(
a_gpu->data_vector(a.size()), cl_int(a.size()),
cl_int(a_gpu->m_offset), result_gpu->data_vector(1)));
T* result = reinterpret_cast<T*>(SG_MALLOC(aligned_t, 1));
viennacl::backend::memory_read(
*(result_gpu->m_data), result_gpu->m_offset * sizeof(T),
sizeof(T), result);
return result[0];
}
/** ViennaCL vectors or matrices mean method */
template <typename T, template <typename> class Container>
float64_t mean_impl(const Container<T>& a) const
{
return sum_impl(a) / float64_t(a.size());
}
/** ViennaCL multiply_by_logistic_derivative method
* Performs the operation C(i,j) = C(i,j) * A(i,j) * (1.0-A(i,j) for all
* i
* and j
*/
template <typename T>
void multiply_by_logistic_derivative_impl(
const SGMatrix<T>& a, SGMatrix<T>& result) const
{
GPUMemoryViennaCL<T>* a_gpu = cast_to_viennacl(a);
GPUMemoryViennaCL<T>* result_gpu = cast_to_viennacl(result);
const std::string operation =
"return element2 * element1*(1.0-element1);";
std::string kernel_name =
"multiply_by_logistic_derivative_" +
linalg::implementation::ocl::get_type_string<T>();
viennacl::ocl::kernel& kernel = linalg::implementation::ocl::
generate_two_arg_elementwise_kernel<T>(kernel_name, operation);
kernel.global_work_size(
0, linalg::implementation::ocl::align_to_multiple_1d(
a.num_rows * a.num_cols));
viennacl::ocl::enqueue(
kernel(
a_gpu->data_matrix(a.num_rows, a.num_cols),
cl_int(a.num_rows * a.num_cols), cl_int(a_gpu->m_offset),
result_gpu->data_matrix(result.num_rows, result.num_cols),
cl_int(result_gpu->m_offset),
result_gpu->data_matrix(result.num_rows, result.num_cols),
cl_int(result_gpu->m_offset)));
result.gpu_ptr = std::shared_ptr<GPUMemoryBase<T>>(
result_gpu->clone_vector(result_gpu, a.num_rows * a.num_cols));
}
/** ViennaCL multiply_by_rectified_linear_derivative method
* Performs the operation C(i,j) = C(i,j) * (A(i,j)!=0) for all i and j
*/
template <typename T>
void multiply_by_rectified_linear_derivative_impl(
const SGMatrix<T>& a, SGMatrix<T>& result) const
{
GPUMemoryViennaCL<T>* a_gpu = cast_to_viennacl(a);
GPUMemoryViennaCL<T>* result_gpu = cast_to_viennacl(result);
const std::string operation = "return element1==0 ? 0 : element2;";
std::string kernel_name =
"multiply_by_rectified_linear_derivative_" +
linalg::implementation::ocl::get_type_string<T>();
viennacl::ocl::kernel& kernel = linalg::implementation::ocl::
generate_two_arg_elementwise_kernel<T>(kernel_name, operation);
kernel.global_work_size(
0, linalg::implementation::ocl::align_to_multiple_1d(
a.num_rows * a.num_cols));
viennacl::ocl::enqueue(
kernel(
a_gpu->data_matrix(a.num_rows, a.num_cols),
cl_int(a.num_rows * a.num_cols), cl_int(a_gpu->m_offset),
result_gpu->data_matrix(result.num_rows, result.num_cols),
cl_int(result_gpu->m_offset),
result_gpu->data_matrix(result.num_rows, result.num_cols),
cl_int(result_gpu->m_offset)));
result.gpu_ptr = std::shared_ptr<GPUMemoryBase<T>>(
result_gpu->clone_vector(result_gpu, a.num_rows * a.num_cols));
}
/** Applies the elementwise rectified linear function f(x) = max(0,x) */
template <typename T>
void
rectified_linear_impl(const SGMatrix<T>& a, SGMatrix<T>& result) const
{
GPUMemoryViennaCL<T>* a_gpu = cast_to_viennacl(a);
GPUMemoryViennaCL<T>* result_gpu = cast_to_viennacl(result);
const std::string operation = "return max((DATATYPE)0,element);";
std::string kernel_name =
"rectified_linear_" +
linalg::implementation::ocl::get_type_string<T>();
viennacl::ocl::kernel& kernel = linalg::implementation::ocl::
generate_single_arg_elementwise_kernel<T>(
kernel_name, operation);
kernel.global_work_size(
0, linalg::implementation::ocl::align_to_multiple_1d(
a.num_rows * a.num_cols));
viennacl::ocl::enqueue(
kernel(
a_gpu->data_matrix(a.num_rows, a.num_cols),
cl_int(a.num_rows * a.num_cols), cl_int(a_gpu->m_offset),
result_gpu->data_matrix(result.num_rows, result.num_cols),
cl_int(result_gpu->m_offset)));
result.gpu_ptr = std::shared_ptr<GPUMemoryBase<T>>(
result_gpu->clone_vector(result_gpu, a.num_rows * a.num_cols));
}
/** ViennaCL vector inplace scale method: result = alpha * A */
template <typename T>
void
scale_impl(const SGVector<T>& a, SGVector<T>& result, T alpha) const
{
GPUMemoryViennaCL<T>* a_gpu = cast_to_viennacl(a);
GPUMemoryViennaCL<T>* result_gpu = cast_to_viennacl(result);
result_gpu->data_vector(a.size()) =
alpha * a_gpu->data_vector(a.size());
}
/** ViennaCL vector inplace scale method: result = alpha * A */
template <typename T>
void
scale_impl(const SGMatrix<T>& a, SGMatrix<T>& result, T alpha) const
{
GPUMemoryViennaCL<T>* a_gpu = cast_to_viennacl(a);
GPUMemoryViennaCL<T>* result_gpu = cast_to_viennacl(result);
result_gpu->data_matrix(a.num_rows, a.num_cols) =
alpha * a_gpu->data_matrix(a.num_rows, a.num_cols);
}
/** Set const to vector or matrix with ViennaCL. */
template <typename T, template <typename> class Container>
void set_const_impl(Container<T>& a, T value) const
{
GPUMemoryViennaCL<T>* a_gpu = cast_to_viennacl(a);
typename GPUMemoryViennaCL<T>::VCLVectorBase vcl_vector =
a_gpu->data_vector(a.size());
viennacl::linalg::vector_assign(vcl_vector, value);
}
/** ViennaCL softmax method */
template <typename T, template <typename> class Container>
void softmax_impl(Container<T>& a) const
{
GPUMemoryViennaCL<T>* a_gpu = cast_to_viennacl(a);
viennacl::ocl::kernel& kernel = generate_softmax_kernel<T>();
kernel.global_work_size(
0,
linalg::implementation::ocl::align_to_multiple_1d(a.num_cols));
viennacl::ocl::enqueue(
kernel(
a_gpu->data_matrix(a.num_rows, a.num_cols),
cl_int(a.num_rows), cl_int(a.num_cols),
cl_int(a_gpu->m_offset)));
a.gpu_ptr = std::shared_ptr<GPUMemoryBase<T>>(
a_gpu->clone_vector(a_gpu, a.num_rows * a.num_cols));
}
/** ViennaCL squared error method
* The squared error is defined as \f$ E(P,Q) = \frac{1}{2} \sum_{ij}
* (P[i,j]-Q[i,j])^2 \f$
*/
template <typename T>
T squared_error_impl(const SGMatrix<T>& p, const SGMatrix<T>& q) const
{
typedef typename std::aligned_storage<sizeof(T), alignof(T)>::type
aligned_t;
GPUMemoryViennaCL<T>* p_gpu = cast_to_viennacl(p);
GPUMemoryViennaCL<T>* q_gpu = cast_to_viennacl(q);
GPUMemoryViennaCL<T>* result_gpu = new GPUMemoryViennaCL<T>(1);
viennacl::ocl::kernel& kernel = generate_squared_error_kernel<T>();
viennacl::ocl::enqueue(
kernel(
p_gpu->data_matrix(p.num_rows, p.num_cols),
cl_int(p.num_rows * p.num_cols), cl_int(p_gpu->m_offset),
q_gpu->data_matrix(q.num_rows, q.num_cols),
cl_int(q_gpu->m_offset), result_gpu->data_vector(1)));
T* result = reinterpret_cast<T*>(SG_MALLOC(aligned_t, 1));
viennacl::backend::memory_read(
*(result_gpu->m_data), result_gpu->m_offset * sizeof(T),
sizeof(T), result);
return result[0];
}
/** ViennaCL matrix sum method. */
template <typename T>
T sum_impl(const SGMatrix<T>& mat, bool no_diag = false) const
{
typedef typename std::aligned_storage<sizeof(T), alignof(T)>::type
aligned_t;
GPUMemoryViennaCL<T>* mat_gpu = cast_to_viennacl(mat);
GPUMemoryViennaCL<T>* result_gpu = new GPUMemoryViennaCL<T>(1);
viennacl::ocl::kernel& kernel = generate_sum_kernel<T>(no_diag);
viennacl::ocl::enqueue(
kernel(
mat_gpu->data_matrix(mat.num_rows, mat.num_cols),
cl_int(mat.num_rows), cl_int(mat.num_cols),
cl_int(mat_gpu->m_offset), result_gpu->data_vector(1)));
T* result;
result = reinterpret_cast<T*>(SG_MALLOC(aligned_t, 1));
viennacl::backend::memory_read(
*(result_gpu->m_data), result_gpu->m_offset * sizeof(T),
sizeof(T), result);
return result[0];
}
/** ViennaCL vector sum method. */
template <typename T>
T sum_impl(const SGVector<T>& vec, bool no_diag = false) const
{
#if VIENNACL_VERSION >= 10700
GPUMemoryViennaCL<T>* vec_gpu = cast_to_viennacl(vec);
return viennacl::linalg::sum(vec_gpu->data_vector(vec.size()));
#else
return sum_impl(SGMatrix<T>(vec));
#endif
}
/** ViennaCL matrix sum method. */
template <typename T>
T sum_symmetric_impl(const SGMatrix<T>& mat, bool no_diag = false) const
{
return sum_impl(mat, no_diag);
}
/** ViennaCL matrix colwise sum method */
template <typename T>
SGVector<T> colwise_sum_impl(const SGMatrix<T>& mat, bool no_diag) const
{
GPUMemoryViennaCL<T>* mat_gpu = cast_to_viennacl(mat);
GPUMemoryViennaCL<T>* result_gpu =
new GPUMemoryViennaCL<T>(mat.num_cols);
viennacl::ocl::kernel& kernel =
generate_colwise_sum_kernel<T>(no_diag);
kernel.global_work_size(
0, linalg::implementation::ocl::align_to_multiple_1d(
mat.num_cols));
viennacl::ocl::enqueue(
kernel(
mat_gpu->data_matrix(mat.num_rows, mat.num_cols),
cl_int(mat.num_rows), cl_int(mat.num_cols),
cl_int(mat_gpu->m_offset),
result_gpu->data_vector(mat.num_cols),
cl_int(result_gpu->m_offset)));
return SGVector<T>(result_gpu, mat.num_cols);
}
/** ViennaCL matrix rowwise sum method */
template <typename T>
SGVector<T> rowwise_sum_impl(const SGMatrix<T>& mat, bool no_diag) const
{
GPUMemoryViennaCL<T>* mat_gpu = cast_to_viennacl(mat);
GPUMemoryViennaCL<T>* result_gpu =
new GPUMemoryViennaCL<T>(mat.num_rows);
viennacl::ocl::kernel& kernel =
generate_rowwise_sum_kernel<T>(no_diag);
kernel.global_work_size(
0, linalg::implementation::ocl::align_to_multiple_1d(
mat.num_rows));
viennacl::ocl::enqueue(
kernel(
mat_gpu->data_matrix(mat.num_rows, mat.num_cols),
cl_int(mat.num_rows), cl_int(mat.num_cols),
cl_int(mat_gpu->m_offset),
result_gpu->data_vector(mat.num_rows),
cl_int(result_gpu->m_offset)));
return SGVector<T>(result_gpu, mat.num_rows);
}
/** Transfer data to GPU with ViennaCL method. */
template <typename T, template <typename> class Container>
GPUMemoryBase<T>* to_gpu_impl(const Container<T>& a) const
{
GPUMemoryViennaCL<T>* gpu_ptr = new GPUMemoryViennaCL<T>();
viennacl::backend::memory_create(
*(gpu_ptr->m_data), sizeof(T) * a.size(), viennacl::context());
viennacl::backend::memory_write(
*(gpu_ptr->m_data), 0, a.size() * sizeof(T), a.data());
return gpu_ptr;
}
/** Fetch data from GPU with ViennaCL method. */
template <typename T, template <typename> class Container>
void from_gpu_impl(const Container<T>& a, T* data) const
{
GPUMemoryViennaCL<T>* gpu_ptr = cast_to_viennacl(a);
viennacl::backend::memory_read(
*(gpu_ptr->m_data), gpu_ptr->m_offset * sizeof(T),
a.size() * sizeof(T), data);
}
// clang-format off
#undef DEFINE_FOR_ALL_PTYPE
#undef DEFINE_FOR_NON_INTEGER_PTYPE
// clang-format on
};
}
#endif // HAVE_VIENNACL
#endif // LINALG_BACKEND_VIENNACL_H__