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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_CXX11_TENSOR_TENSOR_EXECUTOR_H
#define EIGEN_CXX11_TENSOR_TENSOR_EXECUTOR_H
namespace Eigen {
/** \class TensorExecutor
* \ingroup CXX11_Tensor_Module
*
* \brief The tensor executor class.
*
* This class is responsible for launch the evaluation of the expression on
* the specified computing device.
*/
namespace internal {
// Default strategy: the expression is evaluated with a single cpu thread.
template<typename Expression, typename Device, bool Vectorizable>
class TensorExecutor
{
public:
typedef typename Expression::Index Index;
EIGEN_DEVICE_FUNC
static inline void run(const Expression& expr, const Device& device = Device())
{
TensorEvaluator<Expression, Device> evaluator(expr, device);
const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL);
if (needs_assign)
{
const Index size = array_prod(evaluator.dimensions());
for (Index i = 0; i < size; ++i) {
evaluator.evalScalar(i);
}
}
evaluator.cleanup();
}
};
template<typename Expression>
class TensorExecutor<Expression, DefaultDevice, true>
{
public:
typedef typename Expression::Index Index;
EIGEN_DEVICE_FUNC
static inline void run(const Expression& expr, const DefaultDevice& device = DefaultDevice())
{
TensorEvaluator<Expression, DefaultDevice> evaluator(expr, device);
const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL);
if (needs_assign)
{
const Index size = array_prod(evaluator.dimensions());
const int PacketSize = unpacket_traits<typename TensorEvaluator<Expression, DefaultDevice>::PacketReturnType>::size;
const Index VectorizedSize = (size / PacketSize) * PacketSize;
for (Index i = 0; i < VectorizedSize; i += PacketSize) {
evaluator.evalPacket(i);
}
for (Index i = VectorizedSize; i < size; ++i) {
evaluator.evalScalar(i);
}
}
evaluator.cleanup();
}
};
// Multicore strategy: the index space is partitioned and each partition is executed on a single core
#ifdef EIGEN_USE_THREADS
template <typename Evaluator, typename Index, bool Vectorizable>
struct EvalRange {
static void run(Evaluator evaluator, const Index first, const Index last) {
eigen_assert(last > first);
for (Index i = first; i < last; ++i) {
evaluator.evalScalar(i);
}
}
};
template <typename Evaluator, typename Index>
struct EvalRange<Evaluator, Index, true> {
static void run(Evaluator evaluator, const Index first, const Index last) {
eigen_assert(last > first);
Index i = first;
static const int PacketSize = unpacket_traits<typename Evaluator::PacketReturnType>::size;
if (last - first >= PacketSize) {
eigen_assert(first % PacketSize == 0);
Index lastPacket = last - (last % PacketSize);
for (; i < lastPacket; i += PacketSize) {
evaluator.evalPacket(i);
}
}
for (; i < last; ++i) {
evaluator.evalScalar(i);
}
}
};
template<typename Expression, bool Vectorizable>
class TensorExecutor<Expression, ThreadPoolDevice, Vectorizable>
{
public:
typedef typename Expression::Index Index;
static inline void run(const Expression& expr, const ThreadPoolDevice& device)
{
typedef TensorEvaluator<Expression, ThreadPoolDevice> Evaluator;
Evaluator evaluator(expr, device);
const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL);
if (needs_assign)
{
const Index size = array_prod(evaluator.dimensions());
static const int PacketSize = Vectorizable ? unpacket_traits<typename Evaluator::PacketReturnType>::size : 1;
int blocksz = std::ceil<int>(static_cast<float>(size)/device.numThreads()) + PacketSize - 1;
const Index blocksize = numext::maxi<Index>(PacketSize, (blocksz - (blocksz % PacketSize)));
const Index numblocks = size / blocksize;
std::vector<Notification*> results;
results.reserve(numblocks);
for (int i = 0; i < numblocks; ++i) {
results.push_back(device.enqueue(&EvalRange<Evaluator, Index, Vectorizable>::run, evaluator, i*blocksize, (i+1)*blocksize));
}
if (numblocks * blocksize < size) {
EvalRange<Evaluator, Index, Vectorizable>::run(evaluator, numblocks * blocksize, size);
}
for (int i = 0; i < numblocks; ++i) {
wait_until_ready(results[i]);
delete results[i];
}
}
evaluator.cleanup();
}
};
#endif
// GPU: the evaluation of the expression is offloaded to a GPU.
#if defined(EIGEN_USE_GPU)
template <typename Expression>
class TensorExecutor<Expression, GpuDevice, false> {
public:
typedef typename Expression::Index Index;
static void run(const Expression& expr, const GpuDevice& device);
};
template <typename Expression>
class TensorExecutor<Expression, GpuDevice, true> {
public:
typedef typename Expression::Index Index;
static void run(const Expression& expr, const GpuDevice& device);
};
#if defined(__CUDACC__)
template <typename Evaluator, typename Index>
__global__ void
__launch_bounds__(1024)
EigenMetaKernel_NonVectorizable(Evaluator memcopied_eval, Index size) {
// Cuda memcopies the kernel arguments. That's fine for POD, but for more
// complex types such as evaluators we should really conform to the C++
// standard and call a proper copy constructor.
Evaluator eval(memcopied_eval);
const Index first_index = blockIdx.x * blockDim.x + threadIdx.x;
const Index step_size = blockDim.x * gridDim.x;
// Use the scalar path
for (Index i = first_index; i < size; i += step_size) {
eval.evalScalar(i);
}
}
template <typename Evaluator, typename Index>
__global__ void
__launch_bounds__(1024)
EigenMetaKernel_Vectorizable(Evaluator memcopied_eval, Index size) {
// Cuda memcopies the kernel arguments. That's fine for POD, but for more
// complex types such as evaluators we should really conform to the C++
// standard and call a proper copy constructor.
Evaluator eval(memcopied_eval);
const Index first_index = blockIdx.x * blockDim.x + threadIdx.x;
const Index step_size = blockDim.x * gridDim.x;
// Use the vector path
const Index PacketSize = unpacket_traits<typename Evaluator::PacketReturnType>::size;
const Index vectorized_step_size = step_size * PacketSize;
const Index vectorized_size = (size / PacketSize) * PacketSize;
for (Index i = first_index * PacketSize; i < vectorized_size;
i += vectorized_step_size) {
eval.evalPacket(i);
}
for (Index i = vectorized_size + first_index; i < size; i += step_size) {
eval.evalScalar(i);
}
}
/*static*/
template <typename Expression>
inline void TensorExecutor<Expression, GpuDevice, false>::run(const Expression& expr, const GpuDevice& device)
{
TensorEvaluator<Expression, GpuDevice> evaluator(expr, device);
const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL);
if (needs_assign)
{
const int block_size = device.maxCudaThreadsPerBlock();
const int max_blocks = device.getNumCudaMultiProcessors() * device.maxCudaThreadsPerMultiProcessor() / block_size;
const Index size = array_prod(evaluator.dimensions());
// Create a least one block to ensure we won't crash if we're called with tensors of size 0.
const int num_blocks = numext::maxi<int>(numext::mini<int>(max_blocks, (size + block_size - 1) / block_size), 1);
LAUNCH_CUDA_KERNEL((EigenMetaKernel_NonVectorizable<TensorEvaluator<Expression, GpuDevice>, Index>), num_blocks, block_size, 0, device, evaluator, size);
}
evaluator.cleanup();
}
/*static*/
template<typename Expression>
inline void TensorExecutor<Expression, GpuDevice, true>::run(const Expression& expr, const GpuDevice& device)
{
TensorEvaluator<Expression, GpuDevice> evaluator(expr, device);
const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL);
if (needs_assign)
{
const int block_size = device.maxCudaThreadsPerBlock();
const int max_blocks = device.getNumCudaMultiProcessors() * device.maxCudaThreadsPerMultiProcessor() / block_size;
const Index size = array_prod(evaluator.dimensions());
// Create a least one block to ensure we won't crash if we're called with tensors of size 0.
const int num_blocks = numext::maxi<int>(numext::mini<int>(max_blocks, (size + block_size - 1) / block_size), 1);
LAUNCH_CUDA_KERNEL((EigenMetaKernel_Vectorizable<TensorEvaluator<Expression, GpuDevice>, Index>), num_blocks, block_size, 0, device, evaluator, size);
}
evaluator.cleanup();
}
#endif // __CUDACC__
#endif // EIGEN_USE_GPU
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_EXECUTOR_H
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