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Reworked the TensorExecutor code to support in place evaluation.

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This commit is contained in:
Benoit Steiner 2014-08-13 08:22:05 -07:00
parent 647622281e
commit 439feca139
1 changed files with 58 additions and 48 deletions
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42
unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h
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@ -11,7 +11,7 @@
#define EIGEN_CXX11_TENSOR_TENSOR_EXECUTOR_H #define EIGEN_CXX11_TENSOR_TENSOR_EXECUTOR_H
#ifdef EIGEN_USE_THREADS #ifdef EIGEN_USE_THREADS
#include <future> #include <future>"
#endif #endif
namespace Eigen { namespace Eigen {
@ -28,34 +28,38 @@ namespace internal {
// Default strategy: the expression is evaluated with a single cpu thread. // Default strategy: the expression is evaluated with a single cpu thread.
template<typename Expression, typename Device = DefaultDevice, bool Vectorizable = TensorEvaluator<Expression, Device>::PacketAccess> template<typename Expression, typename Device = DefaultDevice, bool Vectorizable = TensorEvaluator<Expression, Device>::PacketAccess>
struct TensorExecutor class TensorExecutor
{ {
public:
typedef typename Expression::Index Index; typedef typename Expression::Index Index;
EIGEN_DEVICE_FUNC EIGEN_DEVICE_FUNC
static inline void run(const Expression& expr, const Device& device = Device()) static inline void run(const Expression& expr, const Device& device = Device())
{ {
TensorEvaluator<Expression, Device> evaluator(expr, device); TensorEvaluator<Expression, Device> evaluator(expr, device);
evaluator.evalSubExprsIfNeeded(); const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL);
if (needs_assign)
{
const Index size = evaluator.dimensions().TotalSize(); const Index size = evaluator.dimensions().TotalSize();
for (Index i = 0; i < size; ++i) { for (Index i = 0; i < size; ++i) {
evaluator.evalScalar(i); evaluator.evalScalar(i);
} }
}
evaluator.cleanup(); evaluator.cleanup();
} }
}; };
template<typename Expression> template<typename Expression>
struct TensorExecutor<Expression, DefaultDevice, true> class TensorExecutor<Expression, DefaultDevice, true>
{ {
public:
typedef typename Expression::Index Index; typedef typename Expression::Index Index;
static inline void run(const Expression& expr, const DefaultDevice& device = DefaultDevice()) static inline void run(const Expression& expr, const DefaultDevice& device = DefaultDevice())
{ {
TensorEvaluator<Expression, DefaultDevice> evaluator(expr, device); TensorEvaluator<Expression, DefaultDevice> evaluator(expr, device);
evaluator.evalSubExprsIfNeeded(); const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL);
if (needs_assign)
{
const Index size = evaluator.dimensions().TotalSize(); const Index size = evaluator.dimensions().TotalSize();
static const int PacketSize = unpacket_traits<typename TensorEvaluator<Expression, DefaultDevice>::PacketReturnType>::size; static const int PacketSize = unpacket_traits<typename TensorEvaluator<Expression, DefaultDevice>::PacketReturnType>::size;
const int VectorizedSize = (size / PacketSize) * PacketSize; const int VectorizedSize = (size / PacketSize) * PacketSize;
@ -66,7 +70,7 @@ struct TensorExecutor<Expression, DefaultDevice, true>
for (Index i = VectorizedSize; i < size; ++i) { for (Index i = VectorizedSize; i < size; ++i) {
evaluator.evalScalar(i); evaluator.evalScalar(i);
} }
}
evaluator.cleanup(); evaluator.cleanup();
} }
}; };
@ -107,15 +111,17 @@ struct EvalRange<Evaluator, Index, true> {
}; };
template<typename Expression, bool Vectorizable> template<typename Expression, bool Vectorizable>
struct TensorExecutor<Expression, ThreadPoolDevice, Vectorizable> class TensorExecutor<Expression, ThreadPoolDevice, Vectorizable>
{ {
public:
typedef typename Expression::Index Index; typedef typename Expression::Index Index;
static inline void run(const Expression& expr, const ThreadPoolDevice& device) static inline void run(const Expression& expr, const ThreadPoolDevice& device)
{ {
typedef TensorEvaluator<Expression, ThreadPoolDevice> Evaluator; typedef TensorEvaluator<Expression, ThreadPoolDevice> Evaluator;
Evaluator evaluator(expr, device); Evaluator evaluator(expr, device);
evaluator.evalSubExprsIfNeeded(); const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL);
if (needs_assign)
{
const Index size = evaluator.dimensions().TotalSize(); const Index size = evaluator.dimensions().TotalSize();
static const int PacketSize = Vectorizable ? unpacket_traits<typename Evaluator::PacketReturnType>::size : 1; static const int PacketSize = Vectorizable ? unpacket_traits<typename Evaluator::PacketReturnType>::size : 1;
@ -138,7 +144,7 @@ struct TensorExecutor<Expression, ThreadPoolDevice, Vectorizable>
if (numblocks * blocksize < size) { if (numblocks * blocksize < size) {
EvalRange<Evaluator, Index>::run(&evaluator, numblocks * blocksize, size); EvalRange<Evaluator, Index>::run(&evaluator, numblocks * blocksize, size);
} }
}
evaluator.cleanup(); evaluator.cleanup();
} }
}; };
@ -157,19 +163,23 @@ __global__ void EigenMetaKernel(Evaluator eval, unsigned int size) {
} }
template<typename Expression, bool Vectorizable> template<typename Expression, bool Vectorizable>
struct TensorExecutor<Expression, GpuDevice, Vectorizable> class TensorExecutor<Expression, GpuDevice, Vectorizable>
{ {
public:
typedef typename Expression::Index Index; typedef typename Expression::Index Index;
static inline void run(const Expression& expr, const GpuDevice& device) static inline void run(const Expression& expr, const GpuDevice& device)
{ {
TensorEvaluator<Expression, GpuDevice> evaluator(expr, device); TensorEvaluator<Expression, GpuDevice> evaluator(expr, device);
evaluator.evalSubExprsIfNeeded(); const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL);
if (needs_assign)
{
const int num_blocks = getNumCudaMultiProcessors() * maxCudaThreadsPerMultiProcessor() / maxCudaThreadsPerBlock(); const int num_blocks = getNumCudaMultiProcessors() * maxCudaThreadsPerMultiProcessor() / maxCudaThreadsPerBlock();
const int block_size = maxCudaThreadsPerBlock(); const int block_size = maxCudaThreadsPerBlock();
const Index size = evaluator.dimensions().TotalSize(); const Index size = evaluator.dimensions().TotalSize();
EigenMetaKernel<TensorEvaluator<Expression, GpuDevice> > <<<num_blocks, block_size, 0, device.stream()>>>(evaluator, size); EigenMetaKernel<TensorEvaluator<Expression, GpuDevice> > <<<num_blocks, block_size, 0, device.stream()>>>(evaluator, size);
eigen_assert(cudaGetLastError() == cudaSuccess); assert(cudaGetLastError() == cudaSuccess);
}
evaluator.cleanup(); evaluator.cleanup();
} }
}; };