Program Listing for File tensor_operators.h

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#pragma once

#include "common/definitions.h"
#include "tensors/allocator.h"
#include "tensors/tensor.h"

#include "tensors/dispatch.h"

#include "functional/shape.h"
#include "functional/tensor.h"
#include "functional/tmp.h"

#ifdef CUDA_FOUND
#include "tensors/gpu/add.h"
#include "tensors/gpu/algorithm.h"
#include "tensors/gpu/element.h"
#include "tensors/gpu/prod.h"
#endif

#include "tensors/cpu/add.h"
#include "tensors/cpu/element.h"

#include <algorithm>

namespace marian {

template <typename InIt, typename OutIt>
void copy(Ptr<Backend>& backend, const InIt beg, const InIt end, OutIt it) {
#ifdef CUDA_FOUND
  if(backend->getDeviceId().type == DeviceType::gpu)
    gpu::copy(backend, beg, end, it);
  else
    std::copy(beg, end, it);
#else
    backend;
    std::copy(beg, end, it);
#endif
}

DISPATCH2(CopyCast, marian::Tensor, const marian::Tensor);
DISPATCH2(AddCast, marian::Tensor, const marian::Tensor);
DISPATCH4(IsNaN, const Tensor, Ptr<Allocator>, bool&, bool&);

#ifdef CUDA_FOUND
namespace gpu {
bool SanitizeGradient(marian::Tensor in, Ptr<Allocator> allocator, bool pruneNaN, bool clipInf);
}
#endif

namespace cpu {
bool SanitizeGradient(marian::Tensor in, Ptr<Allocator> allocator, bool pruneNaN, bool clipInf);
}

static inline bool SanitizeGradient(marian::Tensor in, Ptr<Allocator> allocator, bool pruneNaN, bool clipInf) {
#ifdef CUDA_FOUND
  if(in->getBackend()->getDeviceId().type == DeviceType::gpu)
    return gpu::SanitizeGradient(in, allocator, pruneNaN, clipInf);
  else
#endif
    return cpu::SanitizeGradient(in, allocator, pruneNaN, clipInf);
}

template <class Functor, class... Tensors>
void Element(Functor functor, marian::Tensor out, Tensors... tensors) {
#ifdef CUDA_FOUND
  if(out->getBackend()->getDeviceId().type == DeviceType::gpu)
    gpu::Element(functor, out, tensors...);
  else
#endif
    cpu::Element(functor, out, tensors...);
}

template <class Functor, class... Tensors>
void Add(Functor functor, float scale, marian::Tensor out, Tensors... tensors) {
#ifdef CUDA_FOUND
  if(out->getBackend()->getDeviceId().type == DeviceType::gpu)
    gpu::Add(functor, scale, out, tensors...);
  else
#endif
    cpu::Aggregate(functor, /*aggInit=*/0.0f, functional::_1 + functional::_2, scale, out, tensors...);
}

template <class Functor, class... Tensors>
void Add(Functor functor, marian::Tensor out, Tensors... tensors) {
  Add(functor, /*scale=*/1.f, out, tensors...);
}

template <class Functor, class AggFunctor, class... Tensors>
void Aggregate(Functor functor, float aggInit, AggFunctor aggFunctor, marian::Tensor out, Tensors... tensors) {
#ifdef CUDA_FOUND
  if(out->getBackend()->getDeviceId().type == DeviceType::gpu)
    gpu::Aggregate(functor, aggInit, aggFunctor, 1.0f, out, tensors...);
  else
#endif
    cpu::Aggregate(functor, aggInit, aggFunctor, 1.0f, out, tensors...);
}

template <class Functor, class... Tensors>
void Reduce(Functor functor,
            float scale,
            marian::Tensor out,
            Tensors... tensors) {
  out->set(0.f);
  Add(functor, scale, out, tensors...);
}

template <class Functor, class... Tensors>
void Reduce(Functor functor, marian::Tensor out, Tensors... tensors) {
  out->set(0.f);
  Add(functor, out, tensors...);
}

template <class Functor, class AggFunctor, class... Tensors>
void Reduce(Functor functor, AggFunctor aggFunctor, float aggInit,
            marian::Tensor out,
            Tensors... tensors) {
  out->set(aggInit);
  Aggregate(functor, aggInit, aggFunctor, out, tensors...);
}

// clang-format off
DISPATCH7(Prod, marian::Tensor, const marian::Tensor&, const marian::Tensor&, bool, bool, float, float)
DISPATCH8(Prod, marian::Tensor, const marian::Tensor&, const marian::Tensor&, bool, bool, float, float, Type) // overloading since we want the default to for computeType be C->type() which difficult otherwise.

DISPATCH8(ProdBatched, marian::Tensor, Ptr<Allocator>, const marian::Tensor, const marian::Tensor, bool, bool, float, float)
DISPATCH8(ProdBatchedLegacy, marian::Tensor, Ptr<Allocator>, const marian::Tensor, const marian::Tensor, bool, bool, float, float)
DISPATCH9(CSRProd, marian::Tensor, Ptr<Allocator>, const marian::Tensor&, const marian::Tensor&, const marian::Tensor&, const marian::Tensor&, bool, bool, float)

DISPATCH10(Affine, marian::Tensor, Ptr<Allocator>, const marian::Tensor&, const marian::Tensor&, const marian::Tensor&, bool, bool, float, float, bool)

DISPATCH2(Softmax, marian::Tensor, marian::Tensor)
DISPATCH3(SoftmaxGrad, marian::Tensor, marian::Tensor, marian::Tensor)

DISPATCH2(LogSoftmax, marian::Tensor, marian::Tensor)
DISPATCH3(LogSoftmaxGrad, marian::Tensor, marian::Tensor, marian::Tensor)

DISPATCH4(CrossEntropyPick, marian::Tensor, marian::Tensor, marian::Tensor, float)
DISPATCH5(CrossEntropyPickBackward, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor, float)

DISPATCH3(TransposeND, marian::Tensor, marian::Tensor, const std::vector<int>&)
DISPATCH3(TransposeNDGrad, marian::Tensor, marian::Tensor, const std::vector<int>&)

DISPATCH5(Shift, marian::Tensor, marian::Tensor, marian::Shape, float, bool)
DISPATCH4(ShiftGrad, marian::Tensor, marian::Tensor, marian::Shape, bool)

DISPATCH3(Concatenate, marian::Tensor, const std::vector<marian::Tensor>&, int)

// clang-format on

// Bernoulli(tensor, 0.5f, 2.f, -1.f) generates a tensor composed of 50% of 1 and 50% of -1.
static inline void Bernoulli(Tensor resultTensor, float keepProb, float scale = 1.f, float shift = 0.f) {
  // in-place uniform distribution
  auto rnd = resultTensor->getBackend()->getRandomGenerator();
  rnd->uniform(resultTensor, 0.f, 1.f); // temporarily mis-use this to hold the random numbers
  using namespace functional;
  Element(_1 = (_1 < keepProb) * scale + shift, resultTensor);
}

static inline void Dropout(Tensor tensor, float dropProb) {
  float keepProb = 1.f - dropProb;
  float scale = 1.f / keepProb;
  Bernoulli(tensor, keepProb, scale, /*shift=*/0.f);
}

DISPATCH2(SinusoidalPositionEmbeddings, marian::Tensor, int);

#ifdef CUDA_FOUND
namespace gpu {
void Deconcatenate(std::vector<marian::Tensor>& outputs,
                   const marian::Tensor in,
                   int ax);
}
#endif

namespace cpu {
void Deconcatenate(std::vector<marian::Tensor>& outputs,
                   const marian::Tensor in,
                   int ax);
}

static inline void Deconcatenate(std::vector<marian::Tensor>& outputs,
                                 const marian::Tensor in,
                                 int ax) {
#ifdef CUDA_FOUND
  if(in->getBackend()->getDeviceId().type == DeviceType::gpu)
    gpu::Deconcatenate(outputs, in, ax);
  else
#endif
    cpu::Deconcatenate(outputs, in, ax);
}

// clang-format off
DISPATCH5(LayerNormalization, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor, float)

#ifdef CUDA_FOUND
namespace gpu {
void LayerNormalizationGrad(Ptr<Allocator> allocator,
                            Tensor gradX,
                            Tensor gradGamma,
                            Tensor gradBeta,
                            Tensor adj,
                            Tensor y,
                            Tensor x,
                            Tensor gamma,
                            Tensor beta,
                            float eps);
}
#endif

namespace cpu {
void LayerNormalizationGrad(Tensor gradX,
                            Tensor gradGamma,
                            Tensor gradBeta,
                            Tensor adj,
                            Tensor y,
                            Tensor x,
                            Tensor gamma,
                            Tensor beta,
                            float eps);
}

static inline void LayerNormalizationGrad(
                            Ptr<Allocator> allocator,
                            Tensor gradX,
                            Tensor gradGamma,
                            Tensor gradBeta,
                            Tensor adj,
                            Tensor y,
                            Tensor x,
                            Tensor gamma,
                            Tensor beta,
                            float eps) {
#ifdef CUDA_FOUND
  if(gradX->getBackend()->getDeviceId().type == DeviceType::gpu)
    gpu::LayerNormalizationGrad(allocator, gradX, gradGamma, gradBeta, adj, y, x, gamma, beta, eps);
  else
#endif
    cpu::LayerNormalizationGrad(gradX, gradGamma, gradBeta, adj, y, x, gamma, beta, eps);
}

// clang-format off
DISPATCH5(RMSNormalization, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor, float)

#ifdef CUDA_FOUND
namespace gpu {
void RMSNormalizationGrad(Ptr<Allocator> allocator,
                          Tensor gradX,
                          Tensor gradGamma,
                          Tensor gradBeta,
                          Tensor adj,
                          Tensor y,
                          Tensor x,
                          Tensor gamma,
                          Tensor beta,
                          float eps);
}
#endif

namespace cpu {
void RMSNormalizationGrad(Tensor gradX,
                          Tensor gradGamma,
                          Tensor gradBeta,
                          Tensor adj,
                          Tensor y,
                          Tensor x,
                          Tensor gamma,
                          Tensor beta,
                          float eps);
}

static inline void RMSNormalizationGrad(
                            Ptr<Allocator> allocator,
                            Tensor gradX,
                            Tensor gradGamma,
                            Tensor gradBeta,
                            Tensor adj,
                            Tensor y,
                            Tensor x,
                            Tensor gamma,
                            Tensor beta,
                            float eps) {
#ifdef CUDA_FOUND
  if(gradX->getBackend()->getDeviceId().type == DeviceType::gpu)
    gpu::RMSNormalizationGrad(allocator, gradX, gradGamma, gradBeta, adj, y, x, gamma, beta, eps);
  else
#endif
    cpu::RMSNormalizationGrad(gradX, gradGamma, gradBeta, adj, y, x, gamma, beta, eps);
}

DISPATCH4(HighwayForward, marian::Tensor, const marian::Tensor, const marian::Tensor, const marian::Tensor)
DISPATCH7(HighwayBackward, marian::Tensor, marian::Tensor, marian::Tensor, const marian::Tensor, const marian::Tensor, const marian::Tensor, const marian::Tensor)

DISPATCH3(CopyRows, marian::Tensor, const marian::Tensor, const marian::Tensor)
DISPATCH3(PasteRows, marian::Tensor, const marian::Tensor, const marian::Tensor)

DISPATCH3(CopyCols, marian::Tensor, const marian::Tensor, const marian::Tensor)
DISPATCH3(PasteCols, marian::Tensor, const marian::Tensor, const marian::Tensor)

DISPATCH4(Select, marian::Tensor, const marian::Tensor, const marian::Tensor, int)

#ifdef CUDA_FOUND
namespace gpu {
  template <bool add>
  void Insert(Tensor out, const Tensor in, const Tensor indices, int axis);
}
#endif

namespace cpu {
  template <bool add>
  void Insert(Tensor out, const Tensor in, const Tensor indices, int axis);
}

template <bool add>
static inline void Insert(Tensor out, const Tensor in, const Tensor indices, int axis) {
#ifdef CUDA_FOUND
  if(out->getBackend()->getDeviceId().type == DeviceType::gpu)
    gpu::Insert<add>(out, in, indices, axis);
  else
#endif
    cpu::Insert<add>(out, in, indices, axis);
}

DISPATCH7(TopK, marian::Tensor, marian::Tensor, Ptr<Allocator>, const marian::Tensor, int, int, bool);

DISPATCH2(LSTMCellForward, marian::Tensor, std::vector<marian::Tensor>)
DISPATCH2(LSTMOutputForward, marian::Tensor, std::vector<marian::Tensor>);
// clang-format on

#ifdef CUDA_FOUND
namespace gpu {
void LSTMCellBackward(std::vector<marian::Tensor> outputs,
                      std::vector<marian::Tensor> inputs,
                      marian::Tensor adj);
}
#endif

namespace cpu {
void LSTMCellBackward(std::vector<marian::Tensor> outputs,
                      std::vector<marian::Tensor> inputs,
                      marian::Tensor adj);
}

static inline void LSTMCellBackward(std::vector<marian::Tensor> outputs,
                                    std::vector<marian::Tensor> inputs,
                                    marian::Tensor adj) {
#ifdef CUDA_FOUND
  if(adj->getBackend()->getDeviceId().type == DeviceType::gpu)
    gpu::LSTMCellBackward(outputs, inputs, adj);
  else
#endif
    cpu::LSTMCellBackward(outputs, inputs, adj);
}

#ifdef CUDA_FOUND
namespace gpu {
void LSTMOutputBackward(std::vector<marian::Tensor> outputs,
                        std::vector<marian::Tensor> inputs,
                        marian::Tensor adj);
}
#endif

namespace cpu {
void LSTMOutputBackward(std::vector<marian::Tensor> outputs,
                        std::vector<marian::Tensor> inputs,
                        marian::Tensor adj);
}

static inline void LSTMOutputBackward(std::vector<marian::Tensor> outputs,
                                      std::vector<marian::Tensor> inputs,
                                      marian::Tensor adj) {
#ifdef CUDA_FOUND
  if(adj->getBackend()->getDeviceId().type == DeviceType::gpu)
    gpu::LSTMOutputBackward(outputs, inputs, adj);
  else
#endif
    cpu::LSTMOutputBackward(outputs, inputs, adj);
}

DISPATCH3(GRUFastForward, marian::Tensor, std::vector<marian::Tensor>, bool)

#ifdef CUDA_FOUND
namespace gpu {
void GRUFastBackward(Ptr<Allocator> allocator,
                     std::vector<marian::Tensor> outputs,
                     std::vector<marian::Tensor> inputs,
                     marian::Tensor adj,
                     bool final);
}
#endif

namespace cpu {
void GRUFastBackward(Ptr<Allocator> allocator,
                     std::vector<marian::Tensor> outputs,
                     std::vector<marian::Tensor> inputs,
                     marian::Tensor adj,
                     bool final);
}

static inline void GRUFastBackward(Ptr<Allocator> allocator,
                                   std::vector<marian::Tensor> outputs,
                                   std::vector<marian::Tensor> inputs,
                                   marian::Tensor adj,
                                   bool final = false) {
#ifdef CUDA_FOUND
  if(adj->getBackend()->getDeviceId().type == DeviceType::gpu)
    gpu::GRUFastBackward(allocator, outputs, inputs, adj, final);
  else
#endif
    cpu::GRUFastBackward(allocator, outputs, inputs, adj, final);
}

// clang-format off
DISPATCH4(Att, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor)
DISPATCH7(AttBack, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor)
// clang-format on

#ifdef CUDA_FOUND
namespace gpu {
float L2Norm(marian::Tensor in, Ptr<Allocator> allocator);
}
#endif

namespace cpu {
float L2Norm(marian::Tensor in, Ptr<Allocator> allocator);
}

static inline float L2Norm(marian::Tensor in, Ptr<Allocator> allocator) {
#ifdef CUDA_FOUND
  if(in->getBackend()->getDeviceId().type == DeviceType::gpu)
    return gpu::L2Norm(in, allocator);
  else
#endif
    return cpu::L2Norm(in, allocator);
}

// clang-format off
DISPATCH5(PoolingWithMaskingForward, marian::Tensor, marian::Tensor, marian::Tensor, int, bool)
DISPATCH6(PoolingWithMaskingBackward, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor, int, bool)
// clang-format on
}  // namespace marian