// 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, bool Tileable>
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, false> {
 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;

      // Give compiler a strong possibility to unroll the loop. But don't insist
      // on unrolling, because if the function is expensive compiler should not
      // unroll the loop at the expense of inlining.
      const Index UnrolledSize = (size / (4 * PacketSize)) * 4 * PacketSize;
      for (Index i = 0; i < UnrolledSize; i += 4*PacketSize) {
        for (Index j = 0; j < 4; j++) {
          evaluator.evalPacket(i + j * PacketSize);
        }
      }
      const Index VectorizedSize = (size / PacketSize) * PacketSize;
      for (Index i = UnrolledSize; i < VectorizedSize; i += PacketSize) {
        evaluator.evalPacket(i);
      }
      for (Index i = VectorizedSize; i < size; ++i) {
        evaluator.evalScalar(i);
      }
    }
    evaluator.cleanup();
  }
};

template <typename Expression, bool Vectorizable>
class TensorExecutor<Expression, DefaultDevice, Vectorizable, true> {
 public:
  typedef typename Expression::Index Index;
  EIGEN_DEVICE_FUNC
  static inline void run(const Expression& expr,
                         const DefaultDevice& device = DefaultDevice()) {
    typedef TensorEvaluator<Expression, DefaultDevice> Evaluator;
    typedef typename traits<Expression>::Scalar Scalar;
    typedef typename traits<Expression>::Index Index;
    const std::size_t NumDims = traits<Expression>::NumDimensions;

    typedef TensorBlockMapper<Index,
                              typename internal::remove_const<Scalar>::type,
                              NumDims, Evaluator::Layout> TensorBlockMapper;
    typedef TensorBlock<Index, typename internal::remove_const<Scalar>::type,
                        NumDims, Evaluator::Layout> TensorBlock;

    Evaluator evaluator(expr, device);
    std::size_t total_size = array_prod(evaluator.dimensions());
    std::size_t cache_size = device.firstLevelCacheSize() / sizeof(Scalar);
    if (total_size < cache_size) {
      // TODO(andydavis) Reduce block management overhead for small tensors.
      internal::TensorExecutor<Expression, DefaultDevice, Vectorizable,
                               false>::run(expr, device);
      return;
    }

    const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL);
    if (needs_assign) {
      // Size tensor blocks to fit in cache (or requested target block size).
      size_t block_total_size = numext::mini(cache_size, total_size);
      TensorBlockShapeType block_shape = kUniformAllDims;
      // Query expression tree for desired block size/shape.
      std::vector<internal::TensorOpResourceRequirements> resources;
      evaluator.getResourceRequirements(&resources);
      if (!resources.empty()) {
        // TODO(andydavis) Implement different policies (i.e. revert to a
        // default policy if block shapes/sizes conflict).
        block_shape = resources[0].block_shape;
        block_total_size = resources[0].block_total_size;
      }

      TensorBlockMapper block_mapper(evaluator.dimensions(),
                                     block_shape,
                                     block_total_size);
      block_total_size = block_mapper.block_dims_total_size();

      Scalar* data = static_cast<Scalar*>(device.allocate(
          block_total_size * sizeof(Scalar)));

      const Index total_block_count = block_mapper.total_block_count();
      for (Index i = 0; i < total_block_count; ++i) {
        TensorBlock block = block_mapper.GetBlockForIndex(i, data);
        evaluator.evalBlock(&block);
      }
      device.deallocate(data);
    }
    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(void* evaluator_in, const Index first, const Index last) {
    Evaluator evaluator(*static_cast<Evaluator*>(evaluator_in));
    eigen_assert(last >= first);
    for (Index i = first; i < last; ++i) {
      evaluator.evalScalar(i);
    }
  }

  static Index alignBlockSize(Index size) {
    return size;
  }
};

template <typename Evaluator, typename Index>
struct EvalRange<Evaluator, Index, true> {
  static const int PacketSize = unpacket_traits<typename Evaluator::PacketReturnType>::size;

  static void run(void* evaluator_in, const Index first, const Index last) {
    Evaluator evaluator(*static_cast<Evaluator*>(evaluator_in));
    eigen_assert(last >= first);

    Index i = first;
    if (last - first >= PacketSize) {
      eigen_assert(first % PacketSize == 0);
      Index last_chunk_offset = last - 4 * PacketSize;
      // Give compiler a strong possibility to unroll the loop. But don't insist
      // on unrolling, because if the function is expensive compiler should not
      // unroll the loop at the expense of inlining.
      for (; i <= last_chunk_offset; i += 4*PacketSize) {
        for (Index j = 0; j < 4; j++) {
          evaluator.evalPacket(i + j * PacketSize);
        }
      }
      last_chunk_offset = last - PacketSize;
      for (; i <= last_chunk_offset; i += PacketSize) {
        evaluator.evalPacket(i);
      }
    }
    for (; i < last; ++i) {
      evaluator.evalScalar(i);
    }
  }

  static Index alignBlockSize(Index size) {
    // Align block size to packet size and account for unrolling in run above.
    if (size >= 16 * PacketSize) {
      return (size + 4 * PacketSize - 1) & ~(4 * PacketSize - 1);
    }
    // Aligning to 4 * PacketSize would increase block size by more than 25%.
    return (size + PacketSize - 1) & ~(PacketSize - 1);
  }
};

template <typename Expression, bool Vectorizable, bool Tileable>
class TensorExecutor<Expression, ThreadPoolDevice, Vectorizable, Tileable> {
 public:
  typedef typename Expression::Index Index;
  static inline void run(const Expression& expr, const ThreadPoolDevice& device)
  {
    typedef TensorEvaluator<Expression, ThreadPoolDevice> Evaluator;
    typedef EvalRange<Evaluator, Index, Vectorizable> EvalRange;
    Evaluator evaluator(expr, device);
    const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL);
    if (needs_assign)
    {
      const Index PacketSize = Vectorizable ? unpacket_traits<typename Evaluator::PacketReturnType>::size : 1;
      const Index size = array_prod(evaluator.dimensions());
      device.parallelFor(size, evaluator.costPerCoeff(Vectorizable),
                         EvalRange::alignBlockSize,
                         [&evaluator](Index first, Index last) {
                           EvalRange::run(&evaluator, first, last);
                         });
    }
    evaluator.cleanup();
  }
};


template <typename Expression, bool Vectorizable>
class TensorExecutor<Expression, ThreadPoolDevice, Vectorizable, true> {
 public:
  typedef typename Expression::Index Index;
  static inline void run(const Expression& expr,
                         const ThreadPoolDevice& device) {
    typedef TensorEvaluator<Expression, ThreadPoolDevice> Evaluator;
    typedef typename internal::remove_const<
        typename traits<Expression>::Scalar>::type Scalar;
    typedef typename traits<Expression>::Index Index;
    static const std::size_t NumDims = traits<Expression>::NumDimensions;
    typedef TensorBlockMapper<Index, Scalar, NumDims, Evaluator::Layout>
        TensorBlockMapper;
    typedef TensorBlock<Index, Scalar, NumDims, Evaluator::Layout>
        TensorBlock;

    Evaluator evaluator(expr, device);
    std::size_t total_size = array_prod(evaluator.dimensions());
    std::size_t cache_size = device.firstLevelCacheSize() / sizeof(Scalar);
    if (total_size < cache_size) {
      // TODO(andydavis) Reduce block management overhead for small tensors.
      internal::TensorExecutor<Expression, ThreadPoolDevice, Vectorizable,
                               false>::run(expr, device);
      evaluator.cleanup();
      return;
    }

    const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL);
    if (needs_assign) {
      TensorBlockShapeType block_shape = kUniformAllDims;
      size_t block_total_size = 0;
      // Query expression tree for desired block size/shape.
      std::vector<internal::TensorOpResourceRequirements> resources;
      evaluator.getResourceRequirements(&resources);
      if (!resources.empty()) {
        // TODO(andydavis) Implement different shape/size policies.
        block_shape = resources[0].block_shape;
        block_total_size = resources[0].block_total_size;
      }
      int num_threads = device.numThreads();

      // Estimate minimum block size based on cost.
      TensorOpCost cost = evaluator.costPerCoeff(Vectorizable);
      double taskSize = TensorCostModel<ThreadPoolDevice>::taskSize(1, cost);
      size_t block_size = 1.0 / taskSize;
      TensorBlockMapper block_mapper(evaluator.dimensions(), block_shape,
                                     block_size);
      block_size = block_mapper.block_dims_total_size();
      const size_t aligned_blocksize =
          EIGEN_MAX_ALIGN_BYTES *
          divup<size_t>(block_size * sizeof(Scalar), EIGEN_MAX_ALIGN_BYTES);
      void* buf = internal::aligned_malloc((num_threads+1) * aligned_blocksize);
      device.parallelFor(
          block_mapper.total_block_count(), cost * block_size,
          [=, &device, &evaluator, &block_mapper](Index first, Index last) {
            // currentThreadId() returns -1 if called from a thread not in the
            // threadpool, such as the main thread dispatching Eigen expressions.
            const int thread_idx = device.currentThreadId();
            eigen_assert(thread_idx >= -1 && thread_idx < num_threads);
            Scalar* thread_buf = reinterpret_cast<Scalar*>(
                static_cast<char*>(buf) + aligned_blocksize * (thread_idx + 1));
            for (Index i = first; i < last; ++i) {
              auto block = block_mapper.GetBlockForIndex(i, thread_buf);
              evaluator.evalBlock(&block);
            }
          });
      internal::aligned_free(buf);
    }
    evaluator.cleanup();
  }
};

#endif


// GPU: the evaluation of the expression is offloaded to a GPU.
#if defined(EIGEN_USE_GPU)

template <typename Expression, bool Vectorizable, bool Tileable>
class TensorExecutor<Expression, GpuDevice, Vectorizable, Tileable> {
 public:
  typedef typename Expression::Index Index;
  static void run(const Expression& expr, const GpuDevice& device);
};


#if defined(__CUDACC__)
template <typename Evaluator, typename Index, bool Vectorizable>
struct EigenMetaKernelEval {
  static __device__ EIGEN_ALWAYS_INLINE
  void run(Evaluator eval, Index first, Index last, Index step_size) {
    for (Index i = first; i < last; i += step_size) {
      eval.evalScalar(i);
    }
  }
};

template <typename Evaluator, typename Index>
struct EigenMetaKernelEval<Evaluator, Index, true> {
  static __device__ EIGEN_ALWAYS_INLINE
  void run(Evaluator eval, Index first, Index last, Index step_size) {
    const Index PacketSize = unpacket_traits<typename Evaluator::PacketReturnType>::size;
    const Index vectorized_size = (last / PacketSize) * PacketSize;
    const Index vectorized_step_size = step_size * PacketSize;

    // Use the vector path
    for (Index i = first * PacketSize; i < vectorized_size;
         i += vectorized_step_size) {
      eval.evalPacket(i);
    }
    for (Index i = vectorized_size + first; i < last; i += step_size) {
      eval.evalScalar(i);
    }
  }
};

template <typename Evaluator, typename Index>
__global__ void
__launch_bounds__(1024)
EigenMetaKernel(Evaluator memcopied_eval, Index size) {

  const Index first_index = blockIdx.x * blockDim.x + threadIdx.x;
  const Index step_size = blockDim.x * gridDim.x;

  // 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 bool vectorizable = Evaluator::PacketAccess & Evaluator::IsAligned;
  EigenMetaKernelEval<Evaluator, Index, vectorizable>::run(eval, first_index, size, step_size);
}

/*static*/
template <typename Expression, bool Vectorizable, bool Tileable>
inline void TensorExecutor<Expression, GpuDevice, Vectorizable, Tileable>::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 when tensorflow calls 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<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