// 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_DEVICE_TYPE_H
#define EIGEN_CXX11_TENSOR_TENSOR_DEVICE_TYPE_H


namespace Eigen {

// Default device for the machine (typically a single cpu core)
struct DefaultDevice {
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void* allocate(size_t num_bytes) const {
    return internal::aligned_malloc(num_bytes);
  }
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void deallocate(void* buffer) const {
    internal::aligned_free(buffer);
  }
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memcpy(void* dst, const void* src, size_t n) const {
    ::memcpy(dst, src, n);
  }
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memcpyHostToDevice(void* dst, const void* src, size_t n) const {
    memcpy(dst, src, n);
  }
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memcpyDeviceToHost(void* dst, const void* src, size_t n) const {
    memcpy(dst, src, n);
  }
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memset(void* buffer, int c, size_t n) const {
    ::memset(buffer, c, n);
  }

  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE size_t numThreads() const {
#ifndef __CUDA_ARCH__
    // Running on the host CPU
    return 1;
#else
    // Running on a CUDA device
    return 32;
#endif
  }

  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int majorDeviceVersion() const {
#ifndef __CUDA_ARCH__
    // Running single threaded on the host CPU
    // Should return an enum that encodes the ISA supported by the CPU
    return 1;
#else
    // Running on a CUDA device
    return __CUDA_ARCH__ / 100;
#endif
  }
};


// Multiple cpu cores
// We should really use a thread pool here but first we need to find a portable thread pool library.
#ifdef EIGEN_USE_THREADS

// This defines an interface that ThreadPoolDevice can take to use
// custom thread pools underneath.
class ThreadPoolInterface {
 public:
  virtual void Schedule(std::function<void()> fn) = 0;

  virtual ~ThreadPoolInterface() {}
};

// The implementation of the ThreadPool type ensures that the Schedule method
// runs the functions it is provided in FIFO order when the scheduling is done
// by a single thread.
class ThreadPool : public ThreadPoolInterface {
 public:
  // Construct a pool that contains "num_threads" threads.
  explicit ThreadPool(int num_threads) {
    for (int i = 0; i < num_threads; i++) {
      threads_.push_back(new std::thread([this]() { WorkerLoop(); }));
    }
  }

  // Wait until all scheduled work has finished and then destroy the
  // set of threads.
  ~ThreadPool()
  {
    {
      // Wait for all work to get done.
      std::unique_lock<std::mutex> l(mu_);
      empty_.wait(l, [this]() { return pending_.empty(); });
      exiting_ = true;

      // Wakeup all waiters.
      for (auto w : waiters_) {
        w->ready = true;
        w->work = nullptr;
        w->cv.notify_one();
      }
    }

    // Wait for threads to finish.
    for (auto t : threads_) {
      t->join();
      delete t;
    }
  }

  // Schedule fn() for execution in the pool of threads. The functions are
  // executed in the order in which they are scheduled.
  void Schedule(std::function<void()> fn) {
    std::unique_lock<std::mutex> l(mu_);
    if (waiters_.empty()) {
      pending_.push_back(fn);
    } else {
      Waiter* w = waiters_.back();
      waiters_.pop_back();
      w->ready = true;
      w->work = fn;
      w->cv.notify_one();
    }
  }

 protected:
  void WorkerLoop() {
    std::unique_lock<std::mutex> l(mu_);
    Waiter w;
    while (!exiting_) {
      std::function<void()> fn;
      if (pending_.empty()) {
        // Wait for work to be assigned to me
        w.ready = false;
        waiters_.push_back(&w);
        w.cv.wait(l, [&w]() { return w.ready; });
        fn = w.work;
        w.work = nullptr;
      } else {
        // Pick up pending work
        fn = pending_.front();
        pending_.pop_front();
        if (pending_.empty()) {
          empty_.notify_all();
        }
      }
      if (fn) {
        mu_.unlock();
        fn();
        mu_.lock();
      }
    }
  }

 private:
  struct Waiter {
    std::condition_variable cv;
    std::function<void()> work;
    bool ready;
  };

  std::mutex mu_;
  std::vector<std::thread*> threads_;               // All threads
  std::vector<Waiter*> waiters_;                    // Stack of waiting threads.
  std::deque<std::function<void()>> pending_;       // Queue of pending work
  std::condition_variable empty_;                   // Signaled on pending_.empty()
  bool exiting_ = false;
};


// Notification is an object that allows a user to to wait for another
// thread to signal a notification that an event has occurred.
//
// Multiple threads can wait on the same Notification object.
// but only one caller must call Notify() on the object.
class Notification {
 public:
  Notification() : notified_(false) {}
  ~Notification() {}

  void Notify() {
    std::unique_lock<std::mutex> l(mu_);
    eigen_assert(!notified_);
    notified_ = true;
    cv_.notify_all();
  }

  void WaitForNotification() {
    std::unique_lock<std::mutex> l(mu_);
    cv_.wait(l, [this]() { return notified_; } );
  }

 private:
  std::mutex mu_;
  std::condition_variable cv_;
  bool notified_;
};

// Runs an arbitrary function and then calls Notify() on the passed in
// Notification.
template <typename Function, typename... Args> struct FunctionWrapper
{
  static void run(Notification* n, Function f, Args... args) {
    f(args...);
    n->Notify();
  }
};

static EIGEN_STRONG_INLINE void wait_until_ready(Notification* n) {
  if (n) {
    n->WaitForNotification();
  }
}


// Build a thread pool device on top the an existing pool of threads.
struct ThreadPoolDevice {
  // The ownership of the thread pool remains with the caller.
  ThreadPoolDevice(ThreadPoolInterface* pool, size_t num_cores) : pool_(pool), num_threads_(num_cores) { }

  EIGEN_STRONG_INLINE void* allocate(size_t num_bytes) const {
    return internal::aligned_malloc(num_bytes);
  }

  EIGEN_STRONG_INLINE void deallocate(void* buffer) const {
    internal::aligned_free(buffer);
  }

  EIGEN_STRONG_INLINE void memcpy(void* dst, const void* src, size_t n) const {
    ::memcpy(dst, src, n);
  }
  EIGEN_STRONG_INLINE void memcpyHostToDevice(void* dst, const void* src, size_t n) const {
    memcpy(dst, src, n);
  }
  EIGEN_STRONG_INLINE void memcpyDeviceToHost(void* dst, const void* src, size_t n) const {
    memcpy(dst, src, n);
  }

  EIGEN_STRONG_INLINE void memset(void* buffer, int c, size_t n) const {
    ::memset(buffer, c, n);
  }

  EIGEN_STRONG_INLINE size_t numThreads() const {
    return num_threads_;
  }

  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int majorDeviceVersion() const {
    // Should return an enum that encodes the ISA supported by the CPU
    return 1;
  }

  template <class Function, class... Args>
  EIGEN_STRONG_INLINE Notification* enqueue(Function&& f, Args&&... args) const {
    Notification* n = new Notification();
    std::function<void()> func =
      std::bind(&FunctionWrapper<Function, Args...>::run, n, f, args...);
    pool_->Schedule(func);
    return n;
  }
  template <class Function, class... Args>
  EIGEN_STRONG_INLINE void enqueueNoNotification(Function&& f, Args&&... args) const {
    std::function<void()> func = std::bind(f, args...);
    pool_->Schedule(func);
  }

 private:
  ThreadPoolInterface* pool_;
  size_t num_threads_;
};

#endif


// GPU offloading
#ifdef EIGEN_USE_GPU

// This defines an interface that GPUDevice can take to use
// CUDA streams underneath.
class StreamInterface {
 public:
  virtual ~StreamInterface() {}

  virtual const cudaStream_t& stream() const = 0;
  virtual const cudaDeviceProp& deviceProperties() const = 0;

  // Allocate memory on the actual device where the computation will run
  virtual void* allocate(size_t num_bytes) const = 0;
  virtual void deallocate(void* buffer) const = 0;
};

#if defined(__CUDACC__)
static cudaDeviceProp* m_deviceProperties;
static bool m_devicePropInitialized = false;

static void initializeDeviceProp() {
  if (!m_devicePropInitialized) {
    if (!m_devicePropInitialized) {
      int num_devices;
      cudaError_t status = cudaGetDeviceCount(&num_devices);
      assert(status == cudaSuccess);
      m_deviceProperties = new cudaDeviceProp[num_devices];
      for (int i = 0; i < num_devices; ++i) {
        status = cudaGetDeviceProperties(&m_deviceProperties[i], i);
        assert(status == cudaSuccess);
      }
      m_devicePropInitialized = true;
    }
  }
}

static const cudaStream_t default_stream = cudaStreamDefault;

class CudaStreamDevice : public StreamInterface {
 public:
  // Use the default stream on the current device
  CudaStreamDevice() : stream_(&default_stream) {
    cudaGetDevice(&device_);
    initializeDeviceProp();
  }
  // Use the default stream on the specified device
  CudaStreamDevice(int device) : stream_(&default_stream), device_(device) {
    initializeDeviceProp();
  }
  // Use the specified stream. Note that it's the
  // caller responsibility to ensure that the stream can run on
  // the specified device. If no device is specified the code
  // assumes that the stream is associated to the current gpu device.
  CudaStreamDevice(const cudaStream_t* stream, int device = -1)
      : stream_(stream), device_(device) {
    if (device < 0) {
      cudaGetDevice(&device_);
    } else {
      int num_devices;
      cudaError_t err = cudaGetDeviceCount(&num_devices);
      assert(err == cudaSuccess);
      assert(device < num_devices);
      device_ = device;
    }
    initializeDeviceProp();
  }

  const cudaStream_t& stream() const { return *stream_; }
  const cudaDeviceProp& deviceProperties() const {
    return m_deviceProperties[device_];
  }
  virtual void* allocate(size_t num_bytes) const {
    cudaError_t err = cudaSetDevice(device_);
    assert(err == cudaSuccess);
    void* result;
    err = cudaMalloc(&result, num_bytes);
    assert(err == cudaSuccess);
    assert(result != NULL);
    return result;
  }
  virtual void deallocate(void* buffer) const {
    cudaError_t err = cudaSetDevice(device_);
    assert(err == cudaSuccess);
    assert(buffer != NULL);
    err = cudaFree(buffer);
    assert(err == cudaSuccess);
  }

 private:
  const cudaStream_t* stream_;
  int device_;
};
#endif  // __CUDACC__

struct GpuDevice {
  // The StreamInterface is not owned: the caller is
  // responsible for its initialization and eventual destruction.
  explicit GpuDevice(const StreamInterface* stream) : stream_(stream) {
    eigen_assert(stream);
  }

  // TODO(bsteiner): This is an internal API, we should not expose it.
  EIGEN_STRONG_INLINE const cudaStream_t& stream() const {
    return stream_->stream();
  }

  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void* allocate(size_t num_bytes) const {
#ifndef __CUDA_ARCH__
    return stream_->allocate(num_bytes);
#else
    eigen_assert(false && "The default device should be used instead to generate kernel code");
    return NULL;
#endif
  }

  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void deallocate(void* buffer) const {
#ifndef __CUDA_ARCH__
    stream_->deallocate(buffer);

#else
    eigen_assert(false && "The default device should be used instead to generate kernel code");
#endif
  }

  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memcpy(void* dst, const void* src, size_t n) const {
#ifndef __CUDA_ARCH__
    cudaError_t err = cudaMemcpyAsync(dst, src, n, cudaMemcpyDeviceToDevice,
                                      stream_->stream());
    assert(err == cudaSuccess);
#else
    eigen_assert(false && "The default device should be used instead to generate kernel code");
#endif
  }

  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memcpyHostToDevice(void* dst, const void* src, size_t n) const {
#ifndef __CUDA_ARCH__
    cudaError_t err =
        cudaMemcpyAsync(dst, src, n, cudaMemcpyHostToDevice, stream_->stream());
    assert(err == cudaSuccess);
#else
    eigen_assert(false && "The default device should be used instead to generate kernel code");
#endif
  }

  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memcpyDeviceToHost(void* dst, const void* src, size_t n) const {
#ifndef __CUDA_ARCH__
    cudaError_t err =
        cudaMemcpyAsync(dst, src, n, cudaMemcpyDeviceToHost, stream_->stream());
    assert(err == cudaSuccess);
#else
    eigen_assert(false && "The default device should be used instead to generate kernel code");
#endif
  }

  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memset(void* buffer, int c, size_t n) const {
#ifndef __CUDA_ARCH__
    cudaError_t err = cudaMemsetAsync(buffer, c, n, stream_->stream());
    assert(err == cudaSuccess);
#else
    eigen_assert(false && "The default device should be used instead to generate kernel code");
#endif
  }

  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE size_t numThreads() const {
    // FIXME
    return 32;
  }

  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE size_t firstLevelCacheSize() const {
    // FIXME
    return 48*1024;
  }

  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE size_t lastLevelCacheSize() const {
    // We won't try to take advantage of the l2 cache for the time being, and
    // there is no l3 cache on cuda devices.
    return firstLevelCacheSize();
  }

  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void synchronize() const {
#if defined(__CUDACC__) && !defined(__CUDA_ARCH__)
    cudaError_t err = cudaStreamSynchronize(stream_->stream());
    assert(err == cudaSuccess);
#else
    assert(false && "The default device should be used instead to generate kernel code");
#endif
  }

  inline int getNumCudaMultiProcessors() const {
    return stream_->deviceProperties().multiProcessorCount;
  }
  inline int maxCudaThreadsPerBlock() const {
    return stream_->deviceProperties().maxThreadsPerBlock;
  }
  inline int maxCudaThreadsPerMultiProcessor() const {
    return stream_->deviceProperties().maxThreadsPerMultiProcessor;
  }
  inline int sharedMemPerBlock() const {
    return stream_->deviceProperties().sharedMemPerBlock;
  }
  inline int majorDeviceVersion() const {
    return stream_->deviceProperties().major;
  }

  // This function checks if the CUDA runtime recorded an error for the
  // underlying stream device.
  inline bool ok() const {
#ifdef __CUDACC__
    cudaError_t error = cudaStreamQuery(stream_->stream());
    return (error == cudaSuccess) || (error == cudaErrorNotReady);
#else
    return false;
#endif
  }

 private:
  const StreamInterface* stream_;

};


#define LAUNCH_CUDA_KERNEL(kernel, gridsize, blocksize, sharedmem, device, ...)            \
  (kernel) <<< (gridsize), (blocksize), (sharedmem), (device).stream() >>> (__VA_ARGS__);  \
  assert(cudaGetLastError() == cudaSuccess);


// FIXME: Should be device and kernel specific.
#ifdef __CUDACC__
static inline void setCudaSharedMemConfig(cudaSharedMemConfig config) {
  cudaError_t status = cudaDeviceSetSharedMemConfig(config);
  assert(status == cudaSuccess);
}
#endif

#endif

}  // end namespace Eigen

#endif // EIGEN_CXX11_TENSOR_TENSOR_DEVICE_TYPE_H