/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */

#include <vector>

#include "glog/logging.h"
#include "paddle/fluid/memory/allocation/allocator_facade.h"
#include "paddle/fluid/memory/malloc.h"

#include "paddle/fluid/memory/detail/buddy_allocator.h"
#include "paddle/fluid/memory/detail/system_allocator.h"
#include "paddle/fluid/platform/gpu_info.h"

DEFINE_bool(init_allocated_mem, false,
            "It is a mistake that the values of the memory allocated by "
            "BuddyAllocator are always zeroed in some op's implementation. "
            "To find this error in time, we use init_allocated_mem to indicate "
            "that initializing the allocated memory with a small value "
            "during unit testing.");
DECLARE_double(fraction_of_gpu_memory_to_use);

DEFINE_string(
    allocator_strategy, "legacy",
    "The allocation strategy. Legacy means the original allocator of Fluid."
    "New means the experimental allocators of Fluid. in [legacy, new]");

namespace paddle {
namespace memory {

namespace legacy {

using BuddyAllocator = detail::BuddyAllocator;

BuddyAllocator* GetCPUBuddyAllocator() {
  // We tried thread_local for inference::RNN1 model, but that not works much
  // for multi-thread test.
  static std::once_flag init_flag;
  static detail::BuddyAllocator* a = nullptr;

  std::call_once(init_flag, []() {
    a = new detail::BuddyAllocator(
        std::unique_ptr<detail::SystemAllocator>(new detail::CPUAllocator),
        platform::CpuMinChunkSize(), platform::CpuMaxChunkSize());
  });

  return a;
}

// We compared the NaiveAllocator with BuddyAllocator in CPU memory allocation,
// seems they are almost the same overhead.
struct NaiveAllocator {
  void* Alloc(size_t size) { return malloc(size); }

  void Free(void* p) {
    PADDLE_ENFORCE(p);
    free(p);
  }

  static NaiveAllocator* Instance() {
    static NaiveAllocator x;
    return &x;
  }

 private:
  std::mutex lock_;
};

template <>
void* Alloc<platform::CPUPlace>(const platform::CPUPlace& place, size_t size) {
  VLOG(10) << "Allocate " << size << " bytes on " << platform::Place(place);
  void* p = GetCPUBuddyAllocator()->Alloc(size);
  if (FLAGS_init_allocated_mem) {
    memset(p, 0xEF, size);
  }
  VLOG(10) << "  pointer=" << p;
  return p;
}

template <>
void Free<platform::CPUPlace>(const platform::CPUPlace& place, void* p) {
  VLOG(10) << "Free pointer=" << p << " on " << platform::Place(place);
  GetCPUBuddyAllocator()->Free(p);
}

template <>
size_t Used<platform::CPUPlace>(const platform::CPUPlace& place) {
  return GetCPUBuddyAllocator()->Used();
}

#ifdef PADDLE_WITH_CUDA
BuddyAllocator* GetGPUBuddyAllocator(int gpu_id) {
  static std::once_flag init_flag;
  static detail::BuddyAllocator** a_arr = nullptr;

  std::call_once(init_flag, [gpu_id]() {
    int gpu_num = platform::GetCUDADeviceCount();
    PADDLE_ENFORCE(gpu_id < gpu_num, "gpu_id:%d should < gpu_num:%d", gpu_id,
                   gpu_num);

    a_arr = new BuddyAllocator*[gpu_num];
    for (int i = 0; i < gpu_num; i++) {
      a_arr[i] = nullptr;
      platform::SetDeviceId(i);
      a_arr[i] = new BuddyAllocator(
          std::unique_ptr<detail::SystemAllocator>(new detail::GPUAllocator(i)),
          platform::GpuMinChunkSize(), platform::GpuMaxChunkSize());

      VLOG(10) << "\n\nNOTE: each GPU device use "
               << FLAGS_fraction_of_gpu_memory_to_use * 100
               << "% of GPU memory.\n"
               << "You can set GFlags environment variable '"
               << "FLAGS_fraction_of_gpu_memory_to_use"
               << "' to change the fraction of GPU usage.\n\n";
    }
  });

  platform::SetDeviceId(gpu_id);
  return a_arr[gpu_id];
}
#endif

template <>
size_t Used<platform::CUDAPlace>(const platform::CUDAPlace& place) {
#ifdef PADDLE_WITH_CUDA
  return GetGPUBuddyAllocator(place.device)->Used();
#else
  PADDLE_THROW("'CUDAPlace' is not supported in CPU only device.");
#endif
}

template <>
void* Alloc<platform::CUDAPlace>(const platform::CUDAPlace& place,
                                 size_t size) {
#ifdef PADDLE_WITH_CUDA
  auto* buddy_allocator = GetGPUBuddyAllocator(place.device);
  auto* ptr = buddy_allocator->Alloc(size);
  if (ptr == nullptr) {
    int cur_dev = platform::GetCurrentDeviceId();
    platform::SetDeviceId(place.device);
    size_t avail, total;
    platform::GpuMemoryUsage(&avail, &total);
    LOG(WARNING) << "Cannot allocate " << size << " bytes in GPU "
                 << place.device << ", available " << avail << " bytes";
    LOG(WARNING) << "total " << total;
    LOG(WARNING) << "GpuMinChunkSize " << buddy_allocator->GetMinChunkSize();
    LOG(WARNING) << "GpuMaxChunkSize " << buddy_allocator->GetMaxChunkSize();
    LOG(WARNING) << "GPU memory used: " << Used<platform::CUDAPlace>(place);
    platform::SetDeviceId(cur_dev);
  }
  if (FLAGS_init_allocated_mem) {
    cudaMemset(ptr, 0xEF, size);
  }
  return ptr;
#else
  PADDLE_THROW("'CUDAPlace' is not supported in CPU only device.");
#endif
}

template <>
void Free<platform::CUDAPlace>(const platform::CUDAPlace& place, void* p) {
#ifdef PADDLE_WITH_CUDA
  GetGPUBuddyAllocator(place.device)->Free(p);
#else
  PADDLE_THROW("'CUDAPlace' is not supported in CPU only device.");
#endif
}

#ifdef PADDLE_WITH_CUDA
BuddyAllocator* GetCUDAPinnedBuddyAllocator() {
  static std::once_flag init_flag;
  static BuddyAllocator* ba = nullptr;

  std::call_once(init_flag, []() {
    ba = new BuddyAllocator(std::unique_ptr<detail::SystemAllocator>(
                                new detail::CUDAPinnedAllocator),
                            platform::CUDAPinnedMinChunkSize(),
                            platform::CUDAPinnedMaxChunkSize());
  });

  return ba;
}
#endif

template <>
size_t Used<platform::CUDAPinnedPlace>(const platform::CUDAPinnedPlace& place) {
#ifdef PADDLE_WITH_CUDA
  return GetCUDAPinnedBuddyAllocator()->Used();
#else
  PADDLE_THROW("'CUDAPinnedPlace' is not supported in CPU only device.");
#endif
}

template <>
void* Alloc<platform::CUDAPinnedPlace>(const platform::CUDAPinnedPlace& place,
                                       size_t size) {
#ifdef PADDLE_WITH_CUDA
  auto* buddy_allocator = GetCUDAPinnedBuddyAllocator();
  void* ptr = buddy_allocator->Alloc(size);

  if (ptr == nullptr) {
    LOG(WARNING) << "cudaMallocHost Cannot allocate " << size
                 << " bytes in CUDAPinnedPlace";
  }
  if (FLAGS_init_allocated_mem) {
    memset(ptr, 0xEF, size);
  }
  return ptr;
#else
  PADDLE_THROW("'CUDAPinnedPlace' is not supported in CPU only device.");
#endif
}

template <>
void Free<platform::CUDAPinnedPlace>(const platform::CUDAPinnedPlace& place,
                                     void* p) {
#ifdef PADDLE_WITH_CUDA
  GetCUDAPinnedBuddyAllocator()->Free(p);
#else
  PADDLE_THROW("'CUDAPinnedPlace' is not supported in CPU only device.");
#endif
}

struct AllocVisitor : public boost::static_visitor<void*> {
  inline explicit AllocVisitor(size_t size) : size_(size) {}

  template <typename Place>
  inline void* operator()(const Place& place) const {
    return Alloc<Place>(place, size_);
  }

 private:
  size_t size_;
};

struct FreeVisitor : public boost::static_visitor<void> {
  inline explicit FreeVisitor(void* ptr) : ptr_(ptr) {}

  template <typename Place>
  inline void operator()(const Place& place) const {
    Free<Place>(place, ptr_);
  }

 private:
  void* ptr_;
};

size_t Usage::operator()(const platform::CPUPlace& cpu) const {
  return Used(cpu);
}

size_t Usage::operator()(const platform::CUDAPlace& gpu) const {
#ifdef PADDLE_WITH_CUDA
  return Used(gpu);
#else
  PADDLE_THROW("'CUDAPlace' is not supported in CPU only device.");
#endif
}

size_t Usage::operator()(const platform::CUDAPinnedPlace& cuda_pinned) const {
#ifdef PADDLE_WITH_CUDA
  return Used(cuda_pinned);
#else
  PADDLE_THROW("'CUDAPinnedPlace' is not supported in CPU only device.");
#endif
}

class LegacyAllocation : public Allocation {
 public:
  using Allocation::Allocation;

  ~LegacyAllocation() final {
    boost::apply_visitor(FreeVisitor(this->ptr()), this->place());
  }
};

}  // namespace legacy

std::shared_ptr<Allocation> AllocShared(const platform::Place& place,
                                        size_t size, Allocator::Attr attr) {
  if (FLAGS_allocator_strategy == "legacy") {
    void* p = boost::apply_visitor(legacy::AllocVisitor(size), place);
    return std::shared_ptr<Allocation>(
        new legacy::LegacyAllocation(p, size, place));
  } else {
    return allocation::AllocatorFacade::Instance().AllocShared(place, size,
                                                               attr);
  }
}

std::unique_ptr<Allocation> Alloc(const platform::Place& place, size_t size,
                                  Allocator::Attr attr) {
  if (FLAGS_allocator_strategy == "legacy") {
    void* p = boost::apply_visitor(legacy::AllocVisitor(size), place);
    return std::unique_ptr<Allocation>(
        new legacy::LegacyAllocation(p, size, place));
  } else {
    return allocation::AllocatorFacade::Instance().Alloc(place, size, attr);
  }
}

}  // namespace memory
}  // namespace paddle