/* Copyright (c) 2022 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. */

#ifndef PADDLE_WITH_HIP
// HIP not support cusolver

#include "paddle/phi/kernels/cholesky_kernel.h"

#include <thrust/device_vector.h>

#include <algorithm>
#include <vector>

#include "paddle/phi/backends/dynload/cusolver.h"
#include "paddle/phi/backends/gpu/cuda/cuda_graph_with_memory_pool.h"
#include "paddle/phi/backends/gpu/gpu_context.h"
#include "paddle/phi/common/memory_utils.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/funcs/for_range.h"

namespace phi {

template <typename T>
struct MatrixBandPartFunctor {
  /*! Set output as input value outside a central band and 0 inside that band.
   * That is: output[i, j, ..., m, n] = in_band(m, n) * input[i, j, ..., m, n]
   * where: in_band(m, n) = (num_lower < 0 || (m-n) <= num_lower)) && (num_upper
   * < 0 || (n-m) <= num_upper)
   */
  MatrixBandPartFunctor(const int m,
                        const int n,
                        const int num_lower_diags,
                        const int num_upper_diags,
                        const T* input,
                        T* output)
      : m_(m),
        n_(n),
        num_lower_diags_(num_lower_diags),
        num_upper_diags_(num_upper_diags),
        input_(input),
        output_(output) {}

  HOSTDEVICE void operator()(size_t index) const {
    const int col = index % n_;
    const int row = (index / n_) % m_;
    const int band_start = (num_lower_diags_ < 0 ? 0 : row - num_lower_diags_);
    const int band_end =
        (num_upper_diags_ < 0 ? n_ : row + num_upper_diags_ + 1);
    if (col < band_start || col >= band_end) {
      output_[index] = static_cast<T>(0);
    } else {
      output_[index] = input_[index];
    }
  }

  const int m_, n_, num_lower_diags_, num_upper_diags_;
  const T* input_;
  T* output_;
};

#define FUNC_WITH_TYPES(m) m(float, S) m(double, D)

#define POTRF_INSTANCE(T, C)                                             \
  void Potrf(const GPUContext& dev_ctx,                                  \
             cublasFillMode_t uplo,                                      \
             int n,                                                      \
             T* A,                                                       \
             int lda,                                                    \
             int* info) {                                                \
    auto handle = dev_ctx.cusolver_dn_handle();                          \
    int workspace_size = 0;                                              \
    PADDLE_ENFORCE_GPU_SUCCESS(dynload::cusolverDn##C##potrf_bufferSize( \
        handle, uplo, n, A, lda, &workspace_size));                      \
    auto workspace = memory_utils::Alloc(                                \
        dev_ctx.GetPlace(),                                              \
        workspace_size * sizeof(T),                                      \
        Stream(reinterpret_cast<StreamId>(dev_ctx.stream())));           \
    T* workspace_ptr = reinterpret_cast<T*>(workspace->ptr());           \
    PADDLE_ENFORCE_GPU_SUCCESS(dynload::cusolverDn##C##potrf(            \
        handle, uplo, n, A, lda, workspace_ptr, workspace_size, info));  \
  }

FUNC_WITH_TYPES(POTRF_INSTANCE);

#if CUDA_VERSION >= 11040
#define POTRF64_INSTANCE(T, C)                                            \
  void Potrf64(const GPUContext& dev_ctx,                                 \
               cublasFillMode_t uplo,                                     \
               int64_t n,                                                 \
               T* A,                                                      \
               int64_t lda,                                               \
               int* info) {                                               \
    auto handle = dev_ctx.cusolver_dn_handle();                           \
    cusolverDnParams_t params;                                            \
    PADDLE_ENFORCE_GPU_SUCCESS(dynload::cusolverDnCreateParams(&params)); \
    size_t workspace_device_size = 0;                                     \
    size_t workspace_host_size = 0;                                       \
    cudaDataType_t data_type =                                            \
        std::is_same<T, float>::value ? CUDA_R_32F : CUDA_R_64F;          \
    PADDLE_ENFORCE_GPU_SUCCESS(                                           \
        dynload::cusolverDnXpotrf_bufferSize(handle,                      \
                                             params,                      \
                                             uplo,                        \
                                             n,                           \
                                             data_type,                   \
                                             A,                           \
                                             lda,                         \
                                             data_type,                   \
                                             &workspace_device_size,      \
                                             &workspace_host_size));      \
    auto workspace_device = memory_utils::Alloc(                          \
        dev_ctx.GetPlace(),                                               \
        workspace_device_size,                                            \
        Stream(reinterpret_cast<StreamId>(dev_ctx.stream())));            \
    auto workspace_host =                                                 \
        memory_utils::Alloc(CPUPlace(), workspace_host_size);             \
    PADDLE_ENFORCE_GPU_SUCCESS(                                           \
        dynload::cusolverDnXpotrf(handle,                                 \
                                  params,                                 \
                                  uplo,                                   \
                                  n,                                      \
                                  data_type,                              \
                                  A,                                      \
                                  lda,                                    \
                                  data_type,                              \
                                  workspace_device->ptr(),                \
                                  workspace_device_size,                  \
                                  workspace_host->ptr(),                  \
                                  workspace_host_size,                    \
                                  info));                                 \
    PADDLE_ENFORCE_GPU_SUCCESS(dynload::cusolverDnDestroyParams(params)); \
  }

FUNC_WITH_TYPES(POTRF64_INSTANCE);
#endif

#if CUDA_VERSION >= 9020 && !defined(_WIN32)
#define POTRF_BATCH_INSTANCE(T, C)                                   \
  void PotrfBatched(const GPUContext& dev_ctx,                       \
                    cublasFillMode_t uplo,                           \
                    int n,                                           \
                    T* Aarray[],                                     \
                    int lda,                                         \
                    int* info_array,                                 \
                    int batch_size) {                                \
    auto handle = dev_ctx.cusolver_dn_handle();                      \
    PADDLE_ENFORCE_GPU_SUCCESS(dynload::cusolverDn##C##potrfBatched( \
        handle, uplo, n, Aarray, lda, info_array, batch_size));      \
  }

FUNC_WITH_TYPES(POTRF_BATCH_INSTANCE);
#endif

template <typename T, typename Context>
void CholeskyKernel(const Context& dev_ctx,
                    const DenseTensor& x,
                    bool upper,
                    DenseTensor* out) {
  if (x.numel() == 0) {
    dev_ctx.template Alloc<T>(out);
    return;
  }

  auto& dims = x.dims();
  int64_t batch_count64 = 1;
  for (int i = 0; i < dims.size() - 2; i++) {
    batch_count64 *= dims[i];
  }
  // TODO(large-tensor): cusolver batch_count not support int64
  PADDLE_ENFORCE_LE_INT_MAX(batch_count64, "batch_count");
  int batch_count = static_cast<int>(batch_count64);

  int64_t m = dims[dims.size() - 1];
  // TODO(large-tensor): cusolver n not support int64
  PADDLE_ENFORCE_LE_INT_MAX(m, "m");
  int m_int = static_cast<int>(m);

  int64_t tensor_size = batch_count * static_cast<int64_t>(m) * m;

  const auto* x_data = x.data<T>();
  auto* out_data = dev_ctx.template Alloc<T>(out);

  // matrices are assumed to be stored in column-major order in cusolver
  cublasFillMode_t uplo =
      upper ? CUBLAS_FILL_MODE_LOWER : CUBLAS_FILL_MODE_UPPER;
  // portf is inplace, thus copy the triangular part of the input matrices to
  // the output and set the other triangular part to 0 firstly

  funcs::ForRange<GPUContext> for_range(dev_ctx, tensor_size);
  // Pre-processing
  if (upper) {
    MatrixBandPartFunctor<T> matrix_band_part_functor(
        m, m, 0, -1, x_data, out_data);
    for_range(matrix_band_part_functor);
  } else {
    MatrixBandPartFunctor<T> matrix_band_part_functor(
        m, m, -1, 0, x_data, out_data);
    for_range(matrix_band_part_functor);
  }

  auto info =
      memory_utils::Alloc(dev_ctx.GetPlace(),
                          sizeof(int) * batch_count,
                          Stream(reinterpret_cast<StreamId>(dev_ctx.stream())));
  auto* info_ptr = reinterpret_cast<int*>(info->ptr());

#if CUDA_VERSION >= 9020 && !defined(_WIN32)
  if (batch_count > 1) {
    std::vector<T*> output_ptrs;
    for (int i = 0; i < batch_count; i++) {
      output_ptrs.emplace_back(out_data + static_cast<int64_t>(i) * m * m);
    }
    thrust::device_vector<T*> dev_output_ptrs(output_ptrs.begin(),
                                              output_ptrs.end());
    PotrfBatched(dev_ctx,
                 uplo,
                 m_int,
                 thrust::raw_pointer_cast(dev_output_ptrs.data()),
                 m_int,
                 info_ptr,
                 batch_count);
    // TODO(guosheng): There seems to a bug in cusolver potrfBatched and need
    // to clear the upper triangle of the output. Remove this workaround once
    // the bug is fixed.

    if (!upper) {
      MatrixBandPartFunctor<T> matrix_band_part_functor(
          m, m, -1, 0, out_data, out_data);
      for_range(matrix_band_part_functor);
    }
  } else {
#endif
    for (int i = 0; i < batch_count; i++) {
      int64_t offset = static_cast<int64_t>(i) * m * m;
#if CUDA_VERSION >= 11040
      Potrf64(dev_ctx, uplo, m_int, out_data + offset, m_int, info_ptr + i);
#else
    Potrf(dev_ctx, uplo, m_int, out_data + offset, m_int, info_ptr + i);
#endif
    }
#if CUDA_VERSION >= 9020 && !defined(_WIN32)
  }
#endif
  // check the info
  PADDLE_ENFORCE_EQ(
      backends::gpu::IsCUDAGraphCapturing(),
      false,
      common::errors::InvalidArgument(
          "CholeskyKernel does not support CUDA Graph capture: async D2H copy "
          "to local vector 'error_info' will bake the destination address into "
          "the graph; on replay the vector is re-created at a different "
          "address, causing a dangling-pointer write."));
  std::vector<int> error_info;
  error_info.resize(batch_count);
  memory_utils::Copy(CPUPlace(),
                     error_info.data(),
                     dev_ctx.GetPlace(),
                     info_ptr,
                     sizeof(int) * batch_count,
                     dev_ctx.stream());

  for (int i = 0; i < batch_count; ++i) {
    const int info = error_info[i];
    if (info == 0) {
      continue;
    }
    if (info < 0) {
      PADDLE_ENFORCE_EQ(
          info,
          0,
          errors::InvalidArgument("Cholesky kernel failed for batch %d: "
                                  "The %d-th argument was invalid, please "
                                  "check the kernel implementation.",
                                  i,
                                  -info));
    }
    PADDLE_ENFORCE_EQ(
        info,
        0,
        errors::PreconditionNotMet(
            "Cholesky decomposition failed for batch %d: "
            "The leading minor of order %d is not positive definite.",
            i,
            info));
  }

  // Post-processing to clear the other triangle
  if (upper) {
    MatrixBandPartFunctor<T> band_part_post(m, m, 0, -1, out_data, out_data);
    for_range(band_part_post);
  } else {
    MatrixBandPartFunctor<T> band_part_post(m, m, -1, 0, out_data, out_data);
    for_range(band_part_post);
  }
}

}  // namespace phi

PD_REGISTER_KERNEL(cholesky,  // cuda_only
                   GPU,
                   ALL_LAYOUT,
                   phi::CholeskyKernel,
                   float,
                   double) {}

#endif  // not PADDLE_WITH_HIP