/*
Copyright 2020 The OneFlow 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 <cstdint>
#include "oneflow/core/common/just.h"
#include "oneflow/core/common/util.h"
#include "oneflow/core/framework/consistency_check.h"
#include "oneflow/core/framework/framework.h"
#include "oneflow/core/kernel/new_kernel_util.h"
#include "oneflow/core/kernel/kernel_util.h"
#ifdef WITH_CUDA
#include "oneflow/core/ep/cuda/cuda_stream.h"
#endif
#ifdef WITH_ROCM
#include "oneflow/core/ep/rocm/cuda_stream.h"
#endif
#include "oneflow/core/common/nd_index_offset_helper.h"

namespace oneflow {

namespace {

constexpr size_t NUM_DIM = 20;

template<typename T>
struct AsStridedFunctor final {
  void operator()(ep::Stream* stream, const T* input_buf, T* output_buf, const int64_t* dest_dims,
                  const int32_t* stride, const int32_t dest_num_dims, const int32_t storage_offset,
                  const int32_t input_num, const int32_t output_num) {
    NdIndexOffsetHelper<int64_t, NUM_DIM> destIndexOffsetHelper(dest_dims, dest_num_dims);
    FOR_RANGE(int64_t, i, 0, output_num) {
      int64_t dst_index[NUM_DIM];
      destIndexOffsetHelper.OffsetToNdIndex(i, dst_index, dest_num_dims);
      int32_t index_in_input = storage_offset;
      FOR_RANGE(int64_t, j, 0, dest_num_dims) { index_in_input += dst_index[j] * stride[j]; }
      output_buf[i] = input_buf[index_in_input];
    }
  }
};

template<typename T>
struct AsStridedGradFunctor final {
  void operator()(ep::Stream* stream, const T* dy_buf, T* dx_buf, const int64_t* dy_dims,
                  const int32_t* stride, const int32_t dy_num_dims, const int32_t storage_offset,
                  const int32_t dx_num, const int32_t dy_num) {
    NdIndexOffsetHelper<int64_t, NUM_DIM> destIndexOffsetHelper(dy_dims, dy_num_dims);
    FOR_RANGE(int64_t, i, 0, dy_num) {
      int64_t dy_index[NUM_DIM];
      destIndexOffsetHelper.OffsetToNdIndex(i, dy_index, dy_num_dims);
      int32_t index_in_dx = storage_offset;
      FOR_RANGE(int64_t, j, 0, dy_num_dims) { index_in_dx += dy_index[j] * stride[j]; }
      dx_buf[index_in_dx] += dy_buf[i];
    }
  }
};

}  // namespace

template<typename T>
class CpuAsStridedKernel final : public user_op::OpKernel {
 public:
  CpuAsStridedKernel() = default;
  ~CpuAsStridedKernel() = default;

 private:
  using user_op::OpKernel::Compute;
  void Compute(user_op::KernelComputeContext* ctx) const override {
    const user_op::Tensor* input = ctx->Tensor4ArgNameAndIndex("input", 0);
    user_op::Tensor* output = ctx->Tensor4ArgNameAndIndex("output", 0);
    const auto size = ctx->Attr<std::vector<int32_t>>("size");
    const auto stride = ctx->Attr<std::vector<int32_t>>("stride");
    const int32_t storage_offset = ctx->Attr<int32_t>("storage_offset");

    size_t dest_num_dims = output->shape_view().NumAxes();
    const int64_t* dest_dims = output->shape_view().ptr();
    const size_t input_num = input->shape_view().Count(0);
    const size_t output_num = output->shape_view().Count(0);

    AsStridedFunctor<T>()(ctx->stream(), input->dptr<T>(), output->mut_dptr<T>(), dest_dims,
                          stride.data(), dest_num_dims, storage_offset, input_num, output_num);
  }
  bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; }
};

template<typename T>
class CpuAsStridedGradKernel final : public user_op::OpKernel {
 public:
  CpuAsStridedGradKernel() = default;
  ~CpuAsStridedGradKernel() = default;

 private:
  using user_op::OpKernel::Compute;
  void Compute(user_op::KernelComputeContext* ctx) const override {
    const user_op::Tensor* dy = ctx->Tensor4ArgNameAndIndex("dy", 0);
    user_op::Tensor* dx = ctx->Tensor4ArgNameAndIndex("dx", 0);
    const auto size = ctx->Attr<std::vector<int32_t>>("size");
    const auto stride = ctx->Attr<std::vector<int32_t>>("stride");
    const int32_t storage_offset = ctx->Attr<int32_t>("storage_offset");

    size_t dy_num_dims = dy->shape_view().NumAxes();
    const int64_t* dy_dims = dy->shape_view().ptr();
    const size_t dx_num = dx->shape_view().Count(0);
    const size_t dy_num = dy->shape_view().Count(0);

    Memset<DeviceType::kCPU>(ctx->stream(), dx->mut_dptr(), 0,
                             dx->shape_view().Count(0) * sizeof(T));

    AsStridedGradFunctor<T>()(ctx->stream(), dy->dptr<T>(), dx->mut_dptr<T>(), dy_dims,
                              stride.data(), dy_num_dims, storage_offset, dx_num, dy_num);
  }
  bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; }
};

#define REGISTER_CPUASSTRIDED_KERNEL(in_type)                                                 \
  REGISTER_USER_KERNEL("as_strided")                                                          \
      .SetCreateFn<CpuAsStridedKernel<in_type>>()                                             \
      .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCPU)                         \
                       && (user_op::HobDataType("input", 0) == GetDataType<in_type>::value)); \
  REGISTER_USER_KERNEL("as_strided_grad")                                                     \
      .SetCreateFn<CpuAsStridedGradKernel<in_type>>()                                         \
      .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCPU)                         \
                       && (user_op::HobDataType("input", 0) == GetDataType<in_type>::value));

REGISTER_CPUASSTRIDED_KERNEL(float);
REGISTER_CPUASSTRIDED_KERNEL(double);
REGISTER_CPUASSTRIDED_KERNEL(bool);
REGISTER_CPUASSTRIDED_KERNEL(int8_t);
REGISTER_CPUASSTRIDED_KERNEL(int32_t);
REGISTER_CPUASSTRIDED_KERNEL(int64_t);

#undef REGISTER_CPUASSTRIDED_KERNEL

}  // namespace oneflow