/*************************************************************************
 * Copyright (c) 2022-2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 *
 * See LICENSE for license information.
 ************************************************************************/

#include <ATen/ATen.h>
#include <ATen/AccumulateType.h>
#include <ATen/cuda/CUDAContext.h>
#include <ATen/cuda/Exceptions.h>
// Another possibility:
// #include <torch/all.h>

#include <assert.h>

#include "multi_tensor_apply.cuh"
#include "type_shim.h"

#define BLOCK_SIZE 512
#define ILP 4

typedef enum {
  ADAM_MODE_0 = 0,  // L2 regularization mode
  ADAM_MODE_1 = 1   // Decoupled weight decay mode(AdamW)
} adamMode_t;

using MATH_T = float;

template <typename T, typename FULL_T, typename index_t>
struct AdamFunctor {
  __device__ __forceinline__ void operator()(index_t chunk_size, volatile int *noop_gmem,
                                             TensorListMetadata<4> &tl,  // NOLINT(*)
                                             const float beta1, const float beta2,
                                             const float beta1_correction,
                                             const float beta2_correction, const float epsilon,
                                             const float lr, adamMode_t mode, const float decay) {
    // I'd like this kernel to propagate infs/nans.
    // if(*noop_gmem == 1)
    //   return;

    index_t tensor_loc = tl.block_to_tensor[blockIdx.x];

    // potentially use to pass in list of scalar
    // int tensor_num = tl.start_tensor_this_launch + tensor_loc;

    index_t chunk_idx = tl.block_to_chunk[blockIdx.x];
    index_t n = tl.sizes[tensor_loc];

    T *g = reinterpret_cast<T *>(tl.addresses[0][tensor_loc]);
    g += chunk_idx * chunk_size;

    T *p = reinterpret_cast<T *>(tl.addresses[1][tensor_loc]);
    p += chunk_idx * chunk_size;

    FULL_T *m = reinterpret_cast<FULL_T *>(tl.addresses[2][tensor_loc]);
    m += chunk_idx * chunk_size;

    FULL_T *v = reinterpret_cast<FULL_T *>(tl.addresses[3][tensor_loc]);
    v += chunk_idx * chunk_size;

    n -= chunk_idx * chunk_size;

    // see note in multi_tensor_scale_kernel.cu
    for (index_t i_start = 0; i_start < n && i_start < chunk_size; i_start += blockDim.x * ILP) {
      MATH_T r_g[ILP];
      MATH_T r_p[ILP];
      MATH_T r_m[ILP];
      MATH_T r_v[ILP];
#pragma unroll
      for (int ii = 0; ii < ILP; ii++) {
        int i = i_start + threadIdx.x + ii * blockDim.x;
        if (i < n && i < chunk_size) {
          r_g[ii] = g[i];
          r_p[ii] = p[i];
          r_m[ii] = m[i];
          r_v[ii] = v[i];
        } else {
          r_g[ii] = MATH_T(0);
          r_p[ii] = MATH_T(0);
          r_m[ii] = MATH_T(0);
          r_v[ii] = MATH_T(0);
        }
      }
#pragma unroll
      for (int ii = 0; ii < ILP; ii++) {
        if (mode == ADAM_MODE_0) {  // L2
          r_g[ii] = r_g[ii] + (decay * r_p[ii]);
          r_m[ii] = beta1 * r_m[ii] + (1 - beta1) * r_g[ii];
          r_v[ii] = beta2 * r_v[ii] + (1 - beta2) * r_g[ii] * r_g[ii];
          MATH_T next_m_unbiased = r_m[ii] / beta1_correction;
          MATH_T next_v_unbiased = r_v[ii] / beta2_correction;
          MATH_T denom = sqrtf(next_v_unbiased) + epsilon;
          MATH_T update = next_m_unbiased / denom;
          r_p[ii] = r_p[ii] - (lr * update);
        } else {  // weight decay
          r_m[ii] = beta1 * r_m[ii] + (1 - beta1) * r_g[ii];
          r_v[ii] = beta2 * r_v[ii] + (1 - beta2) * r_g[ii] * r_g[ii];
          MATH_T next_m_unbiased = r_m[ii] / beta1_correction;
          MATH_T next_v_unbiased = r_v[ii] / beta2_correction;
          MATH_T denom = sqrtf(next_v_unbiased) + epsilon;
          MATH_T update = (next_m_unbiased / denom) + (decay * r_p[ii]);
          r_p[ii] = r_p[ii] - (lr * update);
        }
      }
#pragma unroll
      for (int ii = 0; ii < ILP; ii++) {
        int i = i_start + threadIdx.x + ii * blockDim.x;
        if (i < n && i < chunk_size) {
          p[i] = r_p[ii];
          m[i] = r_m[ii];
          v[i] = r_v[ii];
        }
      }
    }
  }
};

template <typename T, typename FULL_T>
struct AdamCapturableFunctor {
  __device__ __forceinline__ void operator()(int chunk_size, volatile int *noop_gmem,
                                             TensorListMetadata<4> &tl,  // NOLINT(*)
                                             const float beta1, const float beta2, const int *step,
                                             const int bias_correction, const float epsilon,
                                             const float *lr, adamMode_t mode, const float decay,
                                             const float *inv_scale) {
    if (*noop_gmem == 1) return;

    float beta1_correction = 1.0f, beta2_correction = 1.0f;
    if (bias_correction == 1) {
      beta1_correction = 1 - pow(beta1, *step);
      beta2_correction = 1 - pow(beta2, *step);
    }

    int tensor_loc = tl.block_to_tensor[blockIdx.x];

    // potentially use to pass in list of scalar
    // int tensor_num = tl.start_tensor_this_launch + tensor_loc;

    int chunk_idx = tl.block_to_chunk[blockIdx.x];
    int n = tl.sizes[tensor_loc];

    T *g = reinterpret_cast<T *>(tl.addresses[0][tensor_loc]);
    g += chunk_idx * chunk_size;

    T *p = reinterpret_cast<T *>(tl.addresses[1][tensor_loc]);
    p += chunk_idx * chunk_size;

    FULL_T *m = reinterpret_cast<FULL_T *>(tl.addresses[2][tensor_loc]);
    m += chunk_idx * chunk_size;

    FULL_T *v = reinterpret_cast<FULL_T *>(tl.addresses[3][tensor_loc]);
    v += chunk_idx * chunk_size;

    n -= chunk_idx * chunk_size;

    // see note in multi_tensor_scale_kernel.cu
    for (int i_start = 0; i_start < n && i_start < chunk_size; i_start += blockDim.x * ILP) {
      MATH_T r_g[ILP];
      MATH_T r_p[ILP];
      MATH_T r_m[ILP];
      MATH_T r_v[ILP];
#pragma unroll
      for (int ii = 0; ii < ILP; ii++) {
        int i = i_start + threadIdx.x + ii * blockDim.x;
        if (i < n && i < chunk_size) {
          r_g[ii] = static_cast<MATH_T>(g[i]) * (*inv_scale);
          g[i] = static_cast<T>(r_g[ii]);
          r_p[ii] = static_cast<MATH_T>(p[i]);
          r_m[ii] = static_cast<MATH_T>(m[i]);
          r_v[ii] = static_cast<MATH_T>(v[i]);
        } else {
          r_g[ii] = MATH_T(0);
          r_p[ii] = MATH_T(0);
          r_m[ii] = MATH_T(0);
          r_v[ii] = MATH_T(0);
        }
      }
#pragma unroll
      for (int ii = 0; ii < ILP; ii++) {
        if (mode == ADAM_MODE_0) {  // L2
          r_g[ii] = r_g[ii] + (decay * r_p[ii]);
          r_m[ii] = beta1 * r_m[ii] + (1 - beta1) * r_g[ii];
          r_v[ii] = beta2 * r_v[ii] + (1 - beta2) * r_g[ii] * r_g[ii];
          MATH_T next_m_unbiased = r_m[ii] / beta1_correction;
          MATH_T next_v_unbiased = r_v[ii] / beta2_correction;
          MATH_T denom = sqrtf(next_v_unbiased) + epsilon;
          MATH_T update = next_m_unbiased / denom;
          r_p[ii] = r_p[ii] - (*lr * update);
        } else {  // weight decay
          r_m[ii] = beta1 * r_m[ii] + (1 - beta1) * r_g[ii];
          r_v[ii] = beta2 * r_v[ii] + (1 - beta2) * r_g[ii] * r_g[ii];
          MATH_T next_m_unbiased = r_m[ii] / beta1_correction;
          MATH_T next_v_unbiased = r_v[ii] / beta2_correction;
          MATH_T denom = sqrtf(next_v_unbiased) + epsilon;
          MATH_T update = (next_m_unbiased / denom) + (decay * r_p[ii]);
          r_p[ii] = r_p[ii] - (*lr * update);
        }
      }
#pragma unroll
      for (int ii = 0; ii < ILP; ii++) {
        int i = i_start + threadIdx.x + ii * blockDim.x;
        if (i < n && i < chunk_size) {
          p[i] = static_cast<T>(r_p[ii]);
          m[i] = static_cast<T>(r_m[ii]);
          v[i] = static_cast<T>(r_v[ii]);
        }
      }
    }
  }
};

template <typename T, typename FULL_T>
struct AdamCapturableMasterFunctor {
  __device__ __forceinline__ void operator()(int chunk_size, volatile int *noop_gmem,
                                             TensorListMetadata<5> &tl,  // NOLINT(*)
                                             const float beta1, const float beta2, const int *step,
                                             const int bias_correction, const float epsilon,
                                             const float *lr, adamMode_t mode, const float decay,
                                             const float *inv_scale) {
    if (*noop_gmem == 1) return;

    float beta1_correction = 1.0f, beta2_correction = 1.0f;
    if (bias_correction == 1) {
      beta1_correction = 1 - pow(beta1, *step);
      beta2_correction = 1 - pow(beta2, *step);
    }

    int tensor_loc = tl.block_to_tensor[blockIdx.x];

    // potentially use to pass in list of scalar
    // int tensor_num = tl.start_tensor_this_launch + tensor_loc;

    int chunk_idx = tl.block_to_chunk[blockIdx.x];
    int n = tl.sizes[tensor_loc];

    T *g = reinterpret_cast<T *>(tl.addresses[0][tensor_loc]);
    g += chunk_idx * chunk_size;

    T *p = reinterpret_cast<T *>(tl.addresses[1][tensor_loc]);
    p += chunk_idx * chunk_size;

    FULL_T *m = reinterpret_cast<FULL_T *>(tl.addresses[2][tensor_loc]);
    m += chunk_idx * chunk_size;

    FULL_T *v = reinterpret_cast<FULL_T *>(tl.addresses[3][tensor_loc]);
    v += chunk_idx * chunk_size;

    FULL_T *p_master = reinterpret_cast<FULL_T *>(tl.addresses[4][tensor_loc]);
    p_master += chunk_idx * chunk_size;

    n -= chunk_idx * chunk_size;

    // see note in multi_tensor_scale_kernel.cu
    for (int i_start = 0; i_start < n && i_start < chunk_size; i_start += blockDim.x * ILP) {
      MATH_T r_g[ILP];
      MATH_T r_p[ILP];
      MATH_T r_m[ILP];
      MATH_T r_v[ILP];
#pragma unroll
      for (int ii = 0; ii < ILP; ii++) {
        int i = i_start + threadIdx.x + ii * blockDim.x;
        if (i < n && i < chunk_size) {
          r_g[ii] = static_cast<MATH_T>(g[i]) * (*inv_scale);
          g[i] = static_cast<T>(r_g[ii]);
          r_p[ii] = static_cast<MATH_T>(p_master[i]);
          r_m[ii] = static_cast<MATH_T>(m[i]);
          r_v[ii] = static_cast<MATH_T>(v[i]);
        } else {
          r_g[ii] = MATH_T(0);
          r_p[ii] = MATH_T(0);
          r_m[ii] = MATH_T(0);
          r_v[ii] = MATH_T(0);
        }
      }
#pragma unroll
      for (int ii = 0; ii < ILP; ii++) {
        if (mode == ADAM_MODE_0) {  // L2
          r_g[ii] = r_g[ii] + (decay * r_p[ii]);
          r_m[ii] = beta1 * r_m[ii] + (1 - beta1) * r_g[ii];
          r_v[ii] = beta2 * r_v[ii] + (1 - beta2) * r_g[ii] * r_g[ii];
          MATH_T next_m_unbiased = r_m[ii] / beta1_correction;
          MATH_T next_v_unbiased = r_v[ii] / beta2_correction;
          MATH_T denom = sqrtf(next_v_unbiased) + epsilon;
          MATH_T update = next_m_unbiased / denom;
          r_p[ii] = r_p[ii] - (*lr * update);
        } else {  // weight decay
          r_m[ii] = beta1 * r_m[ii] + (1 - beta1) * r_g[ii];
          r_v[ii] = beta2 * r_v[ii] + (1 - beta2) * r_g[ii] * r_g[ii];
          MATH_T next_m_unbiased = r_m[ii] / beta1_correction;
          MATH_T next_v_unbiased = r_v[ii] / beta2_correction;
          MATH_T denom = sqrtf(next_v_unbiased) + epsilon;
          MATH_T update = (next_m_unbiased / denom) + (decay * r_p[ii]);
          r_p[ii] = r_p[ii] - (*lr * update);
        }
      }
#pragma unroll
      for (int ii = 0; ii < ILP; ii++) {
        int i = i_start + threadIdx.x + ii * blockDim.x;
        if (i < n && i < chunk_size) {
          p[i] = static_cast<T>(r_p[ii]);
          p_master[i] = static_cast<FULL_T>(r_p[ii]);
          m[i] = static_cast<FULL_T>(r_m[ii]);
          v[i] = static_cast<FULL_T>(r_v[ii]);
        }
      }
    }
  }
};

void multi_tensor_adam_cuda(int chunk_size, at::Tensor noop_flag,
                            std::vector<std::vector<at::Tensor>> tensor_lists, const float lr,
                            const float beta1, const float beta2, const float epsilon,
                            const int step, const int mode, const int bias_correction,
                            const float weight_decay) {
  using namespace at;

  // Handle bias correction mode
  float bias_correction1 = 1.0f, bias_correction2 = 1.0f;
  if (bias_correction == 1) {
    bias_correction1 = 1 - std::pow(beta1, step);
    bias_correction2 = 1 - std::pow(beta2, step);
  }

  size_t max_size = 0;
  bool requires_64bit_indexing = false;
  for (auto it = tensor_lists.begin(); it != tensor_lists.end(); it++) {
    for (auto it2 = it->begin(); it2 != it->end(); it2++) {
      if (it2->numel() > max_size) {
        max_size = it2->numel();
        if (max_size >= INT_MAX) {
          requires_64bit_indexing = true;
          break;
        }
      }
    }
    if (requires_64bit_indexing) {
      break;
    }
  }

  if (requires_64bit_indexing) {
    // Assume single type across p,g,m1,m2 now
    DISPATCH_DOUBLE_FLOAT_HALF_AND_BFLOAT(
        tensor_lists[0][0].scalar_type(), 0, "adam",
        multi_tensor_apply<4>((int64_t)BLOCK_SIZE, (int64_t)chunk_size, noop_flag, tensor_lists,
                              AdamFunctor<scalar_t_0, float, int64_t>(), beta1, beta2,
                              bias_correction1, bias_correction2, epsilon, lr, (adamMode_t)mode,
                              weight_decay);)
  } else {
    // Assume single type across p,g,m1,m2 now
    DISPATCH_DOUBLE_FLOAT_HALF_AND_BFLOAT(
        tensor_lists[0][0].scalar_type(), 0, "adam",
        multi_tensor_apply<4>(BLOCK_SIZE, chunk_size, noop_flag, tensor_lists,
                              AdamFunctor<scalar_t_0, float, int32_t>(), beta1, beta2,
                              bias_correction1, bias_correction2, epsilon, lr, (adamMode_t)mode,
                              weight_decay);)
  }
  AT_CUDA_CHECK(cudaGetLastError());
}

void multi_tensor_adam_capturable_cuda(int chunk_size, at::Tensor noop_flag,
                                       std::vector<std::vector<at::Tensor>> tensor_lists,
                                       at::Tensor lr, const float beta1, const float beta2,
                                       const float epsilon, at::Tensor step, const int mode,
                                       const int bias_correction, const float weight_decay,
                                       at::Tensor inv_scale) {
  using namespace at;

  DISPATCH_DOUBLE_FLOAT_HALF_AND_BFLOAT(
      tensor_lists[0][0].scalar_type(), 0, "adam",
      multi_tensor_apply<4>(BLOCK_SIZE, chunk_size, noop_flag, tensor_lists,
                            AdamCapturableFunctor<scalar_t_0, float>(), beta1, beta2,
                            step.data_ptr<int>(), bias_correction, epsilon, lr.data_ptr<float>(),
                            (adamMode_t)mode, weight_decay, inv_scale.data_ptr<float>());)

  AT_CUDA_CHECK(cudaGetLastError());
}

void multi_tensor_adam_capturable_master_cuda(int chunk_size, at::Tensor noop_flag,
                                              std::vector<std::vector<at::Tensor>> tensor_lists,
                                              at::Tensor lr, const float beta1, const float beta2,
                                              const float epsilon, at::Tensor step, const int mode,
                                              const int bias_correction, const float weight_decay,
                                              at::Tensor inv_scale) {
  using namespace at;

  DISPATCH_DOUBLE_FLOAT_HALF_AND_BFLOAT(
      tensor_lists[0][0].scalar_type(), 0, "adam",
      multi_tensor_apply<5>(BLOCK_SIZE, chunk_size, noop_flag, tensor_lists,
                            AdamCapturableMasterFunctor<scalar_t_0, float>(), beta1, beta2,
                            step.data_ptr<int>(), bias_correction, epsilon, lr.data_ptr<float>(),
                            (adamMode_t)mode, weight_decay, inv_scale.data_ptr<float>());)

  AT_CUDA_CHECK(cudaGetLastError());
}
