arima_common.h

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/*
 * Copyright (c) 2020-2024, NVIDIA CORPORATION.
 *
 * 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.
 */
 
#pragma once
 
#include <raft/util/cudart_utils.hpp>
 
#include <rmm/mr/device/per_device_resource.hpp>
 
#include <cuda_runtime.h>
#include <thrust/execution_policy.h>
#include <thrust/for_each.h>
#include <thrust/iterator/counting_iterator.h>
 
#include <algorithm>
 
namespace ML {
 
struct ARIMAOrder {
  int p;  // Basic order
  int d;
  int q;
  int P;  // Seasonal order
  int D;
  int Q;
  int s;       // Seasonal period
  int k;       // Fit intercept?
  int n_exog;  // Number of exogenous regressors
 
  inline int n_diff() const { return d + s * D; }
  inline int n_phi() const { return p + s * P; }
  inline int n_theta() const { return q + s * Q; }
  inline int r() const { return std::max(n_phi(), n_theta() + 1); }
  inline int rd() const { return n_diff() + r(); }
  inline int complexity() const { return p + P + q + Q + k + n_exog + 1; }
  inline bool need_diff() const { return static_cast<bool>(d + D); }
};
 
template <typename DataT>
struct ARIMAParams {
  DataT* mu     = nullptr;
  DataT* beta   = nullptr;
  DataT* ar     = nullptr;
  DataT* ma     = nullptr;
  DataT* sar    = nullptr;
  DataT* sma    = nullptr;
  DataT* sigma2 = nullptr;
 
  void allocate(const ARIMAOrder& order, int batch_size, cudaStream_t stream, bool tr = false)
  {
    rmm::mr::device_memory_resource* rmm_alloc = rmm::mr::get_current_device_resource();
    if (order.k && !tr) mu = (DataT*)rmm_alloc->allocate(batch_size * sizeof(DataT), stream);
    if (order.n_exog && !tr)
      beta = (DataT*)rmm_alloc->allocate(order.n_exog * batch_size * sizeof(DataT), stream);
    if (order.p) ar = (DataT*)rmm_alloc->allocate(order.p * batch_size * sizeof(DataT), stream);
    if (order.q) ma = (DataT*)rmm_alloc->allocate(order.q * batch_size * sizeof(DataT), stream);
    if (order.P) sar = (DataT*)rmm_alloc->allocate(order.P * batch_size * sizeof(DataT), stream);
    if (order.Q) sma = (DataT*)rmm_alloc->allocate(order.Q * batch_size * sizeof(DataT), stream);
    sigma2 = (DataT*)rmm_alloc->allocate(batch_size * sizeof(DataT), stream);
  }
 
  void deallocate(const ARIMAOrder& order, int batch_size, cudaStream_t stream, bool tr = false)
  {
    rmm::mr::device_memory_resource* rmm_alloc = rmm::mr::get_current_device_resource();
    if (order.k && !tr) rmm_alloc->deallocate(mu, batch_size * sizeof(DataT), stream);
    if (order.n_exog && !tr)
      rmm_alloc->deallocate(beta, order.n_exog * batch_size * sizeof(DataT), stream);
    if (order.p) rmm_alloc->deallocate(ar, order.p * batch_size * sizeof(DataT), stream);
    if (order.q) rmm_alloc->deallocate(ma, order.q * batch_size * sizeof(DataT), stream);
    if (order.P) rmm_alloc->deallocate(sar, order.P * batch_size * sizeof(DataT), stream);
    if (order.Q) rmm_alloc->deallocate(sma, order.Q * batch_size * sizeof(DataT), stream);
    rmm_alloc->deallocate(sigma2, batch_size * sizeof(DataT), stream);
  }
 
  void pack(const ARIMAOrder& order, int batch_size, DataT* param_vec, cudaStream_t stream) const
  {
    int N         = order.complexity();
    auto counting = thrust::make_counting_iterator(0);
    // The device lambda can't capture structure members...
    const DataT *_mu = mu, *_beta = beta, *_ar = ar, *_ma = ma, *_sar = sar, *_sma = sma,
                *_sigma2 = sigma2;
    thrust::for_each(
      thrust::cuda::par.on(stream), counting, counting + batch_size, [=] __device__(int bid) {
        DataT* param = param_vec + bid * N;
        if (order.k) {
          *param = _mu[bid];
          param++;
        }
        for (int i = 0; i < order.n_exog; i++) {
          param[i] = _beta[order.n_exog * bid + i];
        }
        param += order.n_exog;
        for (int ip = 0; ip < order.p; ip++) {
          param[ip] = _ar[order.p * bid + ip];
        }
        param += order.p;
        for (int iq = 0; iq < order.q; iq++) {
          param[iq] = _ma[order.q * bid + iq];
        }
        param += order.q;
        for (int iP = 0; iP < order.P; iP++) {
          param[iP] = _sar[order.P * bid + iP];
        }
        param += order.P;
        for (int iQ = 0; iQ < order.Q; iQ++) {
          param[iQ] = _sma[order.Q * bid + iQ];
        }
        param += order.Q;
        *param = _sigma2[bid];
      });
  }
 
  void unpack(const ARIMAOrder& order, int batch_size, const DataT* param_vec, cudaStream_t stream)
  {
    int N         = order.complexity();
    auto counting = thrust::make_counting_iterator(0);
    // The device lambda can't capture structure members...
    DataT *_mu = mu, *_beta = beta, *_ar = ar, *_ma = ma, *_sar = sar, *_sma = sma,
          *_sigma2 = sigma2;
    thrust::for_each(
      thrust::cuda::par.on(stream), counting, counting + batch_size, [=] __device__(int bid) {
        const DataT* param = param_vec + bid * N;
        if (order.k) {
          _mu[bid] = *param;
          param++;
        }
        for (int i = 0; i < order.n_exog; i++) {
          _beta[order.n_exog * bid + i] = param[i];
        }
        param += order.n_exog;
        for (int ip = 0; ip < order.p; ip++) {
          _ar[order.p * bid + ip] = param[ip];
        }
        param += order.p;
        for (int iq = 0; iq < order.q; iq++) {
          _ma[order.q * bid + iq] = param[iq];
        }
        param += order.q;
        for (int iP = 0; iP < order.P; iP++) {
          _sar[order.P * bid + iP] = param[iP];
        }
        param += order.P;
        for (int iQ = 0; iQ < order.Q; iQ++) {
          _sma[order.Q * bid + iQ] = param[iQ];
        }
        param += order.Q;
        _sigma2[bid] = *param;
      });
  }
};
 
template <typename T, int ALIGN = 256>
struct ARIMAMemory {
  T *params_mu, *params_beta, *params_ar, *params_ma, *params_sar, *params_sma, *params_sigma2,
    *Tparams_ar, *Tparams_ma, *Tparams_sar, *Tparams_sma, *Tparams_sigma2, *d_params, *d_Tparams,
    *Z_dense, *R_dense, *T_dense, *RQR_dense, *RQ_dense, *P_dense, *alpha_dense, *ImT_dense,
    *ImT_inv_dense, *v_tmp_dense, *m_tmp_dense, *K_dense, *TP_dense, *pred, *y_diff, *exog_diff,
    *loglike, *loglike_base, *loglike_pert, *x_pert, *I_m_AxA_dense, *I_m_AxA_inv_dense, *Ts_dense,
    *RQRs_dense, *Ps_dense;
  T **Z_batches, **R_batches, **T_batches, **RQR_batches, **RQ_batches, **P_batches,
    **alpha_batches, **ImT_batches, **ImT_inv_batches, **v_tmp_batches, **m_tmp_batches,
    **K_batches, **TP_batches, **I_m_AxA_batches, **I_m_AxA_inv_batches, **Ts_batches,
    **RQRs_batches, **Ps_batches;
  int *ImT_inv_P, *ImT_inv_info, *I_m_AxA_P, *I_m_AxA_info;
 
  size_t size;
 
 protected:
  char* buf;
 
  template <bool assign, typename ValType>
  inline void append_buffer(ValType*& ptr, size_t n_elem)
  {
    if (assign) { ptr = reinterpret_cast<ValType*>(buf + size); }
    size += ((n_elem * sizeof(ValType) + ALIGN - 1) / ALIGN) * ALIGN;
  }
 
  template <bool assign>
  inline void buf_offsets(const ARIMAOrder& order,
                          int batch_size,
                          int n_obs,
                          char* in_buf = nullptr)
  {
    buf  = in_buf;
    size = 0;
 
    int r      = order.r();
    int rd     = order.rd();
    int N      = order.complexity();
    int n_diff = order.n_diff();
 
    append_buffer<assign>(params_mu, order.k * batch_size);
    append_buffer<assign>(params_beta, order.n_exog * batch_size);
    append_buffer<assign>(params_ar, order.p * batch_size);
    append_buffer<assign>(params_ma, order.q * batch_size);
    append_buffer<assign>(params_sar, order.P * batch_size);
    append_buffer<assign>(params_sma, order.Q * batch_size);
    append_buffer<assign>(params_sigma2, batch_size);
 
    append_buffer<assign>(Tparams_ar, order.p * batch_size);
    append_buffer<assign>(Tparams_ma, order.q * batch_size);
    append_buffer<assign>(Tparams_sar, order.P * batch_size);
    append_buffer<assign>(Tparams_sma, order.Q * batch_size);
    append_buffer<assign>(Tparams_sigma2, batch_size);
 
    append_buffer<assign>(d_params, N * batch_size);
    append_buffer<assign>(d_Tparams, N * batch_size);
    append_buffer<assign>(Z_dense, rd * batch_size);
    append_buffer<assign>(Z_batches, batch_size);
    append_buffer<assign>(R_dense, rd * batch_size);
    append_buffer<assign>(R_batches, batch_size);
    append_buffer<assign>(T_dense, rd * rd * batch_size);
    append_buffer<assign>(T_batches, batch_size);
    append_buffer<assign>(RQ_dense, rd * batch_size);
    append_buffer<assign>(RQ_batches, batch_size);
    append_buffer<assign>(RQR_dense, rd * rd * batch_size);
    append_buffer<assign>(RQR_batches, batch_size);
    append_buffer<assign>(P_dense, rd * rd * batch_size);
    append_buffer<assign>(P_batches, batch_size);
    append_buffer<assign>(alpha_dense, rd * batch_size);
    append_buffer<assign>(alpha_batches, batch_size);
    append_buffer<assign>(ImT_dense, r * r * batch_size);
    append_buffer<assign>(ImT_batches, batch_size);
    append_buffer<assign>(ImT_inv_dense, r * r * batch_size);
    append_buffer<assign>(ImT_inv_batches, batch_size);
    append_buffer<assign>(ImT_inv_P, r * batch_size);
    append_buffer<assign>(ImT_inv_info, batch_size);
    append_buffer<assign>(v_tmp_dense, rd * batch_size);
    append_buffer<assign>(v_tmp_batches, batch_size);
    append_buffer<assign>(m_tmp_dense, rd * rd * batch_size);
    append_buffer<assign>(m_tmp_batches, batch_size);
    append_buffer<assign>(K_dense, rd * batch_size);
    append_buffer<assign>(K_batches, batch_size);
    append_buffer<assign>(TP_dense, rd * rd * batch_size);
    append_buffer<assign>(TP_batches, batch_size);
 
    append_buffer<assign>(pred, n_obs * batch_size);
    append_buffer<assign>(y_diff, n_obs * batch_size);
    append_buffer<assign>(exog_diff, n_obs * order.n_exog * batch_size);
    append_buffer<assign>(loglike, batch_size);
    append_buffer<assign>(loglike_base, batch_size);
    append_buffer<assign>(loglike_pert, batch_size);
    append_buffer<assign>(x_pert, N * batch_size);
 
    if (n_diff > 0) {
      append_buffer<assign>(Ts_dense, r * r * batch_size);
      append_buffer<assign>(Ts_batches, batch_size);
      append_buffer<assign>(RQRs_dense, r * r * batch_size);
      append_buffer<assign>(RQRs_batches, batch_size);
      append_buffer<assign>(Ps_dense, r * r * batch_size);
      append_buffer<assign>(Ps_batches, batch_size);
    }
 
    if (r <= 5) {
      // Note: temp mem for the direct Lyapunov solver grows very quickly!
      // This solver is used iff the condition above is satisfied
      append_buffer<assign>(I_m_AxA_dense, r * r * r * r * batch_size);
      append_buffer<assign>(I_m_AxA_batches, batch_size);
      append_buffer<assign>(I_m_AxA_inv_dense, r * r * r * r * batch_size);
      append_buffer<assign>(I_m_AxA_inv_batches, batch_size);
      append_buffer<assign>(I_m_AxA_P, r * r * batch_size);
      append_buffer<assign>(I_m_AxA_info, batch_size);
    }
  }
 
  ARIMAMemory(const ARIMAOrder& order, int batch_size, int n_obs)
  {
    buf_offsets<false>(order, batch_size, n_obs);
  }
 
 public:
  ARIMAMemory(const ARIMAOrder& order, int batch_size, int n_obs, char* in_buf)
  {
    buf_offsets<true>(order, batch_size, n_obs, in_buf);
  }
 
  static size_t compute_size(const ARIMAOrder& order, int batch_size, int n_obs)
  {
    ARIMAMemory temp(order, batch_size, n_obs);
    return temp.size;
  }
};
 
}  // namespace ML