/* 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. */

#pragma once
#include <string>

#include "paddle/fluid/framework/op_registry.h"
#include "paddle/phi/kernels/funcs/blas/blas.h"
#include "paddle/phi/kernels/funcs/detail/activation_functions.h"
#include "paddle/phi/kernels/funcs/lstm_compute.h"
#include "paddle/phi/kernels/funcs/sequence2batch.h"

namespace paddle {
namespace operators {

template <typename DeviceContext, typename T>
inline void ReorderInitState(const DeviceContext& ctx,
                             const phi::DenseTensor& src,
                             phi::Vector<size_t> index_lod,
                             phi::DenseTensor* dst,
                             bool indexed_src) {
  phi::funcs::CopyMatrixRowsFunctor<DeviceContext, T> row_shuffle;
  dst->mutable_data<T>(src.dims(), ctx.GetPlace());
  row_shuffle(ctx, src, index_lod, dst, indexed_src);
}

template <typename T, typename DeviceContext>
class LSTMKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext& ctx) const override {
    bool is_test = ctx.Attr<bool>("is_test");

    auto* input = ctx.Input<phi::DenseTensor>("Input");
    auto* weight = ctx.Input<phi::DenseTensor>("Weight");
    auto* bias = ctx.Input<phi::DenseTensor>("Bias");

    auto* hidden_t0 = ctx.Input<phi::DenseTensor>("H0");
    auto* cell_t0 = ctx.Input<phi::DenseTensor>("C0");

    phi::DenseTensor* batch_gate = nullptr;
    phi::DenseTensor batch_gate_temp;
    if (is_test) {
      batch_gate = &batch_gate_temp;
      batch_gate->Resize(input->dims());
    } else {
      batch_gate = ctx.Output<phi::DenseTensor>("BatchGate");
    }
    batch_gate->mutable_data<T>(ctx.GetPlace());
    auto* hidden_out = ctx.Output<phi::DenseTensor>("Hidden");
    hidden_out->mutable_data<T>(ctx.GetPlace());
    auto* cell_out = ctx.Output<phi::DenseTensor>("Cell");
    cell_out->mutable_data<T>(ctx.GetPlace());

    bool is_reverse = ctx.Attr<bool>("is_reverse");
    phi::funcs::LoDTensor2BatchFunctor<DeviceContext, T> to_batch;
    auto& device_ctx = ctx.template device_context<DeviceContext>();
    to_batch(device_ctx, *input, batch_gate, true, is_reverse);

    auto in_dims = input->dims();
    int frame_size = static_cast<int>(in_dims[1] / 4);
    framework::DDim dims({in_dims[0], frame_size});

    if (bias) {
      phi::DenseTensor b = *bias;
      b.Resize({bias->numel(), 1});
      phi::DenseTensor gate_bias = b.Slice(0, 4 * frame_size);
      phi::funcs::RowwiseAdd<DeviceContext, T> add_bias;
      add_bias(device_ctx, *batch_gate, gate_bias, batch_gate);
    }

    phi::funcs::LstmMetaValue<T> lstm_value;
    if (bias && ctx.Attr<bool>("use_peepholes")) {
      T* bias_data = const_cast<T*>(bias->data<T>());
      // the code style in LstmMetaValue will be updated later.

      lstm_value.check_ig = bias_data + 4 * frame_size;
      lstm_value.check_fg = lstm_value.check_ig + frame_size;
      lstm_value.check_og = lstm_value.check_fg + frame_size;
    } else {
      lstm_value.check_ig = nullptr;
      lstm_value.check_fg = nullptr;
      lstm_value.check_og = nullptr;
    }
    lstm_value.prev_state_value = nullptr;
    phi::DenseTensor ordered_c0;

    phi::Vector<size_t> order(batch_gate->lod()[2]);

    if (cell_t0) {
      // Since the batch computing for LSTM reorders the input sequence
      // according to their length. The initialized cell state also needs
      // to reorder.
      ReorderInitState<DeviceContext, T>(
          device_ctx, *cell_t0, order, &ordered_c0, true);
      lstm_value.prev_state_value = ordered_c0.data<T>();
    }

    // Use the local variable as here.
    phi::DenseTensor batch_hidden, batch_cell, batch_cell_pre_act_temp;
    phi::DenseTensor* batch_cell_pre_act;
    if (is_test) {
      batch_cell_pre_act = &batch_cell_pre_act_temp;
    } else {
      batch_cell_pre_act = ctx.Output<phi::DenseTensor>("BatchCellPreAct");
    }
    batch_hidden.mutable_data<T>(dims, ctx.GetPlace());
    batch_cell.mutable_data<T>(dims, ctx.GetPlace());
    batch_cell_pre_act->mutable_data<T>(dims, ctx.GetPlace());

    auto batch_starts = batch_gate->lod()[0];
    size_t num_batch = batch_starts.size() - 1;
    auto gate_act = phi::funcs::detail::GetActivationType(
        ctx.Attr<std::string>("gate_activation"));
    auto cell_act = phi::funcs::detail::GetActivationType(
        ctx.Attr<std::string>("cell_activation"));
    auto cand_act = phi::funcs::detail::GetActivationType(
        ctx.Attr<std::string>("candidate_activation"));

    auto blas = phi::funcs::GetBlas<DeviceContext, T>(device_ctx);
    for (size_t n = 0; n < num_batch; n++) {
      int bstart = static_cast<int>(batch_starts[n]);
      int bend = static_cast<int>(batch_starts[n + 1]);

      phi::DenseTensor gate_t = batch_gate->Slice(bstart, bend);
      phi::DenseTensor out_t = batch_hidden.Slice(bstart, bend);
      phi::DenseTensor cell_t = batch_cell.Slice(bstart, bend);
      phi::DenseTensor cell_pre_act_t = batch_cell_pre_act->Slice(bstart, bend);

      int cur_batch_size = bend - bstart;

      if (n > 0) {
        int pre_h_start = static_cast<int>(batch_starts[n - 1]);
        int pre_h_end = pre_h_start + cur_batch_size;
        auto pre_hidden_t = batch_hidden.Slice(pre_h_start, pre_h_end);
        blas.MatMul(pre_hidden_t,
                    false,
                    *weight,
                    false,
                    static_cast<T>(1.0),
                    &gate_t,
                    static_cast<T>(1.0));
      } else if (hidden_t0) {
        // If n == 0 and there is no initialized hidden state, that is to say
        // the H0 is zeros, the calculation W_h * H0 will be skiped.
        // If n == 0 and there is initialized hidden state, calculate W_h * H0.

        // Since the batch computing for LSTM reorders the input sequence
        // according to their length. The initialized hidden state also needs
        // to reorder.
        phi::DenseTensor ordered_h0;
        ReorderInitState<DeviceContext, T>(
            device_ctx, *hidden_t0, order, &ordered_h0, true);
        blas.MatMul(ordered_h0,
                    false,
                    *weight,
                    false,
                    static_cast<T>(1.0),
                    &gate_t,
                    static_cast<T>(1.0));
      }

      lstm_value.gate_value = gate_t.data<T>();
      lstm_value.output_value = out_t.data<T>();
      lstm_value.state_value = cell_t.data<T>();
      lstm_value.state_active_value = cell_pre_act_t.data<T>();
      T cell_clip = 0.0;
      phi::funcs::LstmUnitFunctor<DeviceContext, T>::compute(device_ctx,
                                                             lstm_value,
                                                             frame_size,
                                                             cur_batch_size,
                                                             cell_clip,
                                                             gate_act,
                                                             cell_act,
                                                             cand_act);
      lstm_value.prev_state_value = lstm_value.state_value;
    }

    phi::funcs::Batch2LoDTensorFunctor<DeviceContext, T> to_seq;
    batch_hidden.set_lod(batch_gate->lod());
    // restore the output hidden in phi::DenseTensor from the batch hidden
    to_seq(device_ctx, batch_hidden, hidden_out);

    batch_cell.set_lod(batch_gate->lod());
    // restore the output cell state in phi::DenseTensor from the batch cell
    to_seq(device_ctx, batch_cell, cell_out);
  }
};

template <typename T, typename DeviceContext>
class LSTMGradKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext& ctx) const override {
    auto* input = ctx.Input<phi::DenseTensor>("Input");
    auto* weight = ctx.Input<phi::DenseTensor>("Weight");
    auto* bias = ctx.Input<phi::DenseTensor>("Bias");

    auto* hidden_out = ctx.Input<phi::DenseTensor>("Hidden");
    auto* cell_out = ctx.Input<phi::DenseTensor>("Cell");

    auto* batch_gate = ctx.Input<phi::DenseTensor>("BatchGate");
    auto* batch_cell_pre_act = ctx.Input<phi::DenseTensor>("BatchCellPreAct");

    auto* hidden_g =
        ctx.Input<phi::DenseTensor>(framework::GradVarName("Hidden"));

    auto* in_g = ctx.Output<phi::DenseTensor>(framework::GradVarName("Input"));
    auto* weight_g =
        ctx.Output<phi::DenseTensor>(framework::GradVarName("Weight"));
    auto* bias_g = ctx.Output<phi::DenseTensor>(framework::GradVarName("Bias"));

    auto* h0 = ctx.Input<phi::DenseTensor>("H0");
    auto* c0 = ctx.Input<phi::DenseTensor>("C0");

    auto* h0_g = ctx.Output<phi::DenseTensor>(framework::GradVarName("H0"));
    auto* c0_g = ctx.Output<phi::DenseTensor>(framework::GradVarName("C0"));

    auto& device_ctx = ctx.template device_context<DeviceContext>();
    phi::funcs::SetConstant<DeviceContext, T> zero;
    if (weight_g) {
      weight_g->mutable_data<T>(ctx.GetPlace());
      zero(device_ctx, weight_g, static_cast<T>(0.0));
    }

    // ordered_h0/c0 is the reordered hidden/cell initialization.
    // ordered_h0_g/c0_g is the reordered gradient of hidden/cell
    // initialization.
    phi::DenseTensor ordered_h0, ordered_c0, ordered_h0_g, ordered_c0_g;
    phi::Vector<size_t> order(batch_gate->lod()[2]);

    if (c0) {
      ReorderInitState<DeviceContext, T>(
          device_ctx, *c0, order, &ordered_c0, true);
    }
    if (c0 && c0_g) {
      ordered_c0_g.mutable_data<T>(c0_g->dims(), ctx.GetPlace());
    }

    auto in_dims = input->dims();
    auto out_dims = hidden_g->dims();
    int frame_size = static_cast<int>(in_dims[1] / 4);
    PADDLE_ENFORCE_EQ(
        frame_size,
        out_dims[1],
        platform::errors::InvalidArgument(
            "The second dimension of Input(" +
                framework::GradVarName("Hidden") +
                ") should be %d, but received %d in LSTM@Grad operator.",
            frame_size,
            out_dims[1]));

    phi::funcs::LstmMetaValue<T> lstm_value;
    if (bias && ctx.Attr<bool>("use_peepholes")) {
      T* bias_data = const_cast<T*>(bias->data<T>());
      lstm_value.check_ig = bias_data + 4 * frame_size;
      lstm_value.check_fg = lstm_value.check_ig + frame_size;
      lstm_value.check_og = lstm_value.check_fg + frame_size;
    } else {
      lstm_value.check_ig = nullptr;
      lstm_value.check_fg = nullptr;
      lstm_value.check_og = nullptr;
    }

    phi::funcs::LstmMetaGrad<T> lstm_grad;

    if (bias && bias_g) {
      bias_g->mutable_data<T>(ctx.GetPlace());
      zero(device_ctx, bias_g, static_cast<T>(0.0));
    }
    if (bias && bias_g && ctx.Attr<bool>("use_peepholes")) {
      T* bias_g_data = bias_g->data<T>();
      lstm_grad.check_ig_grad = bias_g_data + 4 * frame_size;
      lstm_grad.check_fg_grad = lstm_grad.check_ig_grad + frame_size;
      lstm_grad.check_og_grad = lstm_grad.check_fg_grad + frame_size;
    } else {
      lstm_grad.check_ig_grad = nullptr;
      lstm_grad.check_fg_grad = nullptr;
      lstm_grad.check_og_grad = nullptr;
    }

    phi::funcs::LoDTensor2BatchFunctor<DeviceContext, T> to_batch;

    auto ToBatch = [&batch_gate, &to_batch](const DeviceContext& ctx,
                                            const phi::DenseTensor& src,
                                            const framework::DDim& dims,
                                            phi::DenseTensor& dst) {
      dst.mutable_data<T>(dims, ctx.GetPlace());
      dst.set_lod(batch_gate->lod());
      to_batch(ctx, src, &dst, false);
    };

    phi::DenseTensor batch_hidden, batch_hidden_g, batch_cell;
    ToBatch(device_ctx, *hidden_out, out_dims, batch_hidden);
    ToBatch(device_ctx, *hidden_g, out_dims, batch_hidden_g);
    ToBatch(device_ctx, *cell_out, out_dims, batch_cell);

    phi::DenseTensor batch_cell_g, batch_gate_g;
    batch_cell_g.mutable_data<T>(out_dims, ctx.GetPlace());
    // TODO(qingqing) support the case output cell has gradient.
    // to_batch(device_ctx, *cell_g, batch_cell_g, false);
    zero(device_ctx, &batch_cell_g, static_cast<T>(0.0));
    batch_gate_g.mutable_data<T>(batch_gate->dims(), ctx.GetPlace());
    batch_gate_g.set_lod(batch_gate->lod());

    auto gate_act = phi::funcs::detail::GetActivationType(
        ctx.Attr<std::string>("gate_activation"));
    auto cell_act = phi::funcs::detail::GetActivationType(
        ctx.Attr<std::string>("cell_activation"));
    auto cand_act = phi::funcs::detail::GetActivationType(
        ctx.Attr<std::string>("candidate_activation"));

    auto batch_starts = batch_gate->lod()[0];
    size_t num_batch = batch_starts.size() - 1;
    auto blas = phi::funcs::GetBlas<DeviceContext, T>(device_ctx);
    for (int n = static_cast<int>(num_batch) - 1; n >= 0; n--) {
      int bstart = static_cast<int>(batch_starts[n]);
      int bend = static_cast<int>(batch_starts[n + 1]);

      phi::DenseTensor gate = batch_gate->Slice(bstart, bend);
      phi::DenseTensor cell = batch_cell.Slice(bstart, bend);
      phi::DenseTensor cell_pre_act = batch_cell_pre_act->Slice(bstart, bend);
      lstm_value.gate_value = gate.data<T>();
      lstm_value.state_value = cell.data<T>();
      lstm_value.state_active_value = cell_pre_act.data<T>();

      phi::DenseTensor out_g = batch_hidden_g.Slice(bstart, bend);
      phi::DenseTensor gate_g = batch_gate_g.Slice(bstart, bend);
      phi::DenseTensor cell_g = batch_cell_g.Slice(bstart, bend);
      lstm_grad.state_grad = cell_g.data<T>();
      lstm_grad.gate_grad = gate_g.data<T>();
      lstm_grad.output_grad = out_g.data<T>();

      if (n > 0) {
        int bstart_pre = static_cast<int>(batch_starts[n - 1]);
        phi::DenseTensor cell_pre = batch_cell.Slice(bstart_pre, bstart);
        phi::DenseTensor cell_pre_g = batch_cell_g.Slice(bstart_pre, bstart);
        lstm_value.prev_state_value = cell_pre.data<T>();
        lstm_grad.prev_state_grad = cell_pre_g.data<T>();
      } else {
        lstm_value.prev_state_value = c0 ? ordered_c0.data<T>() : nullptr;
        lstm_grad.prev_state_grad = c0_g ? ordered_c0_g.data<T>() : nullptr;
      }

      // lstm_value.output_value not used in bp, set to nullptr
      // lstm_grad.state_active_grad not used in bp, set to nullptr
      lstm_value.output_value = nullptr;
      lstm_grad.state_active_grad = nullptr;
      int cur_batch_size = bend - bstart;
      T cell_clip = 0.0;
      phi::funcs::LstmUnitGradFunctor<DeviceContext, T>::compute(device_ctx,
                                                                 lstm_value,
                                                                 lstm_grad,
                                                                 frame_size,
                                                                 cur_batch_size,
                                                                 cell_clip,
                                                                 gate_act,
                                                                 cell_act,
                                                                 cand_act);

      if (n > 0) {
        int pre_h_start = static_cast<int>(batch_starts[n - 1]);
        int pre_h_end = pre_h_start + cur_batch_size;
        auto pre_hidden_g = batch_hidden_g.Slice(pre_h_start, pre_h_end);
        blas.MatMul(gate_g,
                    false,
                    *weight,
                    true,
                    static_cast<T>(1.0),
                    &pre_hidden_g,
                    static_cast<T>(1.0));
        if (weight_g) {
          /* backward weight */
          auto pre_hidden = batch_hidden.Slice(pre_h_start, pre_h_end);
          blas.MatMul(pre_hidden,
                      true,
                      gate_g,
                      false,
                      static_cast<T>(1.0),
                      weight_g,
                      static_cast<T>(1.0));
        }
      } else {
        if (h0 && weight_g) {
          ReorderInitState<DeviceContext, T>(
              device_ctx, *h0, order, &ordered_h0, true);
          blas.MatMul(ordered_h0,
                      true,
                      gate_g,
                      false,
                      static_cast<T>(1.0),
                      weight_g,
                      static_cast<T>(1.0));
        }
        if (h0 && h0_g) {
          ordered_h0_g.mutable_data<T>(h0_g->dims(), ctx.GetPlace());
          blas.MatMul(gate_g,
                      false,
                      *weight,
                      true,
                      static_cast<T>(1.0),
                      &ordered_h0_g,
                      static_cast<T>(0.0));
        }
      }
    }

    phi::funcs::Batch2LoDTensorFunctor<DeviceContext, T> to_seq;
    if (in_g) {
      /* backward data */
      in_g->mutable_data<T>(ctx.GetPlace());
      to_seq(device_ctx, batch_gate_g, in_g);
    }
    if (bias && bias_g) {
      /* backward bias */
      phi::DenseTensor b_g = *bias_g;
      b_g.Resize({bias_g->numel(), 1});
      phi::DenseTensor gate_bias_g = b_g.Slice(0, 4 * frame_size);
      phi::funcs::ColwiseSum<DeviceContext, T> col_sum;
      col_sum(device_ctx, batch_gate_g, &gate_bias_g);
    }

    if (h0 && h0_g) {
      ReorderInitState<DeviceContext, T>(
          device_ctx, ordered_h0_g, order, h0_g, false);
    }
    if (c0 && c0_g) {
      ReorderInitState<DeviceContext, T>(
          device_ctx, ordered_c0_g, order, c0_g, false);
    }
  }
};

}  // namespace operators
}  // namespace paddle