/* Copyright (c) 2021 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. */

#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/op_version_registry.h"
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/operators/matmul_v2_op.h"

#include "paddle/fluid/operators/fused/fused_dropout_helper.h"
#include "paddle/fluid/operators/layer_norm_kernel.cu.h"

namespace paddle {
namespace operators {

using Tensor = framework::Tensor;

template <typename DeviceContext, typename T>
class FusedFeedForwardKernel : public framework::OpKernel<T> {
 public:
  void MatMul(const platform::CUDADeviceContext& ctx,
              const framework::Tensor& a, const framework::Tensor& b,
              framework::Tensor* c) const {
    auto blas = math::GetBlas<DeviceContext, T>(ctx);
    auto a_2d = FoldInitDims(a);
    auto b_2d = FoldInitDims(b);
    auto mat_dim_a = math::CreateMatrixDescriptor(a_2d.dims(), 0, false);
    auto mat_dim_b = math::CreateMatrixDescriptor(b_2d.dims(), 0, false);
    T alpha = static_cast<T>(1.0);
    blas.MatMul(a, mat_dim_a, b, mat_dim_b, alpha, c, T(0));
  }

  void FFN(const framework::Tensor& x, const framework::Tensor& linear1_weight,
           const framework::Tensor* linear1_bias,
           const framework::Tensor& linear2_weight,
           const framework::Tensor* linear2_bias,
           const framework::Tensor* ln1_scale,
           const framework::Tensor* ln1_bias,
           const framework::Tensor* ln2_scale,
           const framework::Tensor* ln2_bias, framework::Tensor* out,
           framework::Tensor* dropout1_mask, framework::Tensor* dropout2_mask,
           framework::Tensor* ln1_mean, framework::Tensor* ln1_variance,
           framework::Tensor* ln2_mean, framework::Tensor* ln2_variance,
           framework::Tensor* linear1_out, framework::Tensor* ln1_out,
           framework::Tensor* dropout1_out, framework::Tensor* dropout2_out,
           const int bsz_seq, const int d_model, const int dim_feedforward,
           const std::string& act_method, const bool pre_layer_norm,
           const float epsilon1, const float epsilon2,
           const DropoutParam& dropout_param1,
           const DropoutParam& dropout_param2,
           const platform::CUDADeviceContext& ctx) const {
    FusedDropoutLayerNormHelper<T, uint8_t> pre_layernorm_helper(
        bsz_seq, d_model, epsilon1);
    FusedDropoutHelper<T, uint8_t> fused_act_dropout_helper(
        ctx, bsz_seq, dim_feedforward, dropout_param1);
    FusedDropoutLayerNormHelper<T, uint8_t> fused_dropout_layernorm_helper(
        ctx, bsz_seq, d_model, dropout_param2, epsilon2);

    auto place = ctx.GetPlace();
    using U = LayerNormParamType<T>;
    const framework::Tensor* in = &x;

    const U* ln1_scale_ptr =
        ln1_scale == nullptr ? nullptr : ln1_scale->data<U>();
    const U* ln1_bias_ptr = ln1_bias == nullptr ? nullptr : ln1_bias->data<U>();
    const U* ln2_scale_ptr =
        ln2_scale == nullptr ? nullptr : ln2_scale->data<U>();
    const U* ln2_bias_ptr = ln2_bias == nullptr ? nullptr : ln2_bias->data<U>();
    const T* linear1_bias_ptr =
        linear1_bias == nullptr ? nullptr : linear1_bias->data<T>();
    const T* linear2_bias_ptr =
        linear2_bias == nullptr ? nullptr : linear2_bias->data<T>();

    if (pre_layer_norm) {
      pre_layernorm_helper.LayerNorm(
          ctx, x.data<T>(), ln1_scale_ptr, ln1_bias_ptr, ln1_out->data<T>(),
          ln1_mean->data<U>(), ln1_variance->data<U>());
      in = ln1_out;
    }
    MatMul(ctx, *in, linear1_weight, linear1_out);
    fused_act_dropout_helper.DropoutActBias(
        ctx, linear1_out->data<T>(), linear1_bias_ptr, act_method,
        dropout1_out->data<T>(), dropout1_mask->data<uint8_t>());
    framework::Tensor linear2_out;
    linear2_out.mutable_data<T>({bsz_seq, d_model}, place);
    MatMul(ctx, *dropout1_out, linear2_weight, &linear2_out);
    if (!pre_layer_norm) {
      fused_dropout_layernorm_helper.LayernormResidualDropoutBias(
          ctx, linear2_out.data<T>(), x.data<T>(), linear2_bias_ptr,
          ln2_scale_ptr, ln2_bias_ptr, dropout2_out->data<T>(),
          dropout2_mask->data<uint8_t>(), out->data<T>(), ln2_mean->data<U>(),
          ln2_variance->data<U>());
    } else {
      fused_dropout_layernorm_helper.ResidualDropoutBias(
          ctx, linear2_out.data<T>(), x.data<T>(), linear2_bias_ptr,
          out->data<T>(), dropout2_mask->data<uint8_t>());
    }
  }

  void Compute(const framework::ExecutionContext& context) const override {
    auto* x = context.Input<framework::Tensor>("X");
    auto* linear1_weight = context.Input<framework::Tensor>("Linear1Weight");
    auto* linear1_bias = context.Input<framework::Tensor>("Linear1Bias");
    auto* linear2_weight = context.Input<framework::Tensor>("Linear2Weight");
    auto* linear2_bias = context.Input<framework::Tensor>("Linear2Bias");
    auto* ln1_scale = context.Input<framework::Tensor>("Ln1Scale");
    auto* ln1_bias = context.Input<framework::Tensor>("Ln1Bias");
    auto* ln2_scale = context.Input<framework::Tensor>("Ln2Scale");
    auto* ln2_bias = context.Input<framework::Tensor>("Ln2Bias");

    auto* ln1_mean = context.Output<framework::Tensor>("Ln1Mean");
    auto* ln1_variance = context.Output<framework::Tensor>("Ln1Variance");
    auto* ln2_mean = context.Output<framework::Tensor>("Ln2Mean");
    auto* ln2_variance = context.Output<framework::Tensor>("Ln2Variance");
    auto* out = context.Output<framework::Tensor>("Out");
    auto* dropout1_mask = context.Output<framework::Tensor>("Dropout1Mask");
    auto* dropout2_mask = context.Output<framework::Tensor>("Dropout2Mask");
    auto* linear1_out = context.Output<framework::Tensor>("Linear1Out");
    auto* ln1_out = context.Output<framework::Tensor>("Ln1Out");
    auto* dropout1_out = context.Output<framework::Tensor>("Dropout1Out");
    auto* dropout2_out = context.Output<framework::Tensor>("Dropout2Out");

    const std::string act_method = context.Attr<std::string>("act_method");

    const bool pre_layer_norm = context.Attr<bool>("pre_layer_norm");
    const float epsilon1 = context.Attr<float>("ln1_epsilon");
    const float epsilon2 = context.Attr<float>("ln2_epsilon");

    DropoutParam dropout_param1(context, 1);
    DropoutParam dropout_param2(context, 2);

    using U = LayerNormParamType<T>;
    auto place = context.GetPlace();
    out->mutable_data<T>(place);
    dropout1_mask->mutable_data<uint8_t>(place);
    dropout2_mask->mutable_data<uint8_t>(place);
    ln1_mean->mutable_data<U>(place);
    ln1_variance->mutable_data<U>(place);
    ln2_mean->mutable_data<U>(place);
    ln2_variance->mutable_data<U>(place);
    linear1_out->mutable_data<T>(place);
    ln1_out->mutable_data<T>(place);
    dropout1_out->mutable_data<T>(place);
    dropout2_out->mutable_data<T>(place);

    auto x_dim = x->dims();
    auto mat_dim_x =
        math::CreateMatrixDescriptor(RowMatrixFromVector(x_dim), 0, false);

    auto dim = linear1_weight->dims();
    int d_model = dim[0];
    int dim_feedforward = dim[dim.size() - 1];
    int bsz_seq = mat_dim_x.batch_size_ * mat_dim_x.height_;

    FFN(*x, *linear1_weight, linear1_bias, *linear2_weight, linear2_bias,
        ln1_scale, ln1_bias, ln2_scale, ln2_bias, out, dropout1_mask,
        dropout2_mask, ln1_mean, ln1_variance, ln2_mean, ln2_variance,
        linear1_out, ln1_out, dropout1_out, dropout2_out, bsz_seq, d_model,
        dim_feedforward, act_method, pre_layer_norm, epsilon1, epsilon2,
        dropout_param1, dropout_param2, context.cuda_device_context());
  }
};

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
    fused_feedforward,
    ops::FusedFeedForwardKernel<paddle::platform::CUDADeviceContext, float>,
    ops::FusedFeedForwardKernel<paddle::platform::CUDADeviceContext, double>,
    ops::FusedFeedForwardKernel<paddle::platform::CUDADeviceContext,
                                paddle::platform::float16>);