/* 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. */

#include "paddle/fluid/operators/fake_quantize_op.h"
#include <string>
#include "paddle/fluid/framework/eigen.h"

namespace paddle {
namespace operators {

template <typename T>
using EigenVectorArrayMap = Eigen::Map<Eigen::Array<T, Eigen::Dynamic, 1>>;
template <typename T>
using ConstEigenVectorArrayMap =
    Eigen::Map<const Eigen::Array<T, Eigen::Dynamic, 1>>;

template <typename T>
struct FindAbsMaxFunctor<platform::CPUDeviceContext, T> {
  void operator()(const CPUDeviceContext& ctx, const T* in, const int num,
                  T* out) {
    ConstEigenVectorArrayMap<T> in_e(in, num);
    EigenVectorArrayMap<T> out_e(out, 1);

    auto& dev = ctx.eigen_device();
    out_e = in_e.abs().maximum();
  }
};

template <typename T>
struct ClipAndFakeQuantFunctor<platform::CPUDeviceContext, T> {
  void operator()(const CPUDeviceContext& ctx, const framework::Tensor& in,
                  const framework::Tensor* scale, const int bin_cnt,
                  framework::Tensor* out) {
    T s = scale->data<T>()[0];
    Transform<DeviceContext> trans;
    trans(ctx, in.data<T>(), in.data<T>() + in.numel(),
          out->mutable_data<T>(ctx.GetPlace()), ClipFunctor<T>(-s, s));
    auto in_e = framework::EigenVector<T>::Flatten(in);
    auto out_e = framework::EigenVector<T>::Flatten(*out);
    out_e.device(dev) = (bin_cnt / s * in_e).round();
  }
};

template <typename T>
struct FindRangeAbsMaxFunctor<platform::CPUDeviceContext, T> {
  void operator()(const platform::CPUDeviceContext& ctx,
                  const framework::Tensor& cur_scale,
                  const framework::Tensor& last_scale,
                  const framework::Tensor& iter, const int window_size,
                  framework::Tensor* scales_arr, framework::Tensor* out_scale) {
    T* scale_arr = scales_arr->mutable_data<T>(cxt.GetPlace());
    int it = iter.data<int>()[0];
    int idx = it % window_size;
    T removd = scale_arr[idx];
    T cur = cur_scale.data<T>()[0];
    scale_arr[idx] = cur;

    T max = last_scale.data<T>()[0];
    if (max < cur) {
      max = cur;
    } else if (fabs(removed - max) < 1e-6) {
      int size = (it > window_size) ? window_size : it;
      FindAbsMaxFunctor<platform::CPUDeviceContext, T>()(ctx, scale_arr, size,
                                                         &max);
    }
    out_scale->mutable_data<T>()[0] = max;
  }
};

class FakeQuantizeAbsMaxOp : public framework::OperatorWithKernel {
 public:
  FakeQuantizeAbsMaxOp(const std::string& type,
                       const framework::VariableNameMap& inputs,
                       const framework::VariableNameMap& outputs,
                       const framework::AttributeMap& attrs)
      : OperatorWithKernel(type, inputs, outputs, attrs) {}

  void InferShape(framework::InferShapeContext* ctx) const override {
    PADDLE_ENFORCE(ctx->HasInput("X"),
                   "Input(X) of FakeQuantizeOp should not be null.");
    PADDLE_ENFORCE(ctx->HasOutput("Out"),
                   "Output(Out) of FakeQuantizeOp should not be null.");
    PADDLE_ENFORCE(ctx->HasOutput("OutScale"),
                   "Output(Scale) of FakeQuantizeOp should not be null.");
    ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
    ctx->SetOutputDim("OutScale", {1});
    ctx->ShareLoD("X", /*->*/ "Out");
  }
};

class FakeQuantizeAbsMaxOpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  void Make() override {
    AddInput("X", "(Tensor) Input is float data type.");
    AddOutput("Out",
              "(Tensor) Output of quantized low level tensor, "
              "but also saved as float data type.");
    AddOutput("OutScale", "(Tensor) Current scale");
    AddAttr<int>("bit_length", "(int, default 8)")
        .SetDefault(8)
        .AddCustomChecker([](const int& bit_length) {
          PADDLE_ENFORCE(bit_length >= 1 && bit_length <= 16,
                         "'bit_length' should be between 1 and 16.");
        });
    AddComment(R"DOC(
FakeQuantize operator

$$scale = max(abs(X))$$ 
$$range = 2^{bit_length - 1} - 1$$
$$Out = round(X/scale * range)$$

)DOC");
  }
};

class FakeQuantizeRangeAbsMaxOp : public framework::OperatorWithKernel {
 public:
  FakeQuantizeOp(const std::string& type,
                 const framework::VariableNameMap& inputs,
                 const framework::VariableNameMap& outputs,
                 const framework::AttributeMap& attrs)
      : OperatorWithKernel(type, inputs, outputs, attrs) {}

  void InferShape(framework::InferShapeContext* ctx) const override {
    PADDLE_ENFORCE(ctx->HasInput("X"),
                   "Input(X) of FakeQuantizeRangeAbsMaxOp should not be null.");
    PADDLE_ENFORCE(
        ctx->HasOutput("Out"),
        "Output(Out) of FakeQuantizeRangeAbsMaxOp should not be null.");
    PADDLE_ENFORCE(
        ctx->HasOutput("OutScale"),
        "Output(OutScale) of FakeQuantizeRangeAbsMaxOp should not be null");
    if (ctx->HasInput("InScales")) {
      PADDLE_ENFORCE(
          ctx->HasOutput("OutScales"),
          "Output(OutScales) of FakeQuantizeRangeAbsMaxOp should not be null");
      ctx->SetOutputDim("OutScales", ctx->GetInputDim("InScales"));
    }
    ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
    ctx->SetOutputDim("OutScale", {1});
    ctx->ShareLoD("X", /*->*/ "Out");
  }
};

class FakeQuantizeRangeAbsMaxOpMaker
    : public framework::OpProtoAndCheckerMaker {
 public:
  void Make() override {
    AddInput("X", "(Tensor) Input is float data type.");
    AddInput("InScales", "(Tensor) scale buffer.").AsDispensable();
    AddInput("InScale", "Last scale.")
        AddInput("Iter", "Global step iteration.")
            .AsDispensable();
    AddOutput("Out", "(Tensor) Output of quantized low level tensor.");
    AddOutput("OutScale", " Current scale");
    AddOutput("OutScales", "(Tensor) scale buffer.").AsDispensable();
    AddAttr<int>("window_size", "(int, default 10000) window range size.")
        .SetDefault(10000);
    AddAttr<int>("bit_length", "(int, default 8), quantization bit number.")
        .SetDefault(8)
        .AddCustomChecker([](const int& bit_length) {
          PADDLE_ENFORCE(bit_length >= 1 && bit_length <= 16,
                         "'bit_length' should be between 1 and 16.");
        });
    AddAttr<bool>("is_test", "").SetDefault(false);
    AddComment(R"DOC(
FakeQuantize operator is used in static quantization.

$$scale = max(max(abs(x)), history_abs_max)$$ 
$$range = 2^{bit_length - 1} - 1$$
$$Out = round(X/scale * range)$$

)DOC");
  }
};

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;

REGISTER_OPERATOR(fake_quantize_abs_max, ops::FakeQuantizeAbsMaxOp,
                  ops::FakeQuantizeAbsMaxOpMaker,
                  paddle::framework::EmptyGradOpMaker);
REGISTER_OP_CPU_KERNEL(
    fake_quantize_abs_max,
    ops::FakeQuantizeKernel<paddle::platform::CPUDeviceContext, float>,
    ops::FakeQuantizeKernel<paddle::platform::CPUDeviceContext, double>);

REGISTER_OPERATOR(fake_quantize_range_abs_max, ops::FakeQuantizeOp,
                  ops::FakeQuantizeRangeAbsMaxOpMaker,
                  paddle::framework::EmptyGradOpMaker);
REGISTER_OP_CPU_KERNEL(
    fake_quantize_range_abs_max,
    ops::FakeQuantizeKernel<paddle::platform::CPUDeviceContext, float>,
    ops::FakeQuantizeKernel<paddle::platform::CPUDeviceContext, double>);