/* 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"
#include "paddle/fluid/operators/clip_op.h"
#include "paddle/fluid/platform/transform.h"

namespace paddle {
namespace operators {

template <typename T>
struct Compare {
 public:
  bool operator()(const T a, const T b) { return (std::abs(a) < std::abs(b)); }
};

template <typename T>
struct FindAbsMaxFunctor<platform::CPUDeviceContext, T> {
  void operator()(const platform::CPUDeviceContext& ctx, const T* in,
                  const int num, T* out) {
    *out = std::abs(*(std::max_element(in + 0, in + num, Compare<T>())));
  }
};

template struct FindAbsMaxFunctor<platform::CPUDeviceContext, float>;

template <typename T>
struct FindChannelAbsMaxFunctor<platform::CPUDeviceContext, T> {
  void operator()(const platform::CPUDeviceContext& ctx, const T* in,
                  const int num, const int channel, T* out) {
    const int channel_size = num / channel;
    for (int i = 0; i < channel; i++) {
      auto* start = in + i * channel_size;
      auto* end = in + (i + 1) * channel_size;
      out[i] = std::abs(*(std::max_element(start, end, Compare<T>())));
    }
  }
};

template struct FindChannelAbsMaxFunctor<platform::CPUDeviceContext, float>;

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

template struct ClipAndFakeQuantFunctor<platform::CPUDeviceContext, float>;

template <typename T>
struct ChannelClipAndFakeQuantFunctor<platform::CPUDeviceContext, T> {
  void operator()(const platform::CPUDeviceContext& ctx,
                  const framework::Tensor& in, const framework::Tensor& scale,
                  const int bin_cnt, const int channel,
                  framework::Tensor* out) {
    auto* scale_data = scale.data<T>();
    auto* in_data = in.data<T>();
    auto* out_data = out->mutable_data<T>(ctx.GetPlace());
    const int channel_size = in.numel() / channel;
    platform::Transform<platform::CPUDeviceContext> trans;
    for (int i = 0; i < channel; i++) {
      T s = scale_data[i];
      auto* start = in_data + i * channel_size;
      auto* end = in_data + (i + 1) * channel_size;
      trans(ctx, start, end, out_data + i * channel_size,
            ClipFunctor<T>(-s, s));
    }
    for (int i = 0; i < channel; i++) {
      T s = scale_data[i];
      framework::Tensor one_channel_out = out->Slice(i, i + 1);
      auto out_e = framework::EigenVector<T>::Flatten(one_channel_out);
      out_e.device(*ctx.eigen_device()) = (bin_cnt / s * out_e).round();
    }
  }
};

template struct ChannelClipAndFakeQuantFunctor<platform::CPUDeviceContext,
                                               float>;

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>(ctx.GetPlace());
    int64_t it = iter.data<int64_t>()[0];
    int idx = it % window_size;
    T removed = 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>(ctx.GetPlace())[0] = max;
  }
};

template struct FindRangeAbsMaxFunctor<platform::CPUDeviceContext, float>;

template <typename T>
struct FindMovingAverageAbsMaxFunctor<platform::CPUDeviceContext, T> {
  void operator()(const platform::CPUDeviceContext& ctx,
                  const framework::Tensor& in_accum,
                  const framework::Tensor& in_state, const T* cur_scale,
                  const float rate, framework::Tensor* out_state,
                  framework::Tensor* out_accum, framework::Tensor* out_scale) {
    T accum = in_accum.data<T>()[0];
    T state = in_state.data<T>()[0];
    T scale = cur_scale[0];

    state = rate * state + 1;
    accum = rate * accum + scale;
    scale = accum / state;

    out_state->mutable_data<T>(ctx.GetPlace())[0] = state;
    out_accum->mutable_data<T>(ctx.GetPlace())[0] = accum;
    out_scale->mutable_data<T>(ctx.GetPlace())[0] = scale;
  }
};

template struct FindMovingAverageAbsMaxFunctor<platform::CPUDeviceContext,
                                               float>;

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");
  }

 protected:
  framework::OpKernelType GetExpectedKernelType(
      const framework::ExecutionContext& ctx) const override {
    return framework::OpKernelType(ctx.Input<framework::LoDTensor>("X")->type(),
                                   ctx.device_context());
  }
};

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 FakeChannelWiseQuantizeAbsMaxOp : public framework::OperatorWithKernel {
 public:
  using framework::OperatorWithKernel::OperatorWithKernel;

  void InferShape(framework::InferShapeContext* ctx) const override {
    PADDLE_ENFORCE(ctx->HasInput("X"),
                   "Input(X) of FakeChannelWiseQuantizeOp should not be null.");
    PADDLE_ENFORCE(
        ctx->HasOutput("Out"),
        "Output(Out) of FakeChannelWiseQuantizeOp should not be null.");
    PADDLE_ENFORCE(
        ctx->HasOutput("OutScale"),
        "Output(Scale) of FakeChannelWiseQuantizeOp should not be null.");
    ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
    ctx->SetOutputDim("OutScale", {ctx->GetInputDim("X")[0]});
    ctx->ShareLoD("X", /*->*/ "Out");
  }

 protected:
  framework::OpKernelType GetExpectedKernelType(
      const framework::ExecutionContext& ctx) const override {
    return framework::OpKernelType(ctx.Input<framework::LoDTensor>("X")->type(),
                                   ctx.GetPlace());
  }
};

class FakeChannelWiseQuantizeAbsMaxOpMaker
    : 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 channel wise 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(
The scale of FakeChannelWiseQuantize operator is a vector.
In detail, each channel of the input X has a scale value.

$$scale_c = max(abs(X_c))$$
$$range = 2^{bit\_length - 1} - 1$$
$$Out_c = round(\frac{X_c * range} {scale_c})$$
In above three formulas, the range value of c is as follow:
$$0 \leq c \lt \ the\ channel\ number\ of\ X$$
)DOC");
  }
};

class FakeQuantizeRangeAbsMaxOp : public framework::OperatorWithKernel {
 public:
  FakeQuantizeRangeAbsMaxOp(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->HasOutput("OutScales")) {
      int window_size = ctx->Attrs().Get<int>("window_size");
      ctx->SetOutputDim("OutScales", {window_size});
    }
    ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
    ctx->SetOutputDim("OutScale", {1});
    ctx->ShareLoD("X", /*->*/ "Out");
  }

 protected:
  framework::OpKernelType GetExpectedKernelType(
      const framework::ExecutionContext& ctx) const override {
    return framework::OpKernelType(ctx.Input<framework::LoDTensor>("X")->type(),
                                   ctx.device_context());
  }
};

class FakeQuantizeRangeAbsMaxOpMaker
    : public framework::OpProtoAndCheckerMaker {
 public:
  void Make() override {
    AddInput("X", "(Tensor) Input is float data type.");
    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",
                  "(bool, default false) Set to true for inference only, false "
                  "for training. Some layers may run faster when this is true.")
        .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");
  }
};

class FakeQuantizeMovingAverageAbsMaxOp : public framework::OperatorWithKernel {
 public:
  FakeQuantizeMovingAverageAbsMaxOp(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 FakeQuantizeMovingAverageAbsMaxOp should not be null.");
    PADDLE_ENFORCE(
        ctx->HasOutput("Out"),
        "Output(Out) of FakeQuantizeMovingAverageAbsMaxOp should not be null.");
    PADDLE_ENFORCE(ctx->HasOutput("OutScale"),
                   "Output(OutScale) of FakeQuantizeMovingAverageAbsMaxOp "
                   "should not be null");
    if (ctx->HasOutput("OutState")) {
      ctx->SetOutputDim("OutState", {1});
    }
    if (ctx->HasOutput("OutAccum")) {
      ctx->SetOutputDim("OutAccum", {1});
    }
    ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
    ctx->SetOutputDim("OutScale", {1});
    ctx->ShareLoD("X", /*->*/ "Out");
  }

 protected:
  framework::OpKernelType GetExpectedKernelType(
      const framework::ExecutionContext& ctx) const override {
    return framework::OpKernelType(ctx.Input<framework::LoDTensor>("X")->type(),
                                   ctx.device_context());
  }
};

class FakeQuantizeMovingAverageAbsMaxOpMaker
    : public framework::OpProtoAndCheckerMaker {
 public:
  void Make() override {
    AddInput("X", "(Tensor) Input is float data type.");
    AddInput("InScale", "Last scale.");
    AddInput("InAccum", "Last accum.").AsDispensable();
    AddInput("InState", "Last state.").AsDispensable();
    AddOutput("Out", "(Tensor) Output of quantized low level tensor.");
    AddOutput("OutScale", " Current scale");
    AddOutput("OutState", "(Tensor) state buffer.").AsDispensable();
    AddOutput("OutAccum", "(Tensor) accum buffer.").AsDispensable();
    AddAttr<float>("moving_rate", "(float, default 0.9) moving rate.")
        .SetDefault(0.9);
    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",
                  "(bool, default false) Set to true for inference only, false "
                  "for training. Some layers may run faster when this is true.")
        .SetDefault(false);
    AddComment(R"DOC(
FakeQuantize operator is used in static quantization.

$$scale = (moving\_rate*accum+max(abs(x)))/(moving\_rate*state+1)$$
$$range = 2^{bit\_length - 1} - 1$$
$$Out = round(X/scale * range)$$

)DOC");
  }
};

class MovingAverageAbsMaxScaleOp : public framework::OperatorWithKernel {
 public:
  using framework::OperatorWithKernel::OperatorWithKernel;

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

 protected:
  framework::OpKernelType GetExpectedKernelType(
      const framework::ExecutionContext& ctx) const override {
    return framework::OpKernelType(ctx.Input<framework::LoDTensor>("X")->type(),
                                   ctx.GetPlace());
  }
};

class MovingAverageAbsMaxScaleOpMaker
    : public framework::OpProtoAndCheckerMaker {
 public:
  void Make() override {
    AddInput("X", "(Tensor) Input is float data type.");
    AddInput("InAccum", "Last accum.").AsDispensable();
    AddInput("InState", "Last state.").AsDispensable();
    AddOutput("Out",
              "(Tensor) Output tensor is just equivalent to the input tensor.");
    AddOutput("OutScale", " Current scale");
    AddOutput("OutState", "(Tensor) state buffer.").AsDispensable();
    AddOutput("OutAccum", "(Tensor) accum buffer.").AsDispensable();
    AddAttr<float>("moving_rate", "(float, default 0.9) moving rate.")
        .SetDefault(0.9);
    AddAttr<bool>("is_test",
                  "(bool, default false) Set true for inference only and false "
                  "for training. Some layers may run faster when this is true.")
        .SetDefault(false);
    AddComment(R"DOC(
MovingAverageAbsMaxScale operator is only used for calculating the quantization scale.
And it will not quantize the input tensor.

$$scale = (moving\_rate*accum+max(abs(x)))/(moving\_rate*state+1)$$
$$Out = X$$

)DOC");
  }
};

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;
using CPU = paddle::platform::CPUDeviceContext;

REGISTER_OPERATOR(fake_quantize_abs_max, ops::FakeQuantizeAbsMaxOp,
                  ops::FakeQuantizeAbsMaxOpMaker,
                  paddle::framework::EmptyGradOpMaker);
REGISTER_OP_CPU_KERNEL(fake_quantize_abs_max,
                       ops::FakeQuantizeAbsMaxKernel<CPU, float>);

REGISTER_OPERATOR(fake_quantize_range_abs_max, ops::FakeQuantizeRangeAbsMaxOp,
                  ops::FakeQuantizeRangeAbsMaxOpMaker,
                  paddle::framework::EmptyGradOpMaker);
REGISTER_OP_CPU_KERNEL(fake_quantize_range_abs_max,
                       ops::FakeQuantizeRangeAbsMaxKernel<CPU, float>);

REGISTER_OPERATOR(fake_quantize_moving_average_abs_max,
                  ops::FakeQuantizeMovingAverageAbsMaxOp,
                  ops::FakeQuantizeMovingAverageAbsMaxOpMaker,
                  paddle::framework::EmptyGradOpMaker);
REGISTER_OP_CPU_KERNEL(fake_quantize_moving_average_abs_max,
                       ops::FakeQuantizeMovingAverageAbsMaxKernel<CPU, float>);
REGISTER_OPERATOR(fake_channel_wise_quantize_abs_max,
                  ops::FakeChannelWiseQuantizeAbsMaxOp,
                  ops::FakeChannelWiseQuantizeAbsMaxOpMaker,
                  paddle::framework::EmptyGradOpMaker);
REGISTER_OP_CPU_KERNEL(fake_channel_wise_quantize_abs_max,
                       ops::FakeChannelWiseQuantizeAbsMaxKernel<CPU, float>);

REGISTER_OPERATOR(moving_average_abs_max_scale, ops::MovingAverageAbsMaxScaleOp,
                  ops::MovingAverageAbsMaxScaleOpMaker,
                  paddle::framework::EmptyGradOpMaker);
REGISTER_OP_CPU_KERNEL(moving_average_abs_max_scale,
                       ops::MovingAverageAbsMaxScaleKernel<CPU, float>);
