// Copyright (c) 2019 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 <gtest/gtest.h>
#include "lite/api/paddle_use_kernels.h"
#include "lite/api/paddle_use_ops.h"
#include "lite/core/arena/framework.h"
#include "lite/tests/utils/fill_data.h"

namespace paddle {
namespace lite {

#define ELT(MATHOP)                                                        \
  for (int n = 0; n < xn; n++) {                                           \
    for (int c = 0; c < xc; c++) {                                         \
      for (int h = 0; h < xh; h++) {                                       \
        for (int w = 0; w < xw; w++) {                                     \
          int x_offset = n * xc * xh * xw + c * xh * xw + h * xw + w;      \
          int y_offset = 0;                                                \
          if (yn != 1) y_offset += n * yc * yh * yw;                       \
          if (yc != 1) y_offset += c * yh * yw;                            \
          if (yh != 1) y_offset += h * yw;                                 \
          if (yw != 1) y_offset += w;                                      \
          out_data[x_offset] = out_data[x_offset] MATHOP y_data[y_offset]; \
        }                                                                  \
      }                                                                    \
    }                                                                      \
  }

class ElementwiseComputeTester : public arena::TestCase {
 protected:
  // common attributes for this op.
  std::string x_ = "x";
  std::string y_ = "y";
  std::string out_ = "out";
  // add, sub, mul, div, max
  std::string elt_type_ = "";
  DDim x_dims_{{1, 2, 3, 4}};
  DDim y_dims_{{1, 2, 3, 4}};
  int axis_ = 1;
  std::string act_type_ = "";

 public:
  ElementwiseComputeTester(const Place& place,
                           const std::string& alias,
                           std::string elt_type = "add",
                           std::vector<int64_t> x_shape = {1, 2, 3, 4},
                           std::vector<int64_t> y_shape = {1, 2, 3, 4},
                           int axis = 1,
                           std::string act_type = "")
      : TestCase(place, alias),
        elt_type_(elt_type),
        x_dims_(DDim(x_shape)),
        y_dims_(DDim(y_shape)),
        axis_(axis),
        act_type_(act_type) {}

  void RunBaseline(Scope* scope) override {
    if (axis_ < 0) {
      axis_ = x_dims_.size() - y_dims_.size();
    }
    auto x_shape = x_dims_.Vectorize();
    while (x_shape.size() < 4) {
      x_shape.push_back(1);
    }
    auto y_shape = y_dims_.Vectorize();
    y_shape.insert(y_shape.begin(), axis_, 1);
    while (y_shape.size() < 4) {
      y_shape.push_back(1);
    }
    CHECK_EQ(x_shape.size(), 4);
    CHECK_EQ(y_shape.size(), 4);

    auto x = scope->FindTensor(x_);
    auto y = scope->FindTensor(y_);
    auto x_data = x->data<float>();
    auto y_data = y->data<float>();
    auto out = scope->NewTensor(out_);
    out->Resize(x_dims_);
    auto out_data = out->mutable_data<float>();
    memcpy(out_data, x_data, sizeof(float) * x_dims_.production());

    int xn = x_shape[0];
    int xc = x_shape[1];
    int xh = x_shape[2];
    int xw = x_shape[3];

    int yn = y_shape[0];
    int yc = y_shape[1];
    int yh = y_shape[2];
    int yw = y_shape[3];

    if (elt_type_ == "add") {
      ELT(+);
    } else if (elt_type_ == "sub") {
      ELT(-);
    } else if (elt_type_ == "mul") {
      ELT(*);
    } else if (elt_type_ == "div") {
      ELT(/);
    } else if (elt_type_ == "max") {
      for (int n = 0; n < xn; n++) {
        for (int c = 0; c < xc; c++) {
          for (int h = 0; h < xh; h++) {
            for (int w = 0; w < xw; w++) {
              int x_offset = n * xc * xh * xw + c * xh * xw + h * xw + w;
              int y_offset = 0;
              if (yn != 1) y_offset += n * yc * yh * yw;
              if (yc != 1) y_offset += c * yh * yw;
              if (yh != 1) y_offset += h * yw;
              if (yw != 1) y_offset += w;
              out_data[x_offset] =
                  std::max(out_data[x_offset], y_data[y_offset]);
            }
          }
        }
      }
    } else {
      LOG(FATAL) << "unsupported";
    }

    if (!act_type_.empty()) {
      if (act_type_ == "relu") {
        for (int i = 0; i < x_dims_.production(); i++) {
          out_data[i] = std::max(0.f, out_data[i]);
        }
      } else {
        LOG(FATAL) << "unsupported";
      }
    }
  }

  void PrepareOpDesc(cpp::OpDesc* op_desc) {
    std::string op_type = "elementwise_" + elt_type_;
    if (!act_type_.empty()) {
      op_type = "fusion_" + op_type + "_activation";
    }
    op_desc->SetType(op_type);
    op_desc->SetInput("X", {x_});
    op_desc->SetInput("Y", {y_});
    op_desc->SetOutput("Out", {out_});
    op_desc->SetAttr("axis", axis_);
    if (!act_type_.empty()) {
      op_desc->SetAttr("act_type", act_type_);
    }
  }

  void PrepareData() override {
    std::vector<float> dx(x_dims_.production());
    for (size_t i = 0; i < dx.size(); i++) {
      dx[i] = (i % 3) * 1.1f;
      dx[i] = dx[i] == 0 ? 1.f : dx[i];
    }
    SetCommonTensor(x_, x_dims_, dx.data());

    std::vector<float> dy(y_dims_.production());
    for (size_t i = 0; i < dy.size(); i++) {
      dy[i] = (i % 5) * 1.1f;
      dy[i] = dy[i] == 0 ? 1.f : dy[i];
    }
    SetCommonTensor(y_, y_dims_, dy.data());
  }
};

// add sub mul div max   +act

void TestElt(Place place,
             float abs_error,
             std::string elt_type,
             std::vector<int64_t> x_shape,
             std::vector<int64_t> y_shape,
             int axis,
             std::string act_type = "") {
  std::unique_ptr<arena::TestCase> tester(new ElementwiseComputeTester(
      place, "def", elt_type, x_shape, y_shape, axis, act_type));
  arena::Arena arena(std::move(tester), place, abs_error);
  arena.TestPrecision();
}

void TestEltDims(Place place, float abs_error) {
  TestElt(place, abs_error, "add", {2, 3, 4, 5}, {2, 3, 4, 5}, 0);
  TestElt(place, abs_error, "add", {2, 3, 4}, {2, 3, 4}, 0);
  TestElt(place, abs_error, "add", {2, 3, 4}, {2, 3}, 0);
  TestElt(place, abs_error, "add", {2, 3}, {2}, 0);
  TestElt(place, abs_error, "add", {2, 3, 4, 5}, {3, 4}, 1);
  TestElt(place, abs_error, "add", {2, 3, 4}, {3, 4}, 1);
  TestElt(place, abs_error, "add", {2, 3}, {3}, 1);
  TestElt(place, abs_error, "add", {2, 3, 4, 5}, {4, 5}, 2);
  TestElt(place, abs_error, "add", {2, 3, 4}, {4}, 2);
  TestElt(place, abs_error, "add", {2, 3, 4, 5}, {5}, 3);
  TestElt(place, abs_error, "add", {2, 3, 4, 5}, {3, 4, 5}, -1);
  TestElt(place, abs_error, "add", {2, 3, 4}, {3, 4}, -1);
}

void TestEltTypes(Place place, float abs_error) {
  for (auto elt_type :
       std::vector<std::string>{"add", "sub", "mul", "div", "max"}) {
    // Huawei Ascend NPU DDK has bugs in div, and not support max yet
    if (place == TARGET(kHuaweiAscendNPU) &&
        (elt_type == "div" || elt_type == "max")) {
      continue;
    }
    TestElt(place, abs_error, elt_type, {2, 3, 4, 5}, {2, 3, 4, 5}, 0);
    TestElt(place, abs_error, elt_type, {2, 3, 4, 5}, {3}, 1);
  }
}

void TestEltFuseAct(Place place, float abs_error) {
  for (auto elt_type :
       std::vector<std::string>{"add", "sub", "mul", "div", "max"}) {
    // Huawei Ascend NPU DDK has bugs in div, and not support max yet
    if (place == TARGET(kHuaweiAscendNPU) &&
        (elt_type == "div" || elt_type == "max")) {
      continue;
    }
    TestElt(place, abs_error, elt_type, {2, 3, 4, 5}, {2, 3, 4, 5}, 0, "relu");
    TestElt(place, abs_error, elt_type, {2, 3, 4, 5}, {3}, 1, "relu");
  }
}

TEST(Elementwise, precision) {
  Place place;
  float abs_error = 2e-5;

#if defined(LITE_WITH_NPU)
  place = TARGET(kNPU);
  abs_error = 1e-2;  // use fp16 in npu
#elif defined(LITE_WITH_HUAWEI_ASCEND_NPU)
  place = TARGET(kHuaweiAscendNPU);
  abs_error = 1e-2;  // precision_mode default is force_fp16
#elif defined(LITE_WITH_ARM)
  place = TARGET(kARM);
#elif defined(LITE_WITH_XPU) && defined(LITE_WITH_XTCL)
  place = TARGET(kXPU);
#else
  return;
#endif

  TestEltDims(place, abs_error);
  TestEltTypes(place, abs_error);
  TestEltFuseAct(place, abs_error);
}

}  // namespace lite
}  // namespace paddle
