// Copyright (c) 2021 CINN 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/cinn/hlir/pe/schedule.h"

#include <absl/container/flat_hash_map.h>
#include <isl/cpp.h>
#include <math.h>

#include <algorithm>
#include <fstream>
#include <functional>
#include <iostream>
#include <numeric>
#include <utility>

#include "paddle/cinn/common/cas.h"
#include "paddle/cinn/hlir/pe/load_x86_params.h"
#include "paddle/cinn/optim/ir_simplify.h"
#include "paddle/cinn/poly/isl_utils.h"
#include "paddle/cinn/utils/string.h"

DECLARE_bool(cinn_use_cuda_vectorize);
namespace cinn {
namespace hlir {
namespace pe {

ScheduleParam::ScheduleParam(common::Target::Arch arch) {
  switch (arch) {
    case common::Target::Arch::X86: {
      param_data = CreateX86Params();
      break;
    }
    case common::Target::Arch::NVGPU: {
      param_data = CreateCudaParams();
      break;
    }
    default: {
      LOG(FATAL)
          << "Schedule params must be initialized with target x86 or nvgpu.";
    }
  }
}

ScheduleParam::~ScheduleParam() {}

int GetInnerSplitter(int origin, int other_axis) {
  if (origin <= 1) return 1;
  int two_exp = 1;
  while (origin % two_exp == 0) {
    two_exp *= 2;
  }
  two_exp = two_exp / 2;
  int a = SplitEven(two_exp);
  int b = two_exp / a;
  while (a * other_axis >= 1024 || b * other_axis >= 1024) {
    two_exp = two_exp / 2;
    a = SplitEven(two_exp);
    b = two_exp / a;
  }
  if (origin == two_exp) {
    return 2;
  }
  return origin / two_exp;
}

int SplitEven(int origin) {
  if (origin <= 1) return 1;
  int res = 1;
  while (origin % res == 0 && res * res < origin) {
    res *= 2;
  }
  res = res / 2;
  return res;
}

int GetBasicFactor(const Type &type, const common::Target &target) {
  int target_native_vector_bits = target.get_target_bits() * 8;
  int type_bits = type.bits();
  return target_native_vector_bits / type_bits;
}

int GetBetterSplitFactor(int shape, int split_factor) {
  int better_factor = split_factor;
  while (better_factor > shape) {
    better_factor /= 2;
  }
  if (better_factor < shape && better_factor != split_factor)
    return better_factor * 2;
  return better_factor;
}

int GetVectorizeFactor(int shape, int split_factor) {
  int better_factor = 1;
  for (int i = split_factor; i > 1; i--) {
    if (shape % i == 0) {
      better_factor = i;
      break;
    }
  }
  return better_factor;
}

void ScheduleInjectiveCPU(poly::Stage *stage,
                          const std::vector<int> &output_shape,
                          const common::Target &target,
                          bool vectorizable) {
  int dims = stage->n_out_dims();
  int factor = GetBasicFactor(stage->tensor()->type(), target);
  poly::Iterator fused = stage->axis(0);
  if (dims >= 5) {
    fused = stage->Fuse({0, 1, 2});
  } else if (dims >= 3) {
    fused = stage->Fuse({0, 1});
  }
  stage->Parallel(fused);
  dims = stage->n_out_dims();

  if (vectorizable) {
    poly::Iterator lo;
    poly::Iterator li;
    int last_shape = stage->GetDimRange(dims - 1);
    factor = GetVectorizeFactor(last_shape, factor);
    std::tie(lo, li) = stage->Split(stage->axis(dims - 1), factor);
    stage->Vectorize(li, factor);
    if (dims == 1) {
      stage->Parallel(0);
    }
  }
}

void ScheduleInjectiveCPU1(poly::Stage *stage,
                           const std::vector<int> &output_shape,
                           const common::Target &target,
                           bool vectorizable) {
  int dims = stage->n_out_dims();
  if (dims > 1) {
    CHECK_EQ(stage->n_out_dims(), stage->n_in_dims())
        << "The dims of op are not equal";
    CHECK_EQ(stage->n_out_dims(), output_shape.size())
        << "The origin stage out dims should be same with output_shape sizes";
    poly::Iterator fused = stage->axis(dims - 1);
    int target_native_vector_bits = target.get_target_bits() * 8;
    int type_bits = stage->tensor()->type().bits();
    int prod_size = output_shape.back();
    // fuse conservatively for the complex index from poly and may not benefit a
    // lot compared with llvm optimization, only fuse the last two dims when the
    // last dimension is too small and can split and vectorize Todo: try reorder
    if (output_shape.back() * type_bits < target_native_vector_bits) {
      int last_two_dim_bits =
          output_shape[dims - 2] * output_shape[dims - 1] * type_bits;
      if (last_two_dim_bits % target_native_vector_bits == 0) {
        fused = stage->Fuse(dims - 2, dims - 1);
        prod_size *= output_shape[dims - 2];
      }
    }
    int split_factor = target_native_vector_bits / type_bits;
    if (vectorizable) {
      if (prod_size <= split_factor) {
        split_factor = GetBetterSplitFactor(prod_size, split_factor);
        if (split_factor >= 8) {
          stage->Vectorize(fused, split_factor);
        }
      } else {
        auto ssplit = stage->Split(fused, split_factor);
        auto &j_outer = std::get<0>(ssplit);
        auto &j_inner = std::get<1>(ssplit);
        stage->Vectorize(j_inner, split_factor);
      }
    }
  }
  if (stage->n_out_dims() > 1) {
    stage->Parallel(0);
  }
}

int GetArrayPackingFactor(int shape,
                          const Type &type,
                          const common::Target &target) {
  int split_base = GetBasicFactor(type, target);
  int split_factor = 1;
  // temporily use shape-1 instead of shape for isl wrong for1 elimination
  int i = split_base * split_base < shape ? split_base * split_base : shape;
  for (; i > 1; i--) {
    if (shape % i == 0) {
      split_factor = i;
      break;
    }
  }
  return split_factor;
}

void MatmulScheduleCUDA(poly::StageMap stages,
                        const ir::Tensor &output,
                        const common::Target &target) {
  stages[output]->Split(1, 2);
  stages[output]->Bind(0, "blockIdx.x");
  stages[output]->Bind(1, "threadIdx.x");
}

void MatmulScheduleCPU(poly::StageMap stages,
                       const ir::Tensor &output,
                       const ir::Tensor &packedB,
                       const common::Target &target) {
  CHECK_EQ(output->type(), packedB->type());
  int basic_split_factor = GetBasicFactor(packedB->type(), target);
  // packedB
  int packedB_dims = stages[packedB]->axis_names().size();
  int packed_last_dim = packedB->shape[packedB_dims - 1].as_int32();
  int packedB_split_factor =
      GetBetterSplitFactor(packed_last_dim, basic_split_factor);
  // tempory solution for indivisible case
  if (packedB_split_factor >= 8 &&
      packed_last_dim % packedB_split_factor == 0) {
    stages[packedB]->Vectorize(packedB_dims - 1, packedB_split_factor);
  }
  // output
  int output_size = output->shape.size();
  // M, N
  int M = output->shape[output_size - 2].as_int32();
  int N = output->shape[output_size - 1].as_int32();
  int bm = GetArrayPackingFactor(M, output->type(), target);
  int bn = GetArrayPackingFactor(N, output->type(), target);
  int out_axis_dims = stages[output]->axis_names().size();
  CHECK_GE(out_axis_dims, 3U) << "output tensor's size should be at least 3";
  poly::Iterator i_axis = stages[output]->axis(out_axis_dims - 3);
  poly::Iterator j_axis = stages[output]->axis(out_axis_dims - 2);
  poly::Iterator i_outer, i_inner, j_outer, j_inner;
  std::vector<poly::Iterator> i_axes, j_axes, k_axes;
  std::vector<poly::Iterator> all_axes;
  std::vector<poly::Iterator> all_axes_outer;
  std::vector<poly::Iterator> all_axes_inner;
  bool is_m_splited = false;
  bool is_n_splited = false;
  // tempory solution for isl for1 wrong elimination
  if (bm >= 4 && M != bm) {
    auto axes = stages[output]->Split(i_axis, bm);
    all_axes_outer.push_back(std::get<0>(axes));
    all_axes_inner.push_back(std::get<1>(axes));
    is_m_splited = true;
  } else {
    all_axes_outer.push_back(i_axis);
  }
  out_axis_dims = stages[output]->axis_names().size();
  // temp solution for isl for1 wrong elimination
  if (bn >= 4 && N != bn) {
    auto axes = stages[output]->Split(j_axis, bn);
    all_axes_outer.push_back(std::get<0>(axes));
    all_axes_inner.push_back(std::get<1>(axes));
    is_n_splited = true;
  } else {
    all_axes_outer.push_back(j_axis);
  }
  // K
  int K = packedB->shape[packedB->shape.size() - 2].as_int32();
  int k_split_factor = GetBetterSplitFactor(K, basic_split_factor);
  out_axis_dims = stages[output]->axis_names().size();
  auto k_axis = stages[output]->axis(out_axis_dims - 1);
  bool is_k_splited = false;
  if (k_split_factor >= 4) {
    auto axes = stages[output]->Split(k_axis, k_split_factor);
    k_axes.push_back(std::get<0>(axes));
    k_axes.push_back(std::get<1>(axes));
    all_axes_outer.push_back(std::get<0>(axes));
    all_axes_inner.push_back(std::get<1>(axes));
    is_k_splited = true;
  } else {
    all_axes_outer.push_back(k_axis);
  }
  std::vector<poly::Iterator> new_order;
  out_axis_dims = stages[output]->axis_names().size();
  if (output_size > 2) {
    // batch
    all_axes.push_back(stages[output]->axis(0));
  }
  for (int i = 0; i < all_axes_outer.size(); ++i) {
    all_axes.push_back(all_axes_outer[i]);
  }
  for (int i = 0; i < all_axes_inner.size(); ++i) {
    all_axes.push_back(all_axes_inner[i]);
  }
  // int axies
  CHECK_EQ(all_axes.size(), out_axis_dims);
  if (is_k_splited) {
    if (is_m_splited || is_n_splited) {
      // swap k_inner and j_inner/i_inner
      std::swap(all_axes[out_axis_dims - 1], all_axes[out_axis_dims - 2]);
    } else {
      // swap k_inner and j
      std::swap(all_axes[out_axis_dims - 1], all_axes[out_axis_dims - 3]);
    }
  } else {
    // swap k and j
    std::swap(all_axes[out_axis_dims - 1], all_axes[out_axis_dims - 2]);
  }
  stages[output]->Reorder(all_axes);
  // vectorize output's last dimemsion
  auto out_domain = stages[output]->transformed_domain();
  auto range =
      poly::isl_set_get_axis_range(out_domain.get(), out_axis_dims - 1);
  auto &min = std::get<0>(range);
  auto &max = std::get<1>(range);
  CHECK_EQ(min.get_num_si(), 0) << "axis range should begin from zero";
  int out_last_dim = max.get_num_si() + 1;
  int output_split_factor =
      GetBetterSplitFactor(out_last_dim, basic_split_factor);
  // tempory solution for indivisible case
  if (output_split_factor >= 8 && packed_last_dim % output_split_factor == 0) {
    stages[output]->Vectorize(out_axis_dims - 1, output_split_factor);
  }
}

void MulScheduleCPU(poly::StageMap stages,
                    const ir::Tensor &output,
                    const ir::Tensor &reduce_first,
                    const common::Target &target) {
  int split_factor = GetBasicFactor(output->type(), target);
  auto out_reduce_axis = output->reduce_axis;
  std::vector<Expr> reduce_first_shape = reduce_first->shape;
  std::vector<Expr> output_shape = output->shape;
  CHECK_EQ(reduce_first_shape.size(), 3U);
  CHECK_EQ(output_shape.size(), 2U);

  // reduce_first init
  auto reduce_first_init = reduce_first->GetInitTensor(stages, target);
  int reduce_first_init_dim = stages[reduce_first_init]->axis_names().size();
  stages[reduce_first_init]->ComputeAt2(stages[reduce_first],
                                        reduce_first_init_dim - 2);
  // output init
  auto out_init = output->GetInitTensor(stages, target);
  int out_init_dim = stages[out_init]->axis_names().size();
  stages[out_init]->ComputeAt2(stages[output], out_init_dim - 1);
  // reduce_first
  int reduce_first_dim = stages[reduce_first]->axis_names().size();
  stages[reduce_first]->Reorder({reduce_first_dim - 1, reduce_first_dim - 2});
  int reduce_first_last_shape = reduce_first_shape.back().as_int32();
  // output
  int out_dims = stages[output]->n_out_dims();
  if (reduce_first_last_shape > 1) {
    stages[output]->Unroll(out_dims - 1);
  }
}

int GetThreadBindAxis(const std::vector<ir::Expr> &shape) {
  int thread_axis = shape.size() - 1;
  for (int idx = thread_axis; idx >= 0; --idx) {
    if (shape[idx].as_int32() > 1) {
      thread_axis = idx;
      break;
    }
  }
  return thread_axis;
}

int GetBlockBindAxis(const std::vector<ir::Expr> &shape,
                     const int thread_axis) {
  int block_axis = 0, max_dim_size = shape[0].as_int32();
  for (int idx = 0; idx <= thread_axis; ++idx) {
    if (max_dim_size < shape[idx].as_int32()) {
      if (idx < thread_axis) {
        max_dim_size = shape[idx].as_int32();
        block_axis = idx;
      } else {
        if (max_dim_size == 1) {
          block_axis = thread_axis;
        }
      }
    }
  }
  return block_axis;
}

void CudaReduceSchedule(poly::StageMap stages,
                        ir::Tensor output,
                        int last_dimension_num,
                        const common::Target &target) {
  int parallel_thread_num = 1;
  for (int idx = output->shape.size() - 1;
       idx >= static_cast<int>(output->shape.size()) - last_dimension_num;
       --idx) {
    parallel_thread_num *= output->shape[idx].as_int32();
  }

  int index = output->shape.size() - last_dimension_num;
  for (int idx = output->shape.size() - last_dimension_num;
       idx < static_cast<int>(output->shape.size()) - 1;
       ++idx) {
    stages[output]->Fuse(index, index + 1);
  }

  int max_block_size = 1024;
  if (parallel_thread_num > max_block_size) {
    stages[output]->Split(index, max_block_size);
    stages[output]->Bind(index + 1, "threadIdx.x");
  } else {
    stages[output]->Bind(index, "threadIdx.x");
  }

  for (int idx = 0; idx < index - 1; ++idx) {
    stages[output]->Fuse(0, 1);
  }

  if (index > 0) {
    stages[output]->Bind(0, "blockIdx.x");
  }
}

void CudaWarpReduceSchedule(poly::StageMap stages,
                            ir::Tensor tmp_out,
                            ir::Tensor out,
                            const common::Target &target) {
  int sum_out_dim = 1;
  for (int idx = 0; idx < static_cast<int>(tmp_out->shape.size()) - 2; ++idx) {
    stages[out]->Fuse(0, 1);
    stages[tmp_out]->Fuse(0, 1);
    sum_out_dim *= out->shape[idx].as_int32();
  }
  sum_out_dim *= out->shape.back().as_int32();

  // out_shape = {1} tmp_shape = {32}
  if (tmp_out->shape.size() == 1) {
    stages[out]->Split(0, 1);
    stages[tmp_out]->Split(0, tmp_out->shape[0].as_int32());
  }

  if (sum_out_dim <= 16) {
    stages[out]->Bind(0, "threadIdx.y");

    stages[tmp_out]->Bind(0, "threadIdx.y");
    stages[tmp_out]->Bind(1, "threadIdx.x");
    stages[tmp_out]->SimpleComputeAt(stages[out], 0);
    stages[tmp_out]->SetBuffer("local");
  } else {
    stages[out]->Split(0, 8);
    stages[out]->Bind(0, "blockIdx.x");
    stages[out]->Bind(1, "threadIdx.y");

    stages[tmp_out]->Split(0, 8);
    stages[tmp_out]->Bind(2, "threadIdx.x");
    stages[tmp_out]->SimpleComputeAt(stages[out], 1);
    stages[tmp_out]->SetBuffer("local");
  }
}

void CudaBlockReduceInternalSchedule(poly::StageMap stages,
                                     ir::Tensor tmp_out,
                                     ir::Tensor out,
                                     const common::Target &target) {
  for (int idx = 0; idx < static_cast<int>(tmp_out->shape.size()) - 2; ++idx) {
    stages[tmp_out]->Fuse(0, 1);
    stages[out]->Fuse(0, 1);
  }

  if (tmp_out->shape.size() == 1) {
    stages[tmp_out]->Split(0, stages[tmp_out]->GetDimRange(0));
    stages[out]->Split(0, stages[out]->GetDimRange(0));
  }

  stages[tmp_out]->Bind(0, "blockIdx.x");
  stages[tmp_out]->Bind(1, "threadIdx.x");
  stages[tmp_out]->SetBuffer("local");
  stages[tmp_out]->SimpleComputeAt(stages[out], 0);

  stages[out]->Bind(0, "blockIdx.x");
}

void CudaBlockReduceSchedule(poly::StageMap stages,
                             ir::Tensor reduce_tmp_out,
                             ir::Tensor tmp_out,
                             ir::Tensor out,
                             const common::Target &target) {
  int output_shape_size_without_reduce = tmp_out->shape.size() - 1;
  // fuse last parallel dimension
  for (int idx = 0; idx < reduce_tmp_out->shape.size() - tmp_out->shape.size();
       ++idx) {
    stages[reduce_tmp_out]->Fuse(output_shape_size_without_reduce,
                                 output_shape_size_without_reduce + 1);
  }

  // fuse parallel dimension
  for (int idx = 0; idx < output_shape_size_without_reduce - 1; ++idx) {
    stages[out]->Fuse(0, 1);
    stages[tmp_out]->Fuse(0, 1);
    stages[reduce_tmp_out]->Fuse(0, 1);
  }

  if (tmp_out->shape.size() == 1) {
    stages[reduce_tmp_out]->Split(0, stages[reduce_tmp_out]->GetDimRange(0));
    stages[tmp_out]->Split(0, stages[tmp_out]->GetDimRange(0));
    stages[out]->Split(0, stages[out]->GetDimRange(0));
  }

  stages[reduce_tmp_out]->Bind(0, "blockIdx.x");
  stages[reduce_tmp_out]->Bind(1, "threadIdx.x");
  stages[reduce_tmp_out]->SetBuffer("local");
  stages[reduce_tmp_out]->SimpleComputeAt(stages[tmp_out], 0);

  stages[tmp_out]->Bind(0, "blockIdx.x");
  stages[tmp_out]->Bind(1, "threadIdx.x");
  stages[tmp_out]->SetBuffer("local");
  stages[tmp_out]->SimpleComputeAt(stages[out], 0);

  stages[out]->Bind(0, "blockIdx.x");
}

void CudaBlockShuffleReduceSchedule(poly::StageMap stages,
                                    ir::Tensor reshape,
                                    ir::Tensor internal,
                                    ir::Tensor out,
                                    const common::Target &target) {
  int fuse_times = internal->shape.size() - 2;
  for (int idx = 0; idx < fuse_times; ++idx) {
    stages[internal]->Fuse(0, 1);
    stages[out]->Fuse(0, 1);
  }

  fuse_times = out->shape.size() - internal->shape.size();
  for (int idx = 0; idx < fuse_times; ++idx) {
    if (internal->shape.size() == 1) {
      stages[out]->Fuse(0, 1);
    } else {
      stages[out]->Fuse(1, 2);
    }
  }

  if (stages[out]->n_out_dims() == 1) {
    stages[internal]->Split(0, stages[internal]->GetDimRange(0));
    stages[out]->Split(0, stages[out]->GetDimRange(0));
  }

  stages[reshape]->ComputeInline();
  stages[internal]->SetBuffer("shared");

  stages[internal]->Bind(0, "blockIdx.x");
  stages[internal]->Bind(1, "threadIdx.x");

  stages[out]->Bind(0, "blockIdx.x");
  stages[out]->Bind(1, "threadIdx.x");
  stages[internal]->SimpleComputeAt(stages[out], 0);

  stages[out]->SyncThreads(0, {internal}, stages);
}

void CudaTwoStepReduceSchedule(poly::StageMap stages,
                               ir::Tensor reshape,
                               ir::Tensor internal,
                               ir::Tensor tmp_out,
                               ir::Tensor out,
                               const common::Target &target) {
  // fuse axis
  for (int idx = 0; idx < static_cast<int>(internal->shape.size()) - 2; ++idx) {
    stages[internal]->Fuse(0, 1);
    stages[tmp_out]->Fuse(0, 1);
    stages[out]->Fuse(0, 1);
  }

  if (stages[tmp_out]->n_out_dims() == 1) {
    stages[internal]->Split(0, stages[internal]->GetDimRange(0));
    stages[tmp_out]->Split(0, stages[tmp_out]->GetDimRange(0));
    stages[out]->Split(0, stages[out]->GetDimRange(0));
  }

  stages[reshape]->ComputeInline();

  stages[internal]->Bind(0, "blockIdx.x");
  stages[internal]->Bind(1, "threadIdx.x");
  stages[internal]->SetBuffer("local");
  stages[internal]->SimpleComputeAt(stages[tmp_out], 0);

  stages[tmp_out]->Bind(0, "blockIdx.x");
  stages[tmp_out]->Bind(1, "threadIdx.x");
  stages[tmp_out]->SetBuffer("local");
  stages[tmp_out]->SimpleComputeAt(stages[out], 0);

  stages[out]->Bind(0, "blockIdx.x");
}

void SoftmaxScheduleCPU(poly::StageMap stage,
                        const ir::Tensor &output,
                        const ir::Tensor &temp,
                        int axis) {
  if (axis == -1) {
    axis += output->shape.size();
  }
  poly::Iterator fused = stage[output]->axis(0);
  stage[output]->Parallel(fused);
  for (int i = 1; i < axis; i++) {
    fused = stage[output]->Fuse(0, 1);
  }
  CHECK_GT(stage[output]->n_out_dims(), 1);
  stage[temp]->ComputeAt(stage[output], 0);
}

void GlobalPoolScheduleGPU(poly::StageMap stages,
                           const std::vector<ir::Tensor> &output,
                           const common::Target &target) {
  auto &out = output[0];
  auto &reduce = output[1];
  stages[out]->Fuse(0, 1);
  stages[out]->Split(0, 32);
  stages[out]->Bind(0, "blockIdx.x");
  stages[out]->Bind(1, "threadIdx.y");
  stages[reduce]->ComputeAt2(stages[out], 1);
  stages[reduce]->SetBuffer("local");
  stages[reduce]->Bind(2, "threadIdx.x");
}
void PoolScheduleCPU(poly::StageMap stages,
                     const ir::Tensor &output,
                     const common::Target &target) {
  CHECK_GE(stages[output]->n_out_dims(), 2);
  stages[output]->Fuse({0, 1});
  stages[output]->Parallel(0);
}

void PoolScheduleGPU(poly::StageMap stages,
                     ir::Tensor &output,
                     const common::Target &target) {
  CHECK_GE(stages[output]->axis_names().size(), 4);
  stages[output]->Fuse({0, 1, 2, 3});
  stages[output]->Split(0, 1024);
  stages[output]->Bind(0, "blockIdx.x");
  stages[output]->Bind(1, "threadIdx.x");
}

void GetConv2dFactors(absl::flat_hash_map<std::string, int> *factors,
                      int oc,
                      int ic,
                      int fc,
                      int oh,
                      int ow,
                      const Type &type,
                      const common::Target &target,
                      const std::string &key,
                      bool import_params) {
  if (import_params) {
    auto &params = ScheduleParam::get_x86_instance().GetParam();
    if (params.count(key)) {
      VLOG(3) << "find saved param, key is: " << key;
      CHECK(!params[key]["oc_bn"].empty());
      CHECK(!params[key]["ic_bn"].empty());
      CHECK(!params[key]["ow_bn"].empty());
      (*factors)["oc_bn"] = params[key]["oc_bn"].back();
      (*factors)["ic_bn"] = params[key]["ic_bn"].back();
      (*factors)["ow_bn"] = params[key]["ow_bn"].back();
      if (!params[key]["oh_bn"].empty()) {
        (*factors)["oh_bn"] = params[key]["oh_bn"].back();
      }
      if (!params[key]["unroll_kw"].empty()) {
        (*factors)["unroll_kw"] = params[key]["unroll_kw"].back();
      }
      if (ic == fc) {
        (*factors)["fc_bn"] = (*factors)["ic_bn"];
      } else {
        int fc_bn = 1;
        for (int i = (*factors)["oc_bn"]; i > 1; i--) {
          if (fc < 1) break;
          if (fc % i == 0) {
            fc_bn = i;
            break;
          }
        }
        (*factors)["fc_bn"] = fc_bn;
      }
      return;
    } else {
      VLOG(3) << "Can not find saved param, key is: " << key;
    }
  }
  int bn_base = GetBasicFactor(type, target);
  int oc_bn = 1;
  for (int i = bn_base; i > 1; i--) {
    if (oc < 1) break;
    if (oc % i == 0) {
      oc_bn = i;
      break;
    }
  }
  int ic_bn = 1;
  for (int i = oc_bn; i > 1; i--) {
    if (ic < 1) break;
    if (ic % i == 0) {
      ic_bn = i;
      break;
    }
  }
  int fc_bn = 1;
  for (int i = oc_bn; i > 1; i--) {
    if (fc < 1) break;
    if (fc % i == 0) {
      fc_bn = i;
      break;
    }
  }
  (*factors)["oc_bn"] = oc_bn;
  (*factors)["ic_bn"] = ic_bn;
  (*factors)["fc_bn"] = fc_bn;
  int ow_bn = 1;

  if (oh < 1) {
    for (int i = bn_base; i > 1; i--) {
      if (ow < 1) break;
      if (ow % i == 0) {
        ow_bn = i;
        break;
      }
    }
    (*factors)["ow_bn"] = ow_bn;
  } else {
    int oh_bn = 1;
    int begin = std::min(ow, bn_base);
    for (int i = begin; i >= 1; i--) {
      if (ow < 1) break;
      if (ow % i == 0) {
        ow_bn = i;
        for (int j = oh; j >= 1; j--) {
          if (oh % j == 0 && j * ow_bn <= 16) {
            oh_bn = j;
            (*factors)["oh_bn"] = oh_bn;
            (*factors)["ow_bn"] = ow_bn;
            return;
          }
        }
      }
    }
  }
}

void GetConv2d1x1Factors(absl::flat_hash_map<std::string, int> *factors,
                         int oc,
                         int ic,
                         int oh,
                         int ow,
                         const Type &type,
                         const common::Target &target) {
  int bn_base = GetBasicFactor(type, target);
  int oc_bn = 1;
  for (int i = bn_base; i > 1; i--) {
    if (oc < 1) break;
    if (oc % i == 0) {
      oc_bn = i;
      break;
    }
  }
  int ic_bn = 1;
  for (int i = oc_bn; i > 1; i--) {
    if (ic < 1) break;
    if (ic % i == 0) {
      ic_bn = i;
      break;
    }
  }
  (*factors)["oc_bn"] = oc_bn;
  (*factors)["ic_bn"] = ic_bn;
  int ow_bn = 1;
  int oh_bn = 1;
  int begin = std::min(ow, bn_base);
  for (int i = begin; i >= 1; i--) {
    if (ow < 1) break;
    if (ow % i == 0) {
      ow_bn = i;
      for (int j = oh; j >= 1; j--) {
        if (oh % j == 0 && j * ow_bn <= 16) {
          oh_bn = j;
          (*factors)["oh_bn"] = oh_bn;
          (*factors)["ow_bn"] = ow_bn;
          return;
        }
      }
    }
  }
}

std::string GenerateX86ConvKey(const std::vector<Expr> &input_shape,
                               const std::vector<Expr> &weight_shape,
                               const std::vector<int> &strides,
                               const std::vector<int> &paddings,
                               const std::vector<int> &dilations,
                               const int &index,
                               const std::string &model_name) {
  // format: (model_name + index +)schedule_name + input_shape + weight_shape +
  // strides + paddings + dilations e.g. resnet18 0 X86ScheduleConv input 1 3
  // 224 224 weight 64 3 7 7 stride 2 2 padding 3 3 dilation 1 1
  std::string key;
  if (model_name != "") {
    key = model_name + " index " + std::to_string(index) + " ";
  }
  key += "X86ScheduleConv input";
  for (auto &shape : input_shape) {
    key += " " + std::to_string(shape.as_int32());
  }
  key += " weight";
  for (auto &shape : weight_shape) {
    key += " " + std::to_string(shape.as_int32());
  }
  key += " stride";
  for (auto &stride : strides) {
    key += " " + std::to_string(stride);
  }
  key += " padding";
  for (auto &padding : paddings) {
    key += " " + std::to_string(padding);
  }
  key += " dilation";
  for (auto &dilation : dilations) {
    key += " " + std::to_string(dilation);
  }
  VLOG(3) << "key: " << key;
  return key;
}

std::string GenerateX86ConvKey(const std::vector<int> &input_shape,
                               const std::vector<int> &weight_shape,
                               const std::vector<int> &strides,
                               const std::vector<int> &paddings,
                               const std::vector<int> &dilations,
                               const int &index,
                               const std::string &model_name) {
  // format: (model_name + index +)schedule_name + input_shape + weight_shape +
  // strides + paddings + dilations
  std::string key;
  if (model_name != "") {
    key = model_name + " index " + std::to_string(index) + " ";
  }
  key += "X86ScheduleConv input";
  for (auto &shape : input_shape) {
    key += " " + std::to_string(shape);
  }
  key += " weight";
  for (auto &shape : weight_shape) {
    key += " " + std::to_string(shape);
  }
  key += " stride";
  for (auto &stride : strides) {
    key += " " + std::to_string(stride);
  }
  key += " padding";
  for (auto &padding : paddings) {
    key += " " + std::to_string(padding);
  }
  key += " dilation";
  for (auto &dilation : dilations) {
    key += " " + std::to_string(dilation);
  }
  VLOG(3) << "key: " << key;
  return key;
}

void CreateX86SerialData(const std::string &file_name) {
  /** The format of serial data is:
   * hash_key: schedule_name + shape of input + shape of weights + stride +
   * padding + dilation value: vector of params
   */
  SaveSerialData(CreateX86Params(), file_name);
}

void Conv2d_NCHWc_1X1_Schedule_CPU(poly::StageMap stages,
                                   const ir::Tensor &res,
                                   ir::Tensor &packed_out,
                                   const ir::Tensor &input_pad,
                                   const ir::Tensor &weights_dilation,
                                   const ir::Tensor &data,
                                   const common::Target &target,
                                   const std::string &key,
                                   bool do_padding) {
  CHECK(target.arch == Target::Arch::X86)
      << "Conv2d_NCHWc_1X1_Schedule_CPU schedule only used in x86";
  CHECK(packed_out.defined());
  CHECK(input_pad.defined());
  auto type = packed_out->type();
  absl::flat_hash_map<std::string, int> conv2d_factors;
  CHECK_EQ(packed_out->shape.size(), 5U)
      << "packed_out's shape size should be 5";
  Expr h_out = common::AutoSimplify(packed_out->shape[2]);
  Expr w_out = common::AutoSimplify(packed_out->shape[3]);
  int oh = h_out.as_int32();
  int ow = w_out.as_int32();
  int basic_split_factor = GetBasicFactor(type, target);
  GetConv2dFactors(&conv2d_factors, -1, -1, -1, oh, ow, type, target, key);
  int oh_bn_size = conv2d_factors["oh_bn"];
  int ow_bn_size = conv2d_factors["ow_bn"];

  auto input_shape = input_pad->shape;
  CHECK_EQ(input_shape.size(), 5U) << "input shape size should be 5";
  Expr oc_bn = common::AutoSimplify(packed_out->shape.back());
  Expr ic_bn = common::AutoSimplify(input_shape.back());
  int oc_bn_size = oc_bn.as_int32();
  int ic_bn_size = ic_bn.as_int32();
  VLOG(3) << "oh_bn_size " << oh_bn_size;
  VLOG(3) << "ow_bn_size " << ow_bn_size;
  VLOG(3) << "oc_bn_size " << oc_bn_size;
  VLOG(3) << "ic_bn_size " << ic_bn_size;

  // data
  if (data.defined()) {
    CHECK_GE(stages[data]->n_out_dims(), 3U)
        << "data's out_dims should be more than 3";
    stages[data]->Fuse({0, 1, 2});
    stages[data]->ComputeInline();
  }
  // input_pad
  if (do_padding) {
    CHECK_GE(stages[input_pad]->n_out_dims(), 3U)
        << "input_pad's out_dims should be more than 3";
    stages[input_pad]->Fuse({0, 1, 2});
    stages[input_pad]->Vectorize(stages[input_pad]->n_out_dims() - 1,
                                 input_pad->shape.back().as_int32());
  } else {
    stages[input_pad]->ComputeInline();
  }

  // weights
  if (weights_dilation.defined()) {
    CHECK_GE(stages[weights_dilation]->n_out_dims(), 3U)
        << "weights_dilation's out_dims should be more than 3";
    // oc_outer, ic_outer, oh, ow, ic_inner, oc_inner -> oc_outer, oh, ic_outer,
    // ow, ic_inner, oc_inner
    stages[weights_dilation]->Reorder({2, 1});
    stages[weights_dilation]->Fuse({0, 1});
  }

  // packed_out
  auto CC = stages[packed_out]->CacheWrite("global", stages, packed_out);
  // packed_out: [batch, oc_outer, oh, ow, oc_inner]
  // split oh, ow
  stages[packed_out]->Split(2, oh_bn_size);
  stages[packed_out]->Split(4, ow_bn_size);
  // [batch, oc_outer, oh_outer, oh_inner, ow_outer, ow_inner, oc_inner] ->
  // [batch_oc_outer_oh_outer_fused, oh_inner, ow_outer, ow_inner, oc_inner]
  stages[packed_out]->Fuse({0, 1, 2});
  VLOG(3) << "stages[CC]->transformed_domain()"
          << stages[CC]->transformed_domain();

  // CC: [batch, oh, ow, oc, ic, kh, kw] -> [batch_oc_outer_oh_outer_fused,
  // oh_inner, ow, oc_inner, ic, kh, kw]
  stages[CC]->ComputeAt2(stages[packed_out], 0);
  VLOG(3) << "cache write shape: " << utils::Join(CC->shape, ", ");
  // tempory solution because reorder may be wrong before ComputeAt
  // reorder: [batch_oc_outer_oh_outer_fused, oh_inner, ow_outer, ow_inner,
  // oc_inner] -> [batch_oc_outer_oh_outer_fused, ow_outer, oh_inner, ow_inner,
  // oc_inner]
  stages[packed_out]->Reorder({2, 1});
  stages[packed_out]->Vectorize(stages[packed_out]->n_out_dims() - 1,
                                packed_out->shape.back().as_int32());
  VLOG(3) << "stages[packed_out]->transformed_domain()"
          << stages[packed_out]->transformed_domain();
  VLOG(3) << "stages[CC]->transformed_domain()"
          << stages[CC]->transformed_domain();

  // CC: [batch_oc_outer_oh_outer_fused, oh_inner, ow, oc_inner, ic, kh, kw]
  // split ow
  stages[CC]->Split(2, ow_bn_size);
  // reorder: [batch_oc_outer_oh_outer_fused, oh_inner, ow_outer, ow_inner,
  // oc_inner, ic, kh, kw] -> [batch_oc_outer_oh_outer_fused, oh_inner,
  // ow_outer, ow_inner, oc_inner, ic, kh, kw]
  stages[CC]->Reorder({2, 1});

  // split ic
  // CC: [batch_oc_outer_oh_outer_fused, ow_outer, oh_inner, ow_inner, oc_inner,
  // ic, kh, kw]
  stages[CC]->Split(5, ic_bn_size);
  // reorder: [batch_oc_outer_oh_outer_fused, ow_outer, oh_inner, ow_inner,
  // oc_inner, ic_outer, ic_inner, kh, kw] -> [batch_oc_outer_oh_outer_fused,
  // ow_outer, ic_outer, ic_inner, oh_inner, ow_inner, oc_inner, kh, kw]
  auto oh_inner = stages[CC]->axis(2);
  auto ow_inner = stages[CC]->axis(3);
  auto oc_inner = stages[CC]->axis(4);
  auto ic_outer = stages[CC]->axis(5);
  auto ic_inner = stages[CC]->axis(6);
  stages[CC]->Reorder({ic_outer, ic_inner, oh_inner, ow_inner, oc_inner});
  VLOG(3) << "stages[CC]->transformed_domain()"
          << stages[CC]->transformed_domain();
  stages[CC]->Vectorize(stages[CC]->n_out_dims() - 3,
                        CC->shape.back().as_int32());
  // unroll ow_inner, oh_inner
  VLOG(3) << stages[CC]->transformed_domain();
  // CC_init
  auto CC_init = CC->GetInitTensor(stages, target);
  stages[CC_init]->Vectorize(stages[CC_init]->n_out_dims() - 1,
                             CC_init->shape.back().as_int32());
  stages[CC]->Unroll(stages[CC]->n_out_dims() - 4);
  stages[CC]->Unroll(stages[CC]->n_out_dims() - 5);
  stages[CC_init]->Unroll(stages[CC_init]->n_out_dims() - 2);

  // res
  // n, oc, oh, ow
  if (res.defined()) {
    stages[res]->Split(1, oc_bn_size);
    stages[res]->Split(3, oh_bn_size);
    stages[res]->Split(5, ow_bn_size);
    // reorder: [n, oc_outer, oc_inner, oh_outer, oh_inner, ow_outer, ow_inner]
    // -> [n, oc_outer, oh_outer, ow_outer, oh_inner, ow_inner, oc_inner]
    auto oc_inner1 = stages[res]->axis(2);
    auto oh_outer1 = stages[res]->axis(3);
    auto oh_inner1 = stages[res]->axis(4);
    auto ow_outer1 = stages[res]->axis(5);
    auto ow_inner1 = stages[res]->axis(6);
    stages[res]->Reorder(
        {oh_outer1, ow_outer1, oh_inner1, ow_inner1, oc_inner1});
    // stages[res]->Fuse({0, 1, 2});
    // Todo: computeAt according to forloops' range
    // stages[packed_out]->ComputeAt2(stages[res], 2);
    VLOG(3) << "stages[res]->transformed_domain()"
            << stages[res]->transformed_domain();
  }
}

void Conv2d_NCHWc_1X1_Schedule_CPU_Nofuse(poly::StageMap stages,
                                          const ir::Tensor &res,
                                          ir::Tensor &packed_out,
                                          const ir::Tensor &input_pad,
                                          const ir::Tensor &weights_dilation,
                                          const ir::Tensor &data,
                                          const common::Target &target) {
  CHECK(target.arch == Target::Arch::X86)
      << "Conv2d_NCHWc_1X1_Schedule_CPU_Nofuse schedule only used in x86";
  CHECK(packed_out.defined());
  CHECK(input_pad.defined());
  auto type = packed_out->type();
  absl::flat_hash_map<std::string, int> conv2d_factors;
  CHECK_EQ(packed_out->shape.size(), 5U)
      << "packed_out's shape size should be 5";
  Expr h_out = common::AutoSimplify(packed_out->shape[2]);
  Expr w_out = common::AutoSimplify(packed_out->shape[3]);
  int oh = h_out.as_int32();
  int ow = w_out.as_int32();
  int basic_split_factor = GetBasicFactor(type, target);
  GetConv2d1x1Factors(&conv2d_factors, -1, -1, oh, ow, type, target);
  int oh_bn_size = conv2d_factors["oh_bn"];
  int ow_bn_size = conv2d_factors["ow_bn"];

  auto input_shape = input_pad->shape;
  int shape_size = input_shape.size();
  CHECK_EQ(shape_size, 5U) << "input shape size should be 5";
  Expr oc_bn = common::AutoSimplify(packed_out->shape.back());
  Expr ic_bn = common::AutoSimplify(input_shape.back());
  int oc_bn_size = oc_bn.as_int32();
  int ic_bn_size = ic_bn.as_int32();
  VLOG(3) << "ow_bn_size" << ow_bn_size;
  VLOG(3) << "oc_bn_size" << oc_bn_size;
  VLOG(3) << "ic_bn_size" << ic_bn_size;

  // data
  if (data.defined()) {
    stages[data]->ComputeInline();
  }
  // weights
  if (weights_dilation.defined()) {
    CHECK_GE(stages[weights_dilation]->n_out_dims(), 3U)
        << "weights_dilation's out_dims should be more than 3";
    // Reorder: [oc_outer, ic_outer, oh, ow, ic_inner, oc_inner] ->
    // [oc_outer, oh, ic_outer, ow, ic_inner, oc_inner]
    stages[weights_dilation]->Reorder({2, 1});
  }

  // packed_out
  auto CC = stages[packed_out]->CacheWrite("global", stages, packed_out);
  VLOG(3) << "stages[packed_out]->transformed_domain()"
          << stages[packed_out]->transformed_domain();
  VLOG(3) << "stages[CC]->transformed_domain()"
          << stages[CC]->transformed_domain();
  // packed_out: [batch, oc_outer, oh, ow, oc_inner]
  // split oh, ow
  stages[packed_out]->Split(2, oh_bn_size);
  stages[packed_out]->Split(4, ow_bn_size);

  // CC: [batch, oc_outer, oh, ow, oc_inner]
  // packed_out: [batch, oc_outer, oh_outer, oh_inner, ow_outer, ow_inner,
  // oc_inner]
  stages[CC]->ComputeAt2(stages[packed_out], 2);
  VLOG(3) << "stages[packed_out]->transformed_domain()"
          << stages[packed_out]->transformed_domain();
  VLOG(3) << "stages[CC]->transformed_domain()"
          << stages[CC]->transformed_domain();
  // tempory solution because reordering before computeAt may be wrong
  // reorder: [batch, oc_outer, oh_outer, oh_inner, ow_outer, ow_inner,
  // oc_inner] -> [batch, oc_outer, oh_outer, ow_outer, oh_inner, ow_inner,
  // oc_inner]
  stages[packed_out]->Reorder({4, 3});
  stages[packed_out]->Vectorize(stages[packed_out]->n_out_dims() - 1,
                                packed_out->shape.back().as_int32());

  // split oh, ow
  // CC: [batch, oc_outer, oh_outer, oh_inner, ow, oc_inner, ic, kh, kw]
  stages[CC]->Split(4, ow_bn_size);
  // CC: [batch, oc_outer, oh_outer, oh_inner, ow_outer, ow_inner, oc_inner, ic,
  // kh, kw] split ic
  stages[CC]->Split(7, ic_bn_size);

  // reorder: [batch, oc_outer, oh_outer, oh_inner, ow_outer, ow_inner,
  // oc_inner, ic_outer, ic_inner, kh, kw] -> [batch, oc_outer, oh_outer,
  // ow_outer, ic_outer, ic_inner, oh_inner, ow_inner, oc_inner, kh, kw]
  auto oh_inner = stages[CC]->axis(3);
  auto ow_outer = stages[CC]->axis(4);
  auto ow_inner = stages[CC]->axis(5);
  auto oc_inner = stages[CC]->axis(6);
  auto ic_outer = stages[CC]->axis(7);
  auto ic_inner = stages[CC]->axis(8);
  stages[CC]->Reorder(
      {ow_outer, ic_outer, ic_inner, oh_inner, ow_inner, oc_inner});
  stages[CC]->Vectorize(stages[CC]->n_out_dims() - 3,
                        CC->shape.back().as_int32());
  VLOG(3) << "stages[CC]->transformed_domain()"
          << stages[CC]->transformed_domain();
  // CC_init
  auto CC_init = CC->GetInitTensor(stages, target);
  stages[CC_init]->Vectorize(stages[CC_init]->n_out_dims() - 1,
                             CC_init->shape.back().as_int32());

  // res
  // n, oc, oh, ow
  if (res.defined()) {
    stages[res]->Split(1, oc_bn_size);
    stages[res]->Split(3, oh_bn_size);
    stages[res]->Split(5, ow_bn_size);
    // reorder: [n, oc_outer, oc_inner, oh_outer, oh_inner, ow_outer, ow_inner]
    // -> [n, oc_outer, oh_outer, ow_outer, oh_inner, ow_inner, oc_inner]
    auto oc_inner1 = stages[res]->axis(2);
    auto oh_outer1 = stages[res]->axis(3);
    auto oh_inner1 = stages[res]->axis(4);
    auto ow_outer1 = stages[res]->axis(5);
    auto ow_inner1 = stages[res]->axis(6);
    stages[res]->Reorder(
        {oh_outer1, ow_outer1, oh_inner1, ow_inner1, oc_inner1});
    VLOG(3) << "stages[res]->transformed_domain()"
            << stages[res]->transformed_domain();
  }
}

void Conv2d_NCHWc_Schedule_CPU_Nofuse(poly::StageMap stages,
                                      const ir::Tensor &res,
                                      ir::Tensor &packed_out,
                                      const ir::Tensor &input_pad,
                                      const ir::Tensor &weights_dilation,
                                      const ir::Tensor &data,
                                      const common::Target &target) {
  CHECK(target.arch == Target::Arch::X86)
      << "Conv2d_NCHWc_Schedule_CPU_Nofuse schedule only used in x86";
  CHECK(packed_out.defined());
  CHECK(input_pad.defined());
  auto type = packed_out->type();
  absl::flat_hash_map<std::string, int> conv2d_factors;
  CHECK_EQ(packed_out->shape.size(), 5U)
      << "packed_out's shape size should be 5";
  Expr w_out = common::AutoSimplify(packed_out->shape[3]);
  int ow = w_out.as_int32();
  int basic_split_factor = GetBasicFactor(type, target);
  GetConv2dFactors(&conv2d_factors, -1, -1, -1, -1, ow, type, target);
  int ow_bn_size = conv2d_factors["ow_bn"];

  auto input_shape = input_pad->shape;
  int shape_size = input_shape.size();
  CHECK_EQ(shape_size, 5U) << "input shape size should be 5";
  Expr oc_bn = common::AutoSimplify(packed_out->shape.back());
  Expr ic_bn = common::AutoSimplify(input_shape.back());
  int oc_bn_size = oc_bn.as_int32();
  int ic_bn_size = ic_bn.as_int32();
  VLOG(3) << "ow_bn_size " << ow_bn_size;
  VLOG(3) << "oc_bn_size " << oc_bn_size;
  VLOG(3) << "ic_bn_size " << ic_bn_size;

  // data
  if (data.defined()) {
    stages[data]->ComputeInline();
  }
  // weights
  if (weights_dilation.defined()) {
    CHECK_GE(stages[weights_dilation]->n_out_dims(), 3U)
        << "weights_dilation's out_dims should be more than 3";
    // Reorder: [oc_outer, ic_outer, oh, ow, ic_inner, oc_inner] ->
    // [oc_outer, oh, ic_outer, ow, ic_inner, oc_inner]
    stages[weights_dilation]->Reorder({2, 1});
  }
  // packed_out
  auto CC = stages[packed_out]->CacheWrite("global", stages, packed_out);
  VLOG(3) << "stages[packed_out]->transformed_domain()"
          << stages[packed_out]->transformed_domain();
  VLOG(3) << "stages[CC]->transformed_domain()"
          << stages[CC]->transformed_domain();
  // packed_out: [batch, oc_outer, oh, ow, oc_inner]
  // split ow
  stages[packed_out]->Split(3, ow_bn_size);
  stages[packed_out]->Vectorize(stages[packed_out]->n_out_dims() - 1,
                                packed_out->shape.back().as_int32());

  // CC: [batch, oc_outer, oh, ow, oc_inner]
  // packed_out: [batch, oc_outer, oh, ow_outer, ow_inner, oc_inner]
  // not computeAt ow_outer but oh
  stages[CC]->ComputeAt2(stages[packed_out], 2);
  VLOG(3) << "stages[packed_out]->transformed_domain()"
          << stages[packed_out]->transformed_domain();
  VLOG(3) << "stages[CC]->transformed_domain()"
          << stages[CC]->transformed_domain();
  // split ow
  stages[CC]->Split(3, ow_bn_size);
  // CC: [batch, oc_outer, oh, ow_outer, ow_inner, oc_inner, ic, kh, kw]
  // split ic
  stages[CC]->Split(6, ic_bn_size);
  // reorder: [batch, oc_outer, oh, ow_outer, ow_inner, oc_inner, ic_outer,
  // ic_inner, kh, kw] -> [batch, oc_outer, oh, ow_outer, ic_outer, kh, kw,
  // ic_inner, ow_inner, oc_inner]
  auto ow_inner = stages[CC]->axis(4);
  auto oc_inner = stages[CC]->axis(5);
  auto ic_outer = stages[CC]->axis(6);
  auto ic_inner = stages[CC]->axis(7);
  auto kh = stages[CC]->axis(8);
  auto kw = stages[CC]->axis(9);
  stages[CC]->Reorder({ic_outer, kh, kw, ic_inner, ow_inner, oc_inner});
  stages[CC]->Vectorize(stages[CC]->n_out_dims() - 1,
                        CC->shape.back().as_int32());
  VLOG(3) << "stages[CC]->transformed_domain()"
          << stages[CC]->transformed_domain();
  // CC_init
  auto CC_init = CC->GetInitTensor(stages, target);
  stages[CC_init]->Vectorize(stages[CC_init]->n_out_dims() - 1,
                             CC_init->shape.back().as_int32());

  // res
  // n, oc, oh, ow
  if (res.defined()) {
    stages[res]->Split(1, oc_bn_size);
    stages[res]->Split(4, ow_bn_size);
    // Reorder: [n, oc_outer, oc_inner, oh, ow_outer, ow_inner] ->
    // [n, oc_outer, oh, ow_outer, ow_inner, oc_inner]
    auto oc_inner1 = stages[res]->axis(2);
    auto oh1 = stages[res]->axis(3);
    auto ow_outer1 = stages[res]->axis(4);
    auto ow_inner1 = stages[res]->axis(5);
    stages[res]->Reorder({oh1, ow_outer1, ow_inner1, oc_inner1});
    VLOG(3) << "stages[res]->transformed_domain()"
            << stages[res]->transformed_domain();
  }
}

void Conv2d_NCHWc_Schedule_CPU(poly::StageMap stages,
                               const ir::Tensor &res,
                               ir::Tensor &packed_out,
                               const ir::Tensor &input_pad,
                               const ir::Tensor &weights_dilation,
                               const ir::Tensor &data,
                               const common::Target &target,
                               const std::string &key,
                               bool do_padding) {
  CHECK(target.arch == Target::Arch::X86)
      << "Conv2d_NCHWc_Schedule_CPU schedule only used in x86";
  CHECK(packed_out.defined());
  CHECK(input_pad.defined());
  auto type = packed_out->type();
  CHECK_EQ(packed_out->shape.size(), 5U)
      << "packed_out's shape size should be 5";
  Expr w_out = common::AutoSimplify(packed_out->shape[3]);
  int ow = w_out.as_int32();
  auto input_shape = input_pad->shape;
  int shape_size = input_shape.size();
  CHECK_EQ(shape_size, 5U) << "input shape size should be 5";
  Expr oc_bn = common::AutoSimplify(packed_out->shape.back());
  Expr ic_bn = common::AutoSimplify(input_shape.back());
  int oc_bn_size = oc_bn.as_int32();
  int ic_bn_size = ic_bn.as_int32();

  absl::flat_hash_map<std::string, int> conv2d_factors;
  GetConv2dFactors(&conv2d_factors, -1, -1, -1, -1, ow, type, target, key);
  int ow_bn_size = conv2d_factors["ow_bn"];
  VLOG(3) << "ow_bn_size " << ow_bn_size;
  VLOG(3) << "oc_bn_size " << oc_bn_size;
  VLOG(3) << "ic_bn_size " << ic_bn_size;
  int unroll_kw = 0;
  if (conv2d_factors.count("unroll_kw")) {
    unroll_kw = conv2d_factors["unroll_kw"];
  }
  VLOG(3) << "unroll_kw " << unroll_kw;
  // data
  if (data.defined()) {
    CHECK_GE(stages[data]->n_out_dims(), 3U)
        << "data's out_dims should be more than 3";
    stages[data]->Fuse({0, 1, 2});
    stages[data]->ComputeInline();
  }
  // input_pad
  if (do_padding) {
    CHECK_GE(stages[input_pad]->n_out_dims(), 3U)
        << "input_pad's out_dims should be more than 3";
    stages[input_pad]->Fuse({0, 1, 2});
    stages[input_pad]->Vectorize(stages[input_pad]->n_out_dims() - 1,
                                 input_pad->shape.back().as_int32());
  } else {
    stages[input_pad]->ComputeInline();
  }
  // weights
  if (weights_dilation.defined()) {
    CHECK_GE(stages[weights_dilation]->n_out_dims(), 3U)
        << "weights_dilation's out_dims should be more than 3";
    // oc_outer, ic_outer, oh, ow, ic_inner, oc_inner -> oc_outer, oh, ic_outer,
    // ow, ic_inner, oc_inner
    stages[weights_dilation]->Reorder({2, 1});
    stages[weights_dilation]->Fuse({0, 1});
  }
  // packed_out
  auto CC = stages[packed_out]->CacheWrite("global", stages, packed_out);
  VLOG(3) << "stages[packed_out]->transformed_domain()"
          << stages[packed_out]->transformed_domain();
  VLOG(3) << "stages[CC]->transformed_domain()"
          << stages[CC]->transformed_domain();
  // packed_out: [batch, oc_outer, oh, ow, oc_inner]
  // split ow
  stages[packed_out]->Split(3, ow_bn_size);
  stages[packed_out]->Fuse({0, 1, 2});
  stages[packed_out]->Vectorize(stages[packed_out]->n_out_dims() - 1,
                                packed_out->shape.back().as_int32());

  // CC
  stages[CC]->ComputeAt2(stages[packed_out], 1);
  VLOG(3) << "cache write shape: " << utils::Join(CC->shape, ", ");
  VLOG(3) << "stages[packed_out]->transformed_domain()"
          << stages[packed_out]->transformed_domain();
  VLOG(3) << "stages[CC]->transformed_domain()"
          << stages[CC]->transformed_domain();
  // CC: [batch_oc_outer_oh_fused, ow_outer, ow_inner, oc_inner, ic, kh, kw]
  // for fused_axes' copy transform, not split ow again
  // split ic
  stages[CC]->Split(4, ic_bn_size);
  // reorder: [batch_oc_outer_oh_fused, ow_outer, ow_inner, oc_inner, ic_outer,
  // ic_inner, kh, kw] -> [batch_oc_outer_oh_fused, ow_outer, ic_outer, kh, kw,
  // ic_inner, ow_inner, oc_inner]
  auto ow_inner = stages[CC]->axis(2);
  auto oc_inner = stages[CC]->axis(3);
  auto ic_outer = stages[CC]->axis(4);
  auto ic_inner = stages[CC]->axis(5);
  auto kh = stages[CC]->axis(6);
  auto kw = stages[CC]->axis(7);
  if (unroll_kw) {
    stages[CC]->Reorder({ic_outer, kh, ic_inner, kw, ow_inner, oc_inner});
    stages[CC]->Unroll(kw);
  } else {
    stages[CC]->Reorder({ic_outer, kh, kw, ic_inner, ow_inner, oc_inner});
  }
  stages[CC]->Vectorize(stages[CC]->n_out_dims() - 1,
                        CC->shape.back().as_int32());
  VLOG(3) << "stages[CC]->transformed_domain()"
          << stages[CC]->transformed_domain();
  // CC_init
  auto CC_init = CC->GetInitTensor(stages, target);
  stages[CC_init]->Vectorize(stages[CC_init]->n_out_dims() - 1,
                             CC_init->shape.back().as_int32());
  // unroll ow_inner
  stages[CC]->Unroll(stages[CC]->n_out_dims() - 2);
  stages[CC_init]->Unroll(stages[CC_init]->n_out_dims() - 2);

  // res
  // n, oc, oh, ow
  if (res.defined()) {
    stages[res]->Split(1, oc_bn_size);
    stages[res]->Split(4, ow_bn_size);
    // Reorder: [n, oc_outer, oc_inner, oh, ow_outer, ow_inner] ->
    // [n, oc_outer, oh, ow_outer, ow_inner, oc_inner]
    auto oc_inner1 = stages[res]->axis(2);
    auto oh1 = stages[res]->axis(3);
    auto ow_outer1 = stages[res]->axis(4);
    auto ow_inner1 = stages[res]->axis(5);
    stages[res]->Reorder({oh1, ow_outer1, ow_inner1, oc_inner1});
    // stages[res]->Fuse({0, 1, 2});
    // Todo: computeAt according to forloops' range
    // stages[packed_out]->ComputeAt2(stages[res], 2);
  }
}

void Depthwise_Conv2d_NCHWc_Schedule_CPU_Nofuse(
    poly::StageMap stages,
    const ir::Tensor &res,
    ir::Tensor &packed_out,
    const ir::Tensor &input_pad,
    const ir::Tensor &weights_dilation,
    const ir::Tensor &data,
    const common::Target &target,
    bool do_padding) {
  CHECK(target.arch == Target::Arch::X86)
      << "Depthwise_Conv2d_NCHWc_Schedule_CPU_Nofuse schedule only used in x86";
  CHECK(packed_out.defined());
  CHECK(input_pad.defined());
  auto type = packed_out->type();
  absl::flat_hash_map<std::string, int> conv2d_factors;
  CHECK_EQ(packed_out->shape.size(), 5U)
      << "packed_out's shape size should be 5";
  Expr w_out = common::AutoSimplify(packed_out->shape[3]);
  int ow = w_out.as_int32();
  int basic_split_factor = GetBasicFactor(type, target);
  GetConv2dFactors(&conv2d_factors, -1, -1, -1, -1, ow, type, target);
  int ow_bn_size = conv2d_factors["ow_bn"];

  auto input_shape = input_pad->shape;
  int shape_size = input_shape.size();
  CHECK_EQ(shape_size, 5U) << "input shape size should be 5";
  Expr oc_bn = common::AutoSimplify(packed_out->shape.back());
  Expr ic_bn = common::AutoSimplify(input_shape.back());
  int oc_bn_size = oc_bn.as_int32();
  int ic_bn_size = ic_bn.as_int32();
  VLOG(3) << "ow_bn_size " << ow_bn_size;
  VLOG(3) << "oc_bn_size " << oc_bn_size;
  VLOG(3) << "ic_bn_size " << ic_bn_size;

  // data
  if (data.defined()) {
    stages[data]->ComputeInline();
  }
  // input_pad
  if (!do_padding) {
    stages[input_pad]->ComputeInline();
  }
  // weights
  if (weights_dilation.defined()) {
    CHECK_GE(stages[weights_dilation]->n_out_dims(), 3U)
        << "weights_dilation's out_dims should be more than 3";
    // Reorder: [oc_outer, ic_outer, oh, ow, ic_inner, oc_inner] ->
    // [oc_outer, oh, ic_outer, ow, ic_inner, oc_inner]
    stages[weights_dilation]->Reorder({2, 1});
  }

  // packed_out
  auto CC = stages[packed_out]->CacheWrite("global", stages, packed_out);
  VLOG(3) << "stages[packed_out]->transformed_domain()"
          << stages[packed_out]->transformed_domain();
  VLOG(3) << "stages[CC]->transformed_domain()"
          << stages[CC]->transformed_domain();
  // packed_out: [batch, oc_outer, oh, ow, oc_inner]
  // split ow
  stages[packed_out]->Split(3, ow_bn_size);
  stages[packed_out]->Vectorize(stages[packed_out]->n_out_dims() - 1,
                                packed_out->shape.back().as_int32());

  // CC: [batch, oc_outer, oh, ow, oc_inner]
  // packed_out: [batch, oc_outer, oh, ow_outer, ow_inner, oc_inner]
  stages[CC]->ComputeAt2(stages[packed_out], 3);
  VLOG(3) << "stages[packed_out]->transformed_domain()"
          << stages[packed_out]->transformed_domain();
  VLOG(3) << "stages[CC]->transformed_domain()"
          << stages[CC]->transformed_domain();

  // CC: [batch, oc_outer, oh, ow_outer, ow_inner, oc_inner, fc, kh, kw]
  // batch, oc_outer, oh, ow_outer, kh, kw, ow_inner, oc_inner
  auto CC_ow_inner = stages[CC]->axis(4);
  auto CC_oc_inner = stages[CC]->axis(5);
  auto CC_fc = stages[CC]->axis(6);
  auto CC_kh = stages[CC]->axis(7);
  auto CC_kw = stages[CC]->axis(8);
  stages[CC]->Reorder({CC_fc, CC_kh, CC_kw, CC_ow_inner, CC_oc_inner});
  stages[CC]->Vectorize(stages[CC]->n_out_dims() - 1,
                        CC->shape.back().as_int32());
  VLOG(3) << "stages[CC]->transformed_domain()"
          << stages[CC]->transformed_domain();
  // CC_init
  auto CC_init = CC->GetInitTensor(stages, target);
  stages[CC_init]->Vectorize(stages[CC_init]->n_out_dims() - 1,
                             CC_init->shape.back().as_int32());

  // res
  // n, oc, oh, ow
  if (res.defined()) {
    stages[res]->Split(1, oc_bn_size);
    stages[res]->Split(4, ow_bn_size);
    // Reorder: [n, oc_outer, oc_inner, oh, ow_outer, ow_inner] ->
    // [n, oc_outer, oh, ow_outer, ow_inner, oc_inner]
    auto oc_inner1 = stages[res]->axis(2);
    auto oh1 = stages[res]->axis(3);
    auto ow_outer1 = stages[res]->axis(4);
    auto ow_inner1 = stages[res]->axis(5);
    stages[res]->Reorder({oh1, ow_outer1, ow_inner1, oc_inner1});
    VLOG(3) << "stages[res]->transformed_domain()"
            << stages[res]->transformed_domain();
  }
}

void CudaScheduleMul(poly::StageMap stages,
                     ir::Tensor output,
                     const std::vector<int> &output_shape,
                     const common::Target &target) {
  stages[output]->Split(1, 2);
  stages[output]->Bind(0, "blockIdx.x");
  stages[output]->Bind(1, "threadIdx.x");
}

inline void InputDirectConvCudaParam(
    absl::flat_hash_map<std::string,
                        absl::flat_hash_map<std::string, std::vector<int>>>
        &model_data,
    const std::string &key,
    const std::vector<std::vector<int>> &int_data) {
  CHECK_EQ(int_data.size(), 6UL);
  absl::flat_hash_map<std::string, std::vector<int>> schedule_data;
  schedule_data["rc"] = int_data[0];
  schedule_data["ry"] = int_data[1];
  schedule_data["rx"] = int_data[2];
  schedule_data["f"] = int_data[3];
  schedule_data["y"] = int_data[4];
  schedule_data["x"] = int_data[5];
  CHECK(model_data.count(key) == 0)
      << "Key " << key << "in conv cuda param already exists.";
  model_data[key] = schedule_data;
}

inline void InputWinogradConvCudaParam(
    absl::flat_hash_map<std::string,
                        absl::flat_hash_map<std::string, std::vector<int>>>
        &model_data,
    const std::string &key,
    const std::vector<std::vector<int>> &int_data) {
  CHECK_EQ(int_data.size(), 4UL);
  absl::flat_hash_map<std::string, std::vector<int>> schedule_data;
  schedule_data["rc"] = int_data[0];
  schedule_data["x"] = int_data[1];
  schedule_data["y"] = int_data[2];
  schedule_data["b"] = int_data[3];
  model_data[key] = schedule_data;
}

absl::flat_hash_map<std::string,
                    absl::flat_hash_map<std::string, std::vector<int>>>
CreateCudaParams() {
  absl::flat_hash_map<std::string,
                      absl::flat_hash_map<std::string, std::vector<int>>>
      model_data;
  // The format of serial data is:
  // hash_key: string = name of schedule + shape of input_pad + shape of weights
  // + shape of output value: vector of params
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 3 230 230 64 3 7 7 1 64 112 112",
      {{3, 1}, {7, 1}, {1, 7}, {1, 4, 8, 2}, {112, 1, 1, 1}, {1, 7, 16, 1}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 64 56 56 64 64 1 1 1 64 56 56",
      {{4, 16}, {1, 1}, {1, 1}, {1, 8, 8, 1}, {56, 1, 1, 1}, {1, 2, 28, 1}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 64 58 58 128 64 3 3 1 128 28 28",
      {{32, 2}, {1, 3}, {1, 3}, {4, 2, 16, 1}, {28, 1, 1, 1}, {1, 2, 14, 1}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 64 56 56 128 64 1 1 1 128 28 28",
      {{4, 16}, {1, 1}, {1, 1}, {2, 2, 32, 1}, {28, 1, 1, 1}, {1, 2, 14, 1}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 128 30 30 256 128 3 3 1 256 14 14",
      {{32, 4}, {1, 3}, {1, 3}, {8, 1, 16, 2}, {7, 1, 2, 1}, {1, 1, 7, 2}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 128 28 28 256 128 1 1 1 256 14 14",
      {{16, 8}, {1, 1}, {1, 1}, {8, 1, 16, 2}, {14, 1, 1, 1}, {1, 1, 14, 1}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 256 16 16 512 256 3 3 1 512 7 7",
      {{64, 4}, {1, 3}, {1, 3}, {32, 1, 16, 1}, {7, 1, 1, 1}, {1, 1, 7, 1}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 256 14 14 512 256 1 1 1 512 7 7",
      {{16, 16}, {1, 1}, {1, 1}, {16, 1, 32, 1}, {7, 1, 1, 1}, {1, 1, 7, 1}});

  // winograd
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 64 58 58 64 64 3 3 1 64 56 56",
      {{32, 2}, {1, 3}, {1, 3}, {4, 1, 8, 2}, {28, 1, 2, 1}, {1, 2, 7, 4}});
  // winograd
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 512 9 9 512 512 3 3 1 512 7 7",
      {{64, 8}, {1, 3}, {1, 3}, {32, 1, 16, 1}, {7, 1, 1, 1}, {1, 1, 7, 1}});
  // winograd
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 256 16 16 256 256 3 3 1 256 14 14",
      {{64, 4}, {1, 3}, {1, 3}, {16, 1, 16, 1}, {14, 1, 1, 1}, {1, 1, 14, 1}});
  // winograd
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 128 30 30 128 128 3 3 1 128 28 28",
      {{32, 4}, {1, 3}, {1, 3}, {8, 1, 16, 1}, {14, 1, 2, 1}, {1, 1, 7, 4}});

  // MobileNetV2 schedule params
  /*   InputDirectConvCudaParam(model_data,
                             "CudaDirectConvSchedule 1 3 226 226 32 3 3 3 1 32
     112 112",
                             {{3, 1}, {1, 3}, {1, 3}, {-1, 2, 8, 2}, {-1, 1, 1,
     7}, {-1, 1, 16, 1}}); */
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 32 112 112 16 32 1 1 1 16 112 112",
      {{-1, 4},
       {-1, 1},
       {-1, 1},
       {-1, 2, 2, 4},
       {-1, 1, 2, 1},
       {-1, 1, 56, 2}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 32 112 112 32 32 1 1 1 32 112 112",
      {{-1, 4},
       {-1, 1},
       {-1, 1},
       {-1, 1, 4, 8},
       {-1, 1, 2, 1},
       {-1, 7, 16, 1}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 16 112 112 96 16 1 1 1 96 112 112",
      {{-1, 4},
       {-1, 1},
       {-1, 1},
       {-1, 4, 4, 2},
       {-1, 2, 2, 1},
       {-1, 1, 16, 1}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 96 56 56 24 96 1 1 1 24 56 56",
      {{-1, 4},
       {-1, 1},
       {-1, 1},
       {-1, 3, 4, 2},
       {-1, 1, 1, 1},
       {-1, 1, 28, 2}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 24 56 56 144 24 1 1 1 144 56 56",
      {{-1, 6},
       {-1, 1},
       {-1, 1},
       {-1, 9, 4, 2},
       {-1, 2, 1, 1},
       {-1, 1, 56, 1}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 144 56 56 24 144 1 1 1 24 56 56",
      {{-1, 12},
       {-1, 1},
       {-1, 1},
       {-1, 1, 8, 3},
       {-1, 1, 1, 1},
       {-1, 2, 14, 2}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 144 28 28 32 144 1 1 1 32 28 28",
      {{-1, 8},
       {-1, 1},
       {-1, 1},
       {-1, 4, 8, 1},
       {-1, 1, 1, 1},
       {-1, 1, 14, 2}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 32 28 28 192 32 1 1 1 192 28 28",
      {{-1, 4},
       {-1, 1},
       {-1, 1},
       {-1, 6, 4, 1},
       {-1, 2, 1, 2},
       {-1, 1, 28, 1}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 192 28 28 32 192 1 1 1 32 28 28",
      {{-1, 48},
       {-1, 1},
       {-1, 1},
       {-1, 4, 8, 1},
       {-1, 1, 1, 1},
       {-1, 1, 28, 1}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 192 14 14 64 192 1 1 1 64 14 14",
      {{-1, 12},
       {-1, 1},
       {-1, 1},
       {-1, 1, 8, 2},
       {-1, 2, 1, 1},
       {-1, 1, 14, 1}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 64 14 14 384 64 1 1 1 384 14 14",
      {{-1, 4}, {-1, 1}, {-1, 1}, {-1, 2, 4, 3}, {-1, 1, 7, 1}, {-1, 1, 7, 2}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 384 14 14 64 384 1 1 1 64 14 14",
      {{-1, 48},
       {-1, 1},
       {-1, 1},
       {-1, 2, 16, 1},
       {-1, 1, 2, 1},
       {-1, 1, 7, 2}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 384 14 14 96 384 1 1 1 96 14 14",
      {{-1, 12},
       {-1, 1},
       {-1, 1},
       {-1, 2, 6, 1},
       {-1, 1, 2, 1},
       {-1, 1, 7, 2}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 96 14 14 576 96 1 1 1 576 14 14",
      {{-1, 6}, {-1, 1}, {-1, 1}, {-1, 1, 6, 6}, {-1, 1, 7, 1}, {-1, 1, 7, 2}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 576 14 14 96 576 1 1 1 96 14 14",
      {{-1, 24},
       {-1, 1},
       {-1, 1},
       {-1, 1, 8, 3},
       {-1, 1, 2, 1},
       {-1, 1, 7, 2}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 576 7 7 160 576 1 1 1 160 7 7",
      {{-1, 36},
       {-1, 1},
       {-1, 1},
       {-1, 2, 2, 2},
       {-1, 1, 7, 1},
       {-1, 1, 7, 1}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 160 7 7 960 160 1 1 1 960 7 7",
      {{-1, 16},
       {-1, 1},
       {-1, 1},
       {-1, 6, 4, 1},
       {-1, 1, 7, 1},
       {-1, 1, 7, 1}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 960 7 7 160 960 1 1 1 160 7 7",
      {{-1, 60},
       {-1, 1},
       {-1, 1},
       {-1, 2, 4, 1},
       {-1, 1, 7, 1},
       {-1, 1, 7, 1}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 960 7 7 320 960 1 1 1 320 7 7",
      {{-1, 20},
       {-1, 1},
       {-1, 1},
       {-1, 2, 2, 2},
       {-1, 1, 7, 1},
       {-1, 1, 7, 1}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 320 7 7 1280 320 1 1 1 1280 7 7",
      {{-1, 16},
       {-1, 1},
       {-1, 1},
       {-1, 2, 16, 1},
       {-1, 7, 1, 1},
       {-1, 1, 7, 1}});

  // EfficientNet schedule params
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 3 228 228 32 3 3 3 1 32 113 113",
      {{-1, 1},
       {-1, 1},
       {-1, 3},
       {-1, 32, 1, 1},
       {-1, 1, 1, 1},
       {-1, 1, 113, 1}});
  InputDirectConvCudaParam(model_data,
                           "CudaDirectConvSchedule 1 32 1 1 8 32 1 1 1 8 1 1",
                           {{-1, 16},
                            {-1, 1},
                            {-1, 1},
                            {-1, 1, 4, 1},
                            {-1, 1, 1, 1},
                            {-1, 1, 1, 1}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 8 1 1 32 8 1 1 1 32 1 1",
      {{-1, 8}, {-1, 1}, {-1, 1}, {-1, 1, 8, 4}, {-1, 1, 1, 1}, {-1, 1, 1, 1}});
  InputDirectConvCudaParam(model_data,
                           "CudaDirectConvSchedule 1 96 1 1 4 96 1 1 1 4 1 1",
                           {{-1, 48},
                            {-1, 1},
                            {-1, 1},
                            {-1, 1, 4, 1},
                            {-1, 1, 1, 1},
                            {-1, 1, 1, 1}});
  InputDirectConvCudaParam(model_data,
                           "CudaDirectConvSchedule 1 4 1 1 96 4 1 1 1 96 1 1",
                           {{-1, 2},
                            {-1, 1},
                            {-1, 1},
                            {-1, 12, 1, 1},
                            {-1, 1, 1, 1},
                            {-1, 1, 1, 1}});
  InputDirectConvCudaParam(model_data,
                           "CudaDirectConvSchedule 1 144 1 1 6 144 1 1 1 6 1 1",
                           {{-1, 48},
                            {-1, 1},
                            {-1, 1},
                            {-1, 1, 6, 1},
                            {-1, 1, 1, 1},
                            {-1, 1, 1, 1}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 6 1 1 144 6 1 1 1 144 1 1",
      {{-1, 2}, {-1, 1}, {-1, 1}, {-1, 2, 8, 1}, {-1, 1, 1, 1}, {-1, 1, 1, 1}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 144 28 28 40 144 1 1 1 40 28 28",
      {{-1, 16},
       {-1, 1},
       {-1, 1},
       {-1, 5, 8, 1},
       {-1, 1, 1, 1},
       {-1, 1, 28, 1}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 40 28 28 240 40 1 1 1 240 28 28",
      {{-1, 8},
       {-1, 1},
       {-1, 1},
       {-1, 2, 8, 3},
       {-1, 4, 1, 1},
       {-1, 1, 28, 1}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 240 1 1 10 240 1 1 1 10 1 1",
      {{-1, 60},
       {-1, 1},
       {-1, 1},
       {-1, 1, 5, 1},
       {-1, 1, 1, 1},
       {-1, 1, 1, 1}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 10 1 1 240 10 1 1 1 240 1 1",
      {{-1, 10},
       {-1, 1},
       {-1, 1},
       {-1, 1, 40, 3},
       {-1, 1, 1, 1},
       {-1, 1, 1, 1}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 240 28 28 40 240 1 1 1 40 28 28",
      {{-1, 16},
       {-1, 1},
       {-1, 1},
       {-1, 1, 8, 5},
       {-1, 1, 1, 1},
       {-1, 1, 28, 1}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 240 14 14 80 240 1 1 1 80 14 14",
      {{-1, 20},
       {-1, 1},
       {-1, 1},
       {-1, 2, 8, 1},
       {-1, 1, 2, 1},
       {-1, 1, 7, 2}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 80 14 14 480 80 1 1 1 480 14 14",
      {{-1, 16},
       {-1, 1},
       {-1, 1},
       {-1, 2, 8, 3},
       {-1, 1, 7, 1},
       {-1, 1, 7, 2}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 480 1 1 20 480 1 1 1 20 1 1",
      {{-1, 60},
       {-1, 1},
       {-1, 1},
       {-1, 1, 4, 1},
       {-1, 1, 1, 1},
       {-1, 1, 1, 1}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 20 1 1 480 20 1 1 1 480 1 1",
      {{-1, 5},
       {-1, 1},
       {-1, 1},
       {-1, 1, 32, 1},
       {-1, 1, 1, 1},
       {-1, 1, 1, 1}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 480 14 14 80 480 1 1 1 80 14 14",
      {{-1, 40},
       {-1, 1},
       {-1, 1},
       {-1, 2, 8, 1},
       {-1, 1, 2, 1},
       {-1, 1, 14, 1}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 480 14 14 112 480 1 1 1 112 14 14",
      {{-1, 20},
       {-1, 1},
       {-1, 1},
       {-1, 1, 8, 2},
       {-1, 1, 2, 1},
       {-1, 1, 7, 2}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 112 14 14 672 112 1 1 1 672 14 14",
      {{-1, 14},
       {-1, 1},
       {-1, 1},
       {-1, 1, 7, 6},
       {-1, 1, 7, 1},
       {-1, 1, 7, 2}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 672 1 1 28 672 1 1 1 28 1 1",
      {{-1, 28},
       {-1, 1},
       {-1, 1},
       {-1, 1, 7, 1},
       {-1, 1, 1, 1},
       {-1, 1, 1, 1}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 28 1 1 672 28 1 1 1 672 1 1",
      {{-1, 28},
       {-1, 1},
       {-1, 1},
       {-1, 1, 16, 1},
       {-1, 1, 1, 1},
       {-1, 1, 1, 1}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 672 14 14 112 672 1 1 1 112 14 14",
      {{-1, 14},
       {-1, 1},
       {-1, 1},
       {-1, 2, 4, 2},
       {-1, 1, 2, 1},
       {-1, 1, 7, 2}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 672 7 7 192 672 1 1 1 192 7 7",
      {{-1, 28},
       {-1, 1},
       {-1, 1},
       {-1, 1, 2, 3},
       {-1, 1, 7, 1},
       {-1, 1, 7, 1}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 192 7 7 1152 192 1 1 1 1152 7 7",
      {{-1, 24},
       {-1, 1},
       {-1, 1},
       {-1, 1, 12, 3},
       {-1, 7, 1, 1},
       {-1, 1, 7, 1}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 1152 1 1 48 1152 1 1 1 48 1 1",
      {{-1, 576},
       {-1, 1},
       {-1, 1},
       {-1, 1, 3, 1},
       {-1, 1, 1, 1},
       {-1, 1, 1, 1}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 48 1 1 1152 48 1 1 1 1152 1 1",
      {{-1, 12},
       {-1, 1},
       {-1, 1},
       {-1, 1, 32, 1},
       {-1, 1, 1, 1},
       {-1, 1, 1, 1}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 1152 7 7 192 1152 1 1 1 192 7 7",
      {{-1, 36},
       {-1, 1},
       {-1, 1},
       {-1, 1, 2, 6},
       {-1, 1, 7, 1},
       {-1, 1, 7, 1}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 1152 7 7 320 1152 1 1 1 320 7 7",
      {{-1, 12},
       {-1, 1},
       {-1, 1},
       {-1, 1, 2, 4},
       {-1, 1, 7, 1},
       {-1, 1, 7, 1}});

  // FaceDet schedule params
  /*   InputDirectConvCudaParam(model_data,
                                 "CudaDirectConvSchedule 1 3 242 322 16 3 3 3 1
     16 120 160",
                                 {{-1, 1}, {-1, 3}, {-1, 3}, {-1, 2, 4, 2}, {-1,
     1, 1, 5}, {-1, 1, 32, 1}}); */
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 16 120 160 32 16 1 1 1 32 120 160",
      {{-1, 4},
       {-1, 1},
       {-1, 1},
       {-1, 8, 4, 1},
       {-1, 1, 1, 1},
       {-1, 5, 32, 1}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 32 60 80 32 32 1 1 1 32 60 80",
      {{-1, 4},
       {-1, 1},
       {-1, 1},
       {-1, 8, 4, 1},
       {-1, 3, 1, 1},
       {-1, 1, 40, 1}});
  /*   InputDirectConvCudaParam(model_data,
                                 "CudaDirectConvSchedule 1 32 30 40 64 32 1 1 1
     64 30 40",
                                 {{-1, 8}, {-1, 1}, {-1, 1}, {-1, 2, 8, 2}, {-1,
     1, 1, 3}, {-1, 1, 20, 1}}); */
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 64 30 40 64 64 1 1 1 64 30 40",
      {{-1, 8}, {-1, 1}, {-1, 1}, {-1, 2, 8, 2}, {-1, 1, 2, 1}, {-1, 5, 8, 1}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 64 30 40 8 64 1 1 1 8 30 40",
      {{-1, 8}, {-1, 1}, {-1, 1}, {-1, 2, 4, 1}, {-1, 1, 2, 1}, {-1, 1, 8, 1}});
  /*   InputDirectConvCudaParam(model_data,
                                 "CudaDirectConvSchedule 1 8 32 42 12 8 3 3 1 12
    30 40",
                                 {{-1, 4}, {-1, 3}, {-1, 3}, {-1, 1, 12, 1},
    {-1, 1, 1, 3}, {-1, 1, 10, 1}}); InputDirectConvCudaParam(model_data,
                                 "CudaDirectConvSchedule 1 8 32 42 16 8 3 3 1 16
    30 40",
                                 {{-1, 8}, {-1, 3}, {-1, 3}, {-1, 1, 16, 1},
    {-1, 3, 1, 2}, {-1, 1, 4, 2}}); */
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 16 36 46 16 16 3 3 1 16 30 40",
      {{-1, 16},
       {-1, 1},
       {-1, 1},
       {-1, 2, 8, 1},
       {-1, 1, 2, 1},
       {-1, 1, 8, 1}});
  /*   InputDirectConvCudaParam(model_data,
                             "CudaDirectConvSchedule 1 16 34 44 16 16 3 3 1 16
     30 40",
                             {{-1, 4}, {-1, 3}, {-1, 3}, {-1, 1, 4, 2}, {-1, 3,
     2, 1}, {-1, 1, 20, 1}}); */
  /*   InputDirectConvCudaParam(model_data,
                                 "CudaDirectConvSchedule 1 12 32 42 16 12 3 3 1
     16 30 40",
                                 {{-1, 4}, {-1, 3}, {-1, 3}, {-1, 1, 16, 1},
     {-1, 1, 2, 3}, {-1, 1, 2, 2}}); */
  /*   InputDirectConvCudaParam(model_data,
                             "CudaDirectConvSchedule 1 16 40 50 16 16 3 3 1 16
     30 40",
                             {{-1, 4}, {-1, 1}, {-1, 3}, {-1, 1, 1, 8}, {-1, 1,
     3, 1}, {-1, 1, 40, 1}}); */
  /*   InputDirectConvCudaParam(model_data,
                               "CudaDirectConvSchedule 1 48 30 40 64 48 1 1 1 64
     30 40",
                               {{-1, 8}, {-1, 1}, {-1, 1}, {-1, 2, 8, 2}, {-1,
     1, 1, 3}, {-1, 1, 20, 1}}); */
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 64 30 40 12 64 1 1 1 12 30 40",
      {{-1, 8},
       {-1, 1},
       {-1, 1},
       {-1, 1, 4, 3},
       {-1, 1, 3, 1},
       {-1, 1, 10, 1}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 64 30 40 6 64 1 1 1 6 30 40",
      {{-1, 8},
       {-1, 1},
       {-1, 1},
       {-1, 3, 2, 1},
       {-1, 1, 3, 1},
       {-1, 1, 10, 1}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 64 15 20 128 64 1 1 1 128 15 20",
      {{-1, 8},
       {-1, 1},
       {-1, 1},
       {-1, 2, 8, 2},
       {-1, 1, 3, 1},
       {-1, 1, 10, 2}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 128 15 20 128 128 1 1 1 128 15 20",
      {{-1, 8},
       {-1, 1},
       {-1, 1},
       {-1, 4, 8, 1},
       {-1, 1, 3, 1},
       {-1, 1, 10, 2}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 128 15 20 8 128 1 1 1 8 15 20",
      {{-1, 8},
       {-1, 1},
       {-1, 1},
       {-1, 1, 8, 1},
       {-1, 1, 1, 1},
       {-1, 1, 10, 2}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 128 15 20 4 128 1 1 1 4 15 20",
      {{-1, 16},
       {-1, 1},
       {-1, 1},
       {-1, 1, 4, 1},
       {-1, 1, 1, 1},
       {-1, 1, 20, 1}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 128 8 10 256 128 1 1 1 256 8 10",
      {{-1, 16},
       {-1, 1},
       {-1, 1},
       {-1, 1, 16, 2},
       {-1, 1, 8, 1},
       {-1, 1, 2, 1}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 256 8 10 256 256 1 1 1 256 8 10",
      {{-1, 8}, {-1, 1}, {-1, 1}, {-1, 4, 8, 1}, {-1, 1, 8, 1}, {-1, 1, 2, 1}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 256 8 10 64 256 1 1 1 64 8 10",
      {{-1, 16},
       {-1, 1},
       {-1, 1},
       {-1, 1, 16, 1},
       {-1, 1, 8, 1},
       {-1, 2, 1, 1}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 256 8 10 8 256 1 1 1 8 8 10",
      {{-1, 32},
       {-1, 1},
       {-1, 1},
       {-1, 1, 8, 1},
       {-1, 1, 2, 1},
       {-1, 1, 2, 1}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 256 8 10 4 256 1 1 1 4 8 10",
      {{-1, 32},
       {-1, 1},
       {-1, 1},
       {-1, 1, 4, 1},
       {-1, 1, 4, 1},
       {-1, 1, 2, 1}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 64 4 5 256 64 1 1 1 256 4 5",
      {{-1, 16},
       {-1, 1},
       {-1, 1},
       {-1, 1, 8, 1},
       {-1, 1, 4, 1},
       {-1, 1, 5, 1}});
  InputDirectConvCudaParam(
      model_data,
      "CudaDirectConvSchedule 1 256 6 7 12 256 3 3 1 12 4 5",
      {{-1, 32},
       {-1, 3},
       {-1, 3},
       {-1, 1, 4, 1},
       {-1, 1, 4, 1},
       {-1, 1, 1, 1}});
  InputDirectConvCudaParam(model_data,
                           "CudaDirectConvSchedule 1 256 6 7 6 256 3 3 1 6 4 5",
                           {{-1, 32},
                            {-1, 3},
                            {-1, 3},
                            {-1, 1, 2, 1},
                            {-1, 1, 4, 1},
                            {-1, 1, 1, 1}});

#ifndef CINN_WITH_CUDNN
  InputWinogradConvCudaParam(
      model_data,
      "CudaWinogradConvSchedule 1 512 9 9 512 512 3 3 1 512 7 7",
      {{32, 16}, {1, 1, 8, 2}, {8, 1, 16, 4}, {16, 1, 1, 1}});
  InputWinogradConvCudaParam(
      model_data,
      "CudaWinogradConvSchedule 1 256 6 7 12 256 3 3 1 12 4 5",
      {{-1, 256}, {-1, 1, 6, 1}, {-1, 1, 6, 1}, {-1, 1, 1, 1}});
  InputWinogradConvCudaParam(
      model_data,
      "CudaWinogradConvSchedule 1 256 6 7 6 256 3 3 1 12 4 5",
      {{-1, 256}, {-1, 1, 6, 1}, {-1, 1, 6, 1}, {-1, 1, 1, 1}});
  InputWinogradConvCudaParam(
      model_data,
      "CudaWinogradConvSchedule 1 12 32 42 16 12 3 3 1 16 30 40",
      {{-1, 12}, {-1, 2, 30, 1}, {-1, 4, 2, 2}, {-1, 1, 1, 1}});
  InputWinogradConvCudaParam(
      model_data,
      "CudaWinogradConvSchedule 1 8 32 42 12 8 3 3 1 12 30 40",
      {{-1, 8}, {-1, 2, 30, 1}, {-1, 1, 2, 6}, {-1, 1, 1, 1}});
  InputWinogradConvCudaParam(
      model_data,
      "CudaWinogradConvSchedule 1 8 32 42 16 8 3 3 1 16 30 40",
      {{-1, 4}, {-1, 2, 30, 1}, {-1, 1, 4, 4}, {-1, 1, 1, 1}});
#endif
  return model_data;
}

void CreateCudaSerialData(const std::string &file_name) {
  SaveSerialData(CreateCudaParams(), file_name);
}

int GetMaxSplitter(int a, int b) {
  while (a % b > 0) {
    b--;
  }
  return b;
}

void LoadSerialData(
    absl::flat_hash_map<std::string,
                        absl::flat_hash_map<std::string, std::vector<int>>>
        *params,
    const std::string &file_name) {
  proto::ModelData read_model_data;
  std::fstream input(file_name, std::ios::in | std::ios::binary);
  if (!read_model_data.ParseFromIstream(&input)) {
    std::cerr << "Failed to parse address book." << std::endl;
    exit(-1);
  }
  input.close();
  std::string test_write3;
  read_model_data.SerializeToString(&test_write3);
  auto read_model_map = read_model_data.data();
  for (auto &i : read_model_map) {
    auto read_schedule_map = i.second.data();
    absl::flat_hash_map<std::string, std::vector<int>> param_data;
    for (auto &j : read_schedule_map) {
      std::vector<int> temp_data;
      for (int k = 0; k < j.second.data_size(); k++) {
        temp_data.push_back(std::stoi(j.second.data(k)));
      }
      param_data[j.first] = temp_data;
    }
    (*params)[i.first] = param_data;
  }
}

void SaveSerialData(
    const absl::flat_hash_map<
        std::string,
        absl::flat_hash_map<std::string, std::vector<int>>> &model_data,
    const std::string &file_name) {
  proto::ModelData write_model_data;
  for (auto &i : model_data) {
    proto::ScheduleData write_schedule_data;
    for (auto &j : i.second) {
      proto::StringData write_vector_data;
      for (auto &k : j.second) {
        write_vector_data.add_data(std::to_string(k));
      }
      auto data_map = write_schedule_data.mutable_data();
      (*data_map)[j.first] = write_vector_data;
    }
    auto model_map = write_model_data.mutable_data();
    (*model_map)[i.first] = write_schedule_data;
    std::string test_write1;
    write_schedule_data.SerializeToString(&test_write1);
  }
  std::fstream output(file_name,
                      std::ios::out | std::ios::trunc | std::ios::binary);
  std::string test_write;
  write_model_data.SerializeToString(&test_write);
  if (!write_model_data.SerializeToOstream(&output)) {
    std::cerr << "Failed to write test_serial.log" << std::endl;
    exit(-1);
  }
  output.close();
}

void CudaScheduleDepthwiseConv(poly::StageMap stages,
                               ir::Tensor &output,
                               const common::Target &target) {
  auto OL = stages[output]->CacheWrite("local", stages, output);
  stages[output]->Bind(0, "blockIdx.x");
  stages[output]->Bind(1, "blockIdx.y");
  stages[output]->Bind(2, "blockIdx.z");
  stages[output]->Bind(3, "threadIdx.x");
  stages[OL]->ComputeAt(stages[output], 3);
  stages[OL]->Unroll(4);
  stages[OL]->Unroll(5);
}

void CudaScheduleConv(poly::StageMap stages,
                      ir::Tensor &input_pad,
                      ir::Tensor &weights,
                      ir::Tensor &output,
                      const common::Target &target) {
  auto &res = ScheduleParam::get_cuda_instance().GetParam();
  int n = output->shape[0].as_int32();
  int c = output->shape[1].as_int32();
  optim::Simplify(&(output->shape[2]));
  int h = output->shape[2].as_int32();
  optim::Simplify(&(output->shape[3]));
  int w = output->shape[3].as_int32();
  int rc = input_pad->shape[1].as_int32();

  std::string key = "CudaDirectConvSchedule " +
                    std::to_string(input_pad->shape[0].as_int32()) + " " +
                    std::to_string(input_pad->shape[1].as_int32()) + " " +
                    std::to_string(input_pad->shape[2].as_int32()) + " " +
                    std::to_string(input_pad->shape[3].as_int32()) + " " +
                    std::to_string(weights->shape[0].as_int32()) + " " +
                    std::to_string(weights->shape[1].as_int32()) + " " +
                    std::to_string(weights->shape[2].as_int32()) + " " +
                    std::to_string(weights->shape[3].as_int32()) + " " +
                    std::to_string(output->shape[0].as_int32()) + " " +
                    std::to_string(output->shape[1].as_int32()) + " " +
                    std::to_string(output->shape[2].as_int32()) + " " +
                    std::to_string(output->shape[3].as_int32());
  if (res.count(key) == 0) {
    VLOG(3) << "Didn't find saved param, key is: " << key;
  } else {
    VLOG(3) << "Find saved param! key is: " << key;
    CudaScheduleConv2(stages, input_pad, weights, output, target, key);
    return;
  }
  stages[input_pad]->ComputeInline();
  if (stages[weights]->has_expression()) {
    stages[weights]->ComputeInline();
  }
  int f_inner = GetInnerSplitter(c, h);
  int block_z = SplitEven(c / f_inner);
  int thread_z = c / f_inner / block_z;

  int rc_factor = SplitEven(rc);
  while (w * thread_z > 1024 && thread_z % 2 == 0) {
    thread_z = thread_z / 2;
    f_inner = f_inner * 2;
  }
  CHECK_LE(w * thread_z, 1024) << "Wrong Param of Conv2d!";
  auto OL = stages[output]->CacheWrite("local", stages, output);

  auto tx = stages[output]->axis(3);
  auto by = stages[output]->axis(2);
  auto tem_fi = stages[output]->Split(1, f_inner);
  auto &tem = std::get<0>(tem_fi);
  auto &fi = std::get<1>(tem_fi);

  auto bz_tz = stages[output]->Split(1, thread_z);
  auto &bz = std::get<0>(bz_tz);
  auto &tz = std::get<1>(bz_tz);

  stages[output]->Reorder({bz, by, tz, tx, fi});
  stages[output]->Bind(1, "blockIdx.z");
  stages[output]->Bind(2, "blockIdx.y");
  stages[output]->Bind(3, "threadIdx.z");
  stages[output]->Bind(4, "threadIdx.x");
  stages[OL]->ComputeAt(stages[output], 4);
  stages[OL]->Split(6, rc_factor);
}

void CudaScheduleConv2(poly::StageMap stages,
                       ir::Tensor &input_pad,
                       ir::Tensor &weights,
                       ir::Tensor &output,
                       const common::Target &target,
                       const std::string &key) {
  auto &res = ScheduleParam::get_cuda_instance().GetParam();
  stages[input_pad]->ComputeInline();
  optim::Simplify(&(output->shape[2]));
  optim::Simplify(&(output->shape[3]));

  std::vector<ir::Tensor> readers{output};
  auto PR = stages[input_pad]->CacheRead("shared", readers, stages);
  auto KR = stages[weights]->CacheRead("shared", readers, stages);
  auto OL = stages[output]->CacheWrite("local", stages, output);

  auto &x_param = res[key]["x"];
  auto &y_param = res[key]["y"];
  auto &f_param = res[key]["f"];
  auto &rx_param = res[key]["rx"];
  auto &ry_param = res[key]["ry"];
  auto &rc_param = res[key]["rc"];

  // x param is :  [1, 7, 16, 1]
  stages[output]->Split(3, x_param[3]);
  stages[output]->Split(3, x_param[2]);
  stages[output]->Split(3, x_param[1]);

  // y param is :  [112, 1, 1, 1]
  stages[output]->Split(2, y_param[3]);
  stages[output]->Split(2, y_param[2]);
  stages[output]->Split(2, y_param[1]);

  // f param is :  [1, 4, 8, 2]
  stages[output]->Split(1, f_param[3]);
  stages[output]->Split(1, f_param[2]);
  stages[output]->Split(1, f_param[1]);

  stages[output]->Reorder({0, 1, 5, 9, 2, 6, 10, 3, 7, 11, 4, 8, 12});
  stages[output]->Bind(1, "blockIdx.z");
  stages[output]->Bind(2, "blockIdx.y");
  stages[output]->Bind(3, "blockIdx.x");
  stages[output]->Bind(7, "threadIdx.z");
  stages[output]->Bind(8, "threadIdx.y");
  stages[output]->Bind(9, "threadIdx.x");
  stages[output]->Unroll(10);
  stages[output]->Unroll(11);
  stages[output]->Unroll(12);

  stages[OL]->ComputeAt(stages[output], 9);

  // rx param is :  [1, 7]
  stages[OL]->Split(15, rx_param[1]);
  // ry param is :  [7, 1]
  stages[OL]->Split(14, ry_param[1]);
  // rc param is :  [3, 1]
  stages[OL]->Split(13, rc_param[1]);

  stages[OL]->Reorder({13, 15, 17, 14, 16, 18, 10, 11, 12});

  auto OL_init = OL->GetInitTensor(stages, target);
  stages[PR]->ComputeAt(stages[OL], 12);
  stages[KR]->ComputeAt(stages[OL], 12);

  stages[PR]->SyncThreads(12, {OL_init}, stages);
  stages[KR]->CtrlDepend(PR);
  stages[KR]->SyncThreads(stages);

  if (stages[PR]->n_out_dims() == 18) {
    stages[PR]->Fuse({13, 14, 15, 16, 17});
  } else if (stages[PR]->n_out_dims() == 19) {
    stages[PR]->Fuse({13, 14, 15, 16, 17, 18});
  } else {
    LOG(FATAL) << "PR number of output dims is wrong: "
               << stages[PR]->n_out_dims();
  }

  if (stages[KR]->n_out_dims() == 18) {
    stages[KR]->Fuse({13, 14, 15, 16, 17});
  } else if (stages[KR]->n_out_dims() == 19) {
    stages[KR]->Fuse({13, 14, 15, 16, 17, 18});
  } else {
    LOG(FATAL) << "KR number of output dims is wrong: "
               << stages[KR]->n_out_dims();
  }
  int thread_z = f_param[2];
  int thread_x = x_param[2];
  if (stages[PR]->GetDimRange(13) <= thread_z) {
    stages[PR]->Bind(13, "threadIdx.z");
  } else {
    stages[PR]->Split(13,
                      GetMaxSplitter(stages[PR]->GetDimRange(13), thread_z));
    stages[PR]->Bind(14, "threadIdx.z");
    stages[PR]->Unroll(13);
  }
  if (stages[KR]->GetDimRange(13) <= thread_x) {
    stages[KR]->Bind(13, "threadIdx.x");
  } else {
    stages[KR]->Split(13,
                      GetMaxSplitter(stages[KR]->GetDimRange(13), thread_x));
    stages[KR]->Bind(14, "threadIdx.x");
    stages[KR]->Unroll(13);
  }
  stages[output]->Unroll(4);
  stages[output]->Unroll(5);
  stages[output]->Unroll(6);

  stages[OL]->Unroll(4);
  stages[OL]->Unroll(5);
  stages[OL]->Unroll(6);
  stages[OL]->Unroll(10);
  stages[OL]->Unroll(11);
  stages[OL]->Unroll(12);
  stages[OL]->Unroll(13);
  stages[OL]->Unroll(14);
  stages[OL]->Unroll(15);
  stages[OL]->Unroll(16);
  stages[OL]->Unroll(17);

  stages[PR]->Unroll(4);
  stages[PR]->Unroll(5);
  stages[PR]->Unroll(6);
  stages[PR]->Unroll(10);
  stages[PR]->Unroll(11);
  stages[PR]->Unroll(12);

  stages[KR]->Unroll(4);
  stages[KR]->Unroll(5);
  stages[KR]->Unroll(6);
  stages[KR]->Unroll(10);
  stages[KR]->Unroll(11);
  stages[KR]->Unroll(12);
}

void CudaScheduleWinogradConv(poly::StageMap wino_stages,
                              std::vector<ir::Tensor> &all_tensors,
                              const common::Target &target) {
  auto &res = ScheduleParam::get_cuda_instance().GetParam();
  auto &wino_weights_dilation = all_tensors[0];
  auto &wino_input_pad = all_tensors[1];
  auto &wino_A = all_tensors[2];
  auto &wino_B = all_tensors[3];
  auto &wino_G = all_tensors[4];
  auto &kernel_pack = all_tensors[5];
  auto &input_tile = all_tensors[6];
  auto &data_pack = all_tensors[7];
  auto &bgemm = all_tensors[8];
  auto &inverse = all_tensors[9];
  auto &wino_conv = all_tensors[10];
  std::string key =
      "CudaWinogradConvSchedule " +
      std::to_string(wino_input_pad->shape[0].as_int32()) + " " +
      std::to_string(wino_input_pad->shape[1].as_int32()) + " " +
      std::to_string(wino_input_pad->shape[2].as_int32()) + " " +
      std::to_string(wino_input_pad->shape[3].as_int32()) + " " +
      std::to_string(wino_weights_dilation->shape[0].as_int32()) + " " +
      std::to_string(wino_weights_dilation->shape[1].as_int32()) + " " +
      std::to_string(wino_weights_dilation->shape[2].as_int32()) + " " +
      std::to_string(wino_weights_dilation->shape[3].as_int32()) + " " +
      std::to_string(wino_conv->shape[0].as_int32()) + " " +
      std::to_string(wino_conv->shape[1].as_int32()) + " " +
      std::to_string(wino_conv->shape[2].as_int32()) + " " +
      std::to_string(wino_conv->shape[3].as_int32());
  VLOG(1) << "Key in CudaScheduleWinogradConv is : " << key;
  CHECK_GT(res.count(key), 0);
  auto &rc_param = res[key]["rc"];
  auto &x_param = res[key]["x"];
  auto &y_param = res[key]["y"];
  auto &b_param = res[key]["b"];

  wino_stages[wino_B]->ComputeInline();

  auto data_l =
      wino_stages[data_pack]->CacheWrite("local", wino_stages, data_pack);

  wino_stages[data_pack]->Split(3, 1);
  wino_stages[data_pack]->Fuse({2, 3});
  wino_stages[data_pack]->Split(2, 128);
  wino_stages[data_pack]->Reorder({2, 3, 4, 0, 1});
  wino_stages[data_pack]->Bind(0, "blockIdx.x");
  wino_stages[data_pack]->Bind(1, "threadIdx.x");
  wino_stages[input_tile]->SetBuffer("local");
  wino_stages[data_l]->ComputeAt(wino_stages[data_pack], 2);
  wino_stages[input_tile]->ComputeAt(wino_stages[data_l], 2);
  wino_stages[wino_input_pad]->ComputeInline();

  wino_stages[wino_G]->ComputeInline();

  wino_stages[kernel_pack]->Fuse({2, 3});
  wino_stages[kernel_pack]->Split(2, 128);
  wino_stages[kernel_pack]->Reorder({2, 3, 0, 1});

  wino_stages[kernel_pack]->Bind(0, "blockIdx.x");
  wino_stages[kernel_pack]->Bind(1, "threadIdx.x");

  wino_stages[wino_weights_dilation]->ComputeInline();

  auto wino_OL = wino_stages[bgemm]->CacheWrite("local", wino_stages, bgemm);
  std::vector<ir::Tensor> readers{wino_OL};
  auto AA = wino_stages[kernel_pack]->CacheRead("shared", readers, wino_stages);
  auto BB = wino_stages[data_pack]->CacheRead("shared", readers, wino_stages);

  wino_stages[bgemm]->Fuse({0, 1});

  wino_stages[bgemm]->SplitOuter(0, 1);

  // x param is :  [1, 1, 8, 2]
  wino_stages[bgemm]->Split(3, x_param[3]);
  wino_stages[bgemm]->Split(3, x_param[2]);
  wino_stages[bgemm]->Split(3, x_param[1]);
  // y param is :  [8, 1, 16, 4]
  wino_stages[bgemm]->Split(2, y_param[3]);
  wino_stages[bgemm]->Split(2, y_param[2]);
  wino_stages[bgemm]->Split(2, y_param[1]);
  // b param is :  [16, 1, 1, 1]
  wino_stages[bgemm]->Split(1, b_param[3]);
  wino_stages[bgemm]->Split(1, b_param[2]);
  wino_stages[bgemm]->Split(1, b_param[1]);

  wino_stages[bgemm]->Reorder({0, 1, 5, 9, 2, 6, 10, 3, 7, 11, 4, 8, 12});

  wino_stages[bgemm]->Bind(1, "blockIdx.z");
  wino_stages[bgemm]->Bind(2, "blockIdx.y");
  wino_stages[bgemm]->Bind(3, "blockIdx.x");
  wino_stages[bgemm]->Bind(7, "threadIdx.z");
  wino_stages[bgemm]->Bind(8, "threadIdx.y");
  wino_stages[bgemm]->Bind(9, "threadIdx.x");

  wino_stages[wino_OL]->ComputeAt(wino_stages[bgemm], 9);

  // rc param is :  [32, 16]
  wino_stages[wino_OL]->Split(12, rc_param[1]);

  wino_stages[wino_OL]->Reorder({12, 13, 10, 11});

  auto bgemm_init = wino_OL->GetInitTensor(wino_stages, target);
  wino_stages[AA]->ComputeAt(wino_stages[wino_OL], 10);
  wino_stages[BB]->ComputeAt(wino_stages[wino_OL], 10);

  wino_stages[AA]->Fuse(11, 12);
  wino_stages[AA]->SplitOuter(11, x_param[2]);
  wino_stages[AA]->Bind(11, "threadIdx.x");

  wino_stages[BB]->Fuse(11, 12);
  wino_stages[BB]->SplitOuter(11, y_param[2]);
  wino_stages[BB]->Bind(11, "threadIdx.y");

  wino_stages[AA]->SyncThreads(10, {bgemm_init}, wino_stages);
  wino_stages[BB]->SyncThreads(wino_stages);

  int m = 2;

  wino_stages[wino_conv]->Tile(2, 3, m, m);
  wino_stages[wino_conv]->Reorder({2, 4, 3, 5});
  wino_stages[wino_conv]->FuseDirect({0, 1, 2, 3});
  wino_stages[wino_conv]->Split(0, 128);
  wino_stages[wino_conv]->Bind(0, "blockIdx.x");
  wino_stages[wino_conv]->Bind(1, "threadIdx.x");
  wino_stages[wino_A]->ComputeInline();

  wino_stages[inverse]->SetBuffer("local");
  wino_stages[inverse]->Fuse(0, 1);
  wino_stages[inverse]->Split(0, 128);
  wino_stages[inverse]->Bind(0, "blockIdx.x");
  wino_stages[inverse]->Bind(1, "threadIdx.x");
  wino_stages[inverse]->SimpleComputeAt(wino_stages[wino_conv], 1);
}

int gcd(int a, int b) {
  int r;
  while (b > 0) {
    r = a % b;
    a = b;
    b = r;
  }
  return a;
}

int MaxFactorLessThan(int a, int b) {
  CHECK_GT(a, b);
  int res = 1;
  for (int i = 2; i <= (int)sqrt((double)a); i++) {
    if (a % i == 0) {
      if (i <= b) res = std::max(res, i);
      if (a / i <= b) res = std::max(res, a / i);
    }
  }
  return res;
}

void CudaScheduleInjectiveWithVectorize(poly::Stage *stage,
                                        const std::vector<int> &output_shape,
                                        const common::Target &target) {
  int dims = stage->n_out_dims();
  int prod_size = std::accumulate(
      output_shape.begin(), output_shape.end(), 1, std::multiplies<int>());
  int num_thread = target.max_num_threads();
  int last_shape = stage->GetDimRange(stage->n_out_dims() - 1);
  // determine the factor of vectorize
  int vector_width = 1;
  if (last_shape % 4 == 0) {
    vector_width = 4;
  } else if (last_shape % 2 == 0) {
    vector_width = 2;
  }

  // print range of stage for debug
  auto range_str_fn = [stage]() {
    std::vector<int> dim_ranges;
    for (int i = 0; i < stage->n_out_dims(); ++i) {
      dim_ranges.push_back(stage->GetDimRange(i));
    }
    std::string res = "[" + utils::Join(dim_ranges, ",") + "]";
    return res;
  };

  // the first bind position from tail
  int bind_idx = stage->n_out_dims() - 1;
  // it will add a new dim by split before vectorize, but the new dim will
  // be eleminated when vectorizng, so the bind_idx does't need to increase
  if (vector_width > 1) {
    stage->Split(bind_idx, vector_width);
  }
  VLOG(5) << "vectorize result:" << range_str_fn();

  // revise dim for binding threadIdx.x, here only use the x of threadIdx
  if (stage->GetDimRange(bind_idx) > num_thread) {
    stage->Split(bind_idx, gcd(stage->GetDimRange(bind_idx), num_thread));
    ++bind_idx;
  }
  while (bind_idx > 0 &&
         stage->GetDimRange(bind_idx - 1) * stage->GetDimRange(bind_idx) <
             num_thread) {
    stage->Fuse(bind_idx - 1, bind_idx);
    --bind_idx;
  }
  // call vectorize on the last dim
  if (vector_width > 1) {
    stage->Vectorize(stage->n_out_dims() - 1, vector_width);
  }
  stage->Bind(bind_idx, "threadIdx.x");
  --bind_idx;
  VLOG(5) << "bind threadIdx.x result:" << range_str_fn();

  // revise dim for binding blockIdx, at most 3 indexes can be used
  while (bind_idx > 2) {
    stage->Fuse(bind_idx - 1, bind_idx);
    --bind_idx;
  }
  std::string block_idx = "blockIdx.x";
  for (int j = 0; bind_idx >= 0; ++j) {
    block_idx.back() = 'x' + j;
    stage->Bind(bind_idx, block_idx);
    --bind_idx;
  }
  VLOG(5) << "CudaScheduleInjectiveWithVectorize tensor:"
          << stage->tensor()->name << ", vector_width:" << vector_width
          << ", prod_size:" << prod_size << ", shape:["
          << utils::Join(output_shape, ",") << "]"
          << ", range:" << range_str_fn();
}

void CudaScheduleInjective(poly::Stage *stage,
                           const std::vector<int> &output_shape,
                           const common::Target &target) {
  CHECK_EQ(stage->n_out_dims(), stage->n_in_dims())
      << "The dims of op are not equal";
  if (FLAGS_cinn_use_cuda_vectorize) {
    CudaScheduleInjectiveWithVectorize(stage, output_shape, target);
    return;
  }
  int dims = stage->n_out_dims();
  for (int i = 1; i < dims; i++) {
    stage->Fuse(0, 1);
  }

  int num_thread = target.max_num_threads();
  int prod_size = std::accumulate(
      output_shape.begin(), output_shape.end(), 1, std::multiplies<int>());
  if (prod_size <= num_thread) {
    stage->Bind(0, "threadIdx.x");
    return;
  }
  int new_num_thread = gcd(prod_size, num_thread);
  if (new_num_thread % 32 != 0) {
    new_num_thread = MaxFactorLessThan(prod_size, num_thread);
  }
  if (new_num_thread == 1)
    LOG(FATAL) << "prod_size out of range: " << prod_size;

  CHECK_GT(prod_size, new_num_thread);
  stage->Split(0, new_num_thread);
  stage->Bind(0, "blockIdx.x");
  stage->Bind(1, "threadIdx.x");
}

void CudaSplitSchedule(common::CINNValuePack *arg_pack,
                       const std::vector<std::vector<int>> &output_shapes,
                       int axis,
                       const common::Target &target) {
  poly::StageMap stages = arg_pack->back();
  std::vector<ir::Tensor> out_tensors;
  int dims = output_shapes[0].size();
  for (int i = 0; i < arg_pack->size() - 1; ++i) {
    Expr Out = (*arg_pack)[i];
    CHECK(Out.as_tensor());
    out_tensors.push_back(Out.as_tensor_ref());
  }
  std::vector<int> reorders;
  for (int i = 0; i < dims; i++) {
    reorders.push_back(i);
  }
  reorders.erase(reorders.begin() + axis);
  reorders.push_back(axis);
  for (auto &out : out_tensors) {
    stages[out]->Reorder(reorders);
  }
  auto last_output = out_tensors.back();

  std::vector<int> fuse_index;
  for (int i = 0; i < dims - 1; i++) fuse_index.push_back(i);
  for (auto &out : out_tensors) stages[out]->Fuse(fuse_index);
  int fused_shape = 1;
  for (int i = 0; i < dims; i++) {
    if (i != axis) fused_shape = fused_shape * output_shapes[0][i];
  }
  int compute_at_level = 0;
  if (target.arch == Target::Arch::NVGPU) {
    if (fused_shape > target.max_num_threads()) {
      stages[last_output]->Split(0, target.max_num_threads());
      stages[last_output]->Bind(0, "blockIdx.x");
      stages[last_output]->Bind(1, "threadIdx.x");
      compute_at_level++;
    } else {
      stages[last_output]->Bind(0, "threadIdx.x");
    }
  }

  for (int i = 0; i < out_tensors.size() - 1; i++) {
    stages[out_tensors[i]]->ComputeAt2(stages[last_output], compute_at_level);
  }
}

}  // namespace pe
}  // namespace hlir
}  // namespace cinn