// Copyright (c) 2022 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/auto_schedule/task/task_optimizer.h"

#include <glog/logging.h>

#include <functional>
#include <limits>

#include "paddle/cinn/auto_schedule/analysis/analyze_ir.h"
#include "paddle/cinn/auto_schedule/cost_model/expr_cost_model.h"
#include "paddle/cinn/auto_schedule/measure/measure.h"
#include "paddle/cinn/auto_schedule/search_strategy/evolutionary_search.h"
#include "paddle/cinn/common/target.h"
#include "paddle/cinn/hlir/framework/op_lowering.h"
#include "paddle/cinn/hlir/op/external_api_registry.h"
#include "paddle/cinn/ir/buffer.h"
#include "paddle/cinn/ir/ir.h"
#include "paddle/cinn/ir/ir_base.h"
#include "paddle/cinn/ir/schedule/ir_schedule.h"
#include "paddle/cinn/ir/utils/ir_copy.h"
#include "paddle/cinn/optim/transform_gpu_forloop.h"
#include "paddle/cinn/runtime/flags.h"
#include "paddle/cinn/utils/string.h"
#ifdef CINN_WITH_CUDA
#include <cuda_runtime_api.h>

#include "paddle/cinn/backends/cuda_util.h"
#endif

DECLARE_bool(auto_schedule_use_cost_model);

namespace cinn {
namespace auto_schedule {

using cinn::hlir::op::ExternalApiRegistry;

// *** forward declarations of auxiliary functions to be used in this file only
// *** update a scheduled function with several post-processors
ir::LoweredFunc FuncWithUpdatedBody(const common::Target& target,
                                    const ir::LoweredFunc& old_func,
                                    ir::Expr& body);  // NOLINT
// check whether a scheduled lowered function is valid
bool PruneInvalid(const ir::LoweredFunc& lowered_func,
                  const common::Target& target);
// exclude some special tasks
bool IsForbiddenToTune(const TuneTask* task);
// tell whether the task has been wrapped by custom_call in
// TransToCustomCallPass
bool IsWrappedByCustomCall(const TuneTask* task);
// tell whether the task has registered external api
bool HasExternalApi(const TuneTask* task);

TaskOptimizer::TaskOptimizer(TuneTask* task,
                             ScheduleMeasurer* schedule_measurer,
                             Database* database,
                             utils::LinearRandomEngine::StateType rand_seed)
    : task_(task),
      schedule_measurer_(schedule_measurer),
      database_(database),
      cost_model_(),
      rand_seed_(utils::LinearRandomEngine::NormalizeState(rand_seed)) {}

FunctionGroup TaskOptimizer::Optimize(const TuningOptions& options) {
  CHECK(task_->subgraph != nullptr) << "subgraph can't be empty";
  // task with forbidden or custom_call ops can't be tuned
  if (IsForbiddenToTune(task_) || IsWrappedByCustomCall(task_)) {
    return task_->op_lowerer->Lower(task_->subgraph);
  }
  // TODO(CtfGo): the input/output names of a Graph::Group will be changed in
  // Lowering by OpLowerer currently, so we should revert them after following
  // different lower methods, remove this hard code by fixing the decoupling
  // between lowering and BuildInstructions
  auto initial_input_names = task_->subgraph->input_names;
  auto initial_output_names = task_->subgraph->output_names;

  std::vector<TaskOptimizer::Result> candidates;
  candidates.emplace_back(OptimizeByEvolution(options));
  candidates.emplace_back(OptimizeByManual(options.num_measure_trials > 0));
  if (HasExternalApi(task_)) {
    candidates.emplace_back(OptimizeByExternal(options.num_measure_trials > 0));
  }
  sort(candidates.begin(),
       candidates.end(),
       [](const auto& lhs, const auto& rhs) { return lhs.cost < rhs.cost; });
  auto&& best = candidates.front();
  VLOG(4) << "Total candidates=" << candidates.size()
          << ", the best from=" << best.from << ", cost=" << best.cost;

  // revert input/output names
  task_->subgraph->input_names = initial_input_names;
  task_->subgraph->output_names = initial_output_names;
  return best.functions;
}

TaskOptimizer::Result TaskOptimizer::OptimizeByManual(bool need_measured) {
  static constexpr char* kManualMeasuredKeyPrefix = "@ManualMeasured:\n";
  TaskOptimizer::Result result("Manual");
  result.functions = task_->op_lowerer->Lower(task_->subgraph);

  // pack functions body
  std::vector<ir::Expr> func_bodys;
  for (const ir::LoweredFunc& func : result.functions) {
    func_bodys.push_back(func->body);
  }

  SearchState state(ir::IRSchedule(ir::ModuleExpr(std::move(func_bodys))));
  // the manual is regarded as the second best in default, so we set its cost
  // 0.0
  result.cost = 0.0;

  // add the specific prefix in front of serialized_key to be store/load
  // measured record for manual schedule
  std::string measured_key = kManualMeasuredKeyPrefix + task_->serialized_key;
  if (need_measured && database_->Count(measured_key) == 0) {
    std::vector<MeasureInput> inputs(1);
    inputs.back().task = task_;
    inputs.back().lowered_funcs = result.functions;
    VLOG(4) << "Measure manual schedule";
    std::vector<MeasureResult> measure_outputs =
        schedule_measurer_->Measure(inputs);
    database_->AddRecord(
        TuningRecord(measured_key, state, measure_outputs[0].execution_cost));
  }

  auto measured_records = database_->LookUp(measured_key);
  if (!measured_records.empty()) {  // update result.cost by measured if exists
    result.cost = measured_records[0].execution_cost;
  }
  return result;
}

TaskOptimizer::Result TaskOptimizer::OptimizeByExternal(bool need_measured) {
  static constexpr char* kExternalMeasuredKeyPrefix = "@ExternalMeasured:\n";
  TaskOptimizer::Result result("External");
  auto nodes = task_->subgraph->CollectNodes();
  auto* first_node = nodes.front();

  // set the necessary field for lowering with external api
  std::string original_op = first_node->op()->name;
  first_node->attrs.attr_store["original_op"] = original_op;
  first_node->attrs.op = hlir::framework::Operator::Get("custom_call");
  result.functions = task_->op_lowerer->Lower(task_->subgraph);

  // add the specific prefix in front of serialized_key to be store/load
  // measured record for external api
  result.cost = -1.0;  // the external is regarded as the best in default, so we
                       // set its cost -1.0
  std::string measured_key = kExternalMeasuredKeyPrefix + task_->serialized_key;
  if (need_measured && database_->Count(measured_key) == 0) {
    std::vector<MeasureInput> inputs(1);
    inputs.back().task = task_;
    inputs.back().lowered_funcs = result.functions;
    VLOG(4) << "Measure external api";
    std::vector<MeasureResult> measure_outputs =
        schedule_measurer_->Measure(inputs);
    // the SearchState of external is invalid and will not be used, so we just
    // put a temporary one
    database_->AddRecord(TuningRecord(measured_key,
                                      SearchState(ir::IRSchedule()),
                                      measure_outputs[0].execution_cost));
  }

  auto measured_records = database_->LookUp(measured_key);
  if (!measured_records.empty()) {  // update result.cost by measured if exists
    result.cost = measured_records[0].execution_cost;
  }
  return result;
}

bool IsForbiddenToTune(const TuneTask* task) {
  // TODO(CtfGo): some operators may change its linked edges in
  // TransToCustomCallPass, like conv2d, we will skip these ops in auto-schedule
  // because they can't revert original links for no schedule and manual
  // schedule lowering.
  static std::unordered_set<std::string> links_changed_ops = {"conv2d"};
  auto nodes = task->subgraph->CollectNodes();
  auto&& op_name = nodes.front()->op()->name;
  if (nodes.size() == 1 && links_changed_ops.count(op_name)) {
    VLOG(5) << "Op:" << op_name << " is forbidden to call external_api";
    return true;
  }

  return false;
}

bool HasExternalApi(const TuneTask* task) {
  auto nodes = task->subgraph->CollectNodes();
  auto* first_node = nodes.front();
  if (nodes.size() == 1 && ExternalApiRegistry::Global()->Has(
                               first_node->op()->name, task->target)) {
    return true;
  }
  return false;
}

bool IsWrappedByCustomCall(const TuneTask* task) {
  auto nodes = task->subgraph->CollectNodes();
  auto* first_node = nodes.front();
  if (nodes.size() == 1 && first_node->op()->name == "custom_call") {
    CHECK(first_node->attrs.attr_store.count("original_op"))
        << "a custom_call op must store its original op name";
    std::string op_name =
        absl::get<std::string>(first_node->attrs.attr_store.at("original_op"));
    VLOG(5) << "Op:" << op_name << " was wrapped as custom_call";
    return true;
  }

  return false;
}

TaskOptimizer::Result TaskOptimizer::OptimizeByEvolution(
    const TuningOptions& options) {
  CHECK_EQ(options.num_measure_trials % options.num_samples_per_iteration, 0)
      << "TuningOptions.num_measure_trials % "
         "TuningOptions.num_samples_per_iteration must be 0.";

  VLOG(4) << "Optimizing TuneTask with num_measure_trials:"
          << options.num_measure_trials
          << ", LoweredFunc before optimization is:";
  VLOG(4) << "lowered function size = " << task_->lowered_funcs.size();
  for (size_t i = 0; i < task_->lowered_funcs.size(); ++i) {
    VLOG(4) << "lowered_funcs[" << i << "] detail:\n"
            << task_->lowered_funcs[i];
  }

  if (evolutionary_search_ == nullptr) {
    // TODO(zhhsplendid): check whether the options is same as previous,
    // if not, we should create new EvolutionarySearch
    evolutionary_search_ = std::make_unique<EvolutionarySearch>(
        *task_, cost_model_, database_, utils::ForkRandomState(&rand_seed_));
  }

  TaskOptimizer::Result result("Evolution");
  auto& optimized_funcs = result.functions;
  auto& best_cost = result.cost;
  // use initial lowered function as default result
  optimized_funcs = optim::IRCopy(task_->lowered_funcs);
  if (options.num_measure_trials ==
      0) {  // no need to measure and simply return the best searched
    std::vector<MeasureInput> measure_candidates;
    std::vector<SearchState> states =
        SearchOneRound(options, &measure_candidates);
    if (!states.empty()) {
      if (FLAGS_auto_schedule_use_cost_model) {
        best_cost = cost_model_.Predict(states.front()->ir_schedule.GetModule(),
                                        task_->target);
      }
      optimized_funcs = measure_candidates[0].lowered_funcs;
    } else {
      LOG(WARNING) << "No valid candidate searched, will return initial state";
    }
    return result;
  }

  int measured_count = 0;
  uint32_t continuous_empty_cnt = 0;
  while (measured_count < options.num_measure_trials) {
    VLOG(4) << "Launch a new search, current measured_count:" << measured_count;
    std::vector<MeasureInput> measure_inputs;
    std::vector<SearchState> states = SearchOneRound(options, &measure_inputs);
    if (states.empty()) {  // no new valid candidate achieved
      ++continuous_empty_cnt;
      if (continuous_empty_cnt <= kMaxRetryContinuousEmpty_) {
        VLOG(4) << "No valid state searched, continuous_empty_cnt="
                << continuous_empty_cnt;
        continue;
      } else {
        LOG(WARNING) << "OptimizeByEvolution will be exited in advance due to "
                        "continuous invalid search, final measured_count="
                     << measured_count;
        break;
      }
    }
    continuous_empty_cnt = 0;  // reset if get valid candidates

    VLOG(4) << "ScheduleMeasurer start with input size="
            << measure_inputs.size();
    std::vector<MeasureResult> measure_outputs =
        schedule_measurer_->Measure(measure_inputs);
    CHECK_EQ(measure_outputs.size(), states.size())
        << "ScheduleMeasurer didn't output same number of MeasureOutput of "
           "states in TaskOptimizer";
    // record to database
    for (size_t i = 0; i < states.size(); ++i) {
      database_->AddRecord(TuningRecord(measure_inputs[i].task->serialized_key,
                                        states[i],
                                        measure_outputs[i].execution_cost));
    }

    // update cost model
    if (FLAGS_auto_schedule_use_cost_model) {
      std::vector<const ir::ModuleExpr*> cost_model_samples(states.size());
      std::vector<float> cost_model_labels(states.size());
      for (size_t i = 0; i < states.size(); ++i) {
        cost_model_samples[i] = &(states[i]->ir_schedule.GetModule());
        cost_model_labels[i] = measure_outputs[i].execution_cost;
      }
      VLOG(4) << utils::StringFormat(
          "Update CostModel with samples size=%lu,labels size=%lu",
          cost_model_samples.size(),
          cost_model_labels.size());
      cost_model_.Update(cost_model_samples, cost_model_labels, task_->target);
    }

    // update the best
    for (size_t i = 0; i < measure_outputs.size(); ++i) {
      if (measure_outputs[i].execution_cost < best_cost) {
        VLOG(4) << "Update best candidate with execution_cost:"
                << measure_outputs[i].execution_cost << "us";
        best_cost = measure_outputs[i].execution_cost;
        optimized_funcs = measure_inputs[i].lowered_funcs;
      }
    }

    // count result size
    measured_count += states.size();
  }
  return result;
}

std::vector<SearchState> TaskOptimizer::SearchOneRound(
    const TuningOptions& options,
    std::vector<MeasureInput>* measure_candidates) {
  std::vector<SearchState> states =
      evolutionary_search_->SearchModuleExprEpsGreedy(options);
  VLOG(4) << JoinStatesDebugString("TaskOptimizer::EvolutionarySearch-Result",
                                   states,
                                   /*verbose=*/VLOG_IS_ON(5));

  size_t valid_cnt = 0;
  for (size_t i = 0; i < states.size(); ++i) {
    std::vector<ir::Expr> best_exprs =
        states[i]->ir_schedule.GetModule().GetExprs();
    CHECK_EQ(best_exprs.size(), task_->lowered_funcs.size())
        << "RuntimeError: Expr size is not equal to LoweredFunc size in "
           "TaskOptimizer";
    auto init_funcs = optim::IRCopy(task_->lowered_funcs);
    std::vector<ir::LoweredFunc> valid_funcs;
    for (size_t j = 0; j < best_exprs.size(); ++j) {
      auto updated_f =
          UpdateFuncWithNewBody(task_->target, init_funcs[j], best_exprs[j]);
      if (PruneInvalid(updated_f, task_->target)) {
        VLOG(4) << "PruneInvalid states-" << i;
        break;
      }
      valid_funcs.emplace_back(updated_f);
    }

    // all functions are validated, collect this state to be measured
    if (valid_funcs.size() == init_funcs.size()) {
      states[valid_cnt++] = states[i];
      measure_candidates->emplace_back(MeasureInput());
      measure_candidates->back().task = task_;
      measure_candidates->back().lowered_funcs = std::move(valid_funcs);
    }
  }

  states.erase(states.begin() + valid_cnt, states.end());
  CHECK_EQ(states.size(), measure_candidates->size())
      << "result size of states not equal to measure_candidates";
  VLOG(4) << "EvolutionarySearch return size=" << states.size()
          << ", valid count=" << valid_cnt;
  VLOG(4) << JoinStatesDebugString("TaskOptimizer::SearchOneRound-Result",
                                   states,
                                   /*verbose=*/VLOG_IS_ON(5));
  return states;
}

// detect the limit of available shared memory on the current NVGPU with CUDA
// runtime
size_t GetGPUSharedMemoryLimit() {
#ifdef CINN_WITH_CUDA
  int device_id;
  CUDA_CALL(cudaGetDevice(&device_id));
  cudaDeviceProp prop;
  CUDA_CALL(cudaGetDeviceProperties(&prop, device_id));
  VLOG(4) << utils::StringFormat(
      "GPU-%d GPUSharedMemoryLimit=%d", device_id, prop.sharedMemPerBlock);
  return prop.sharedMemPerBlock;
#else
  return 0;
#endif
}

// detect the limit of available local/stack memory on the current NVGPU with
// CUDA runtime
size_t GetGPULocalStackLimit() {
#ifdef CINN_WITH_CUDA
  int device_id;
  CUDA_CALL(cudaGetDevice(&device_id));
  cudaDeviceProp prop;
  CUDA_CALL(cudaGetDeviceProperties(&prop, device_id));
  size_t limit = prop.totalGlobalMem / prop.multiProcessorCount /
                 prop.maxThreadsPerMultiProcessor;
  VLOG(4) << utils::StringFormat(
      "GPU-%d "
      "totalGlobalMem=%lu,maxThreadsPerMultiProcessor=%d,multiProcessorCount=%"
      "d, calculated "
      "GPULocalStackLimit=%lu",
      device_id,
      prop.totalGlobalMem,
      prop.multiProcessorCount,
      prop.maxThreadsPerMultiProcessor,
      limit);
  return limit;
#else
  return 0;
#endif
}

// check whether usage of the specific memory type in the lowered_func exceeds
// hardware limit
bool IsGPUMemoryUsageExceedLimit(const ir::LoweredFunc& lowered_func,
                                 const ir::MemoryType& used_memory_type,
                                 const size_t limit_bytes) {
  std::unordered_set<std::string> visited;
  size_t used_bytes_cnt = 0;
  for (auto&& buf : lowered_func->temp_bufs) {
    VLOG(5) << "temp buf name=" << buf->name << ", numel=" << buf->numel()
            << ",dtype=" << buf->dtype;
    if (buf->memory_type == used_memory_type && !visited.count(buf->name)) {
      used_bytes_cnt += buf->numel() * buf->dtype.bytes();
      visited.insert(buf->name);
    }
  }
  VLOG(5) << "total used_bytes_cnt=" << used_bytes_cnt;
  return used_bytes_cnt >= limit_bytes;
}

bool PruneInvalid(const ir::LoweredFunc& lowered_func,
                  const common::Target& target) {
  static const size_t kGPUSharedMemoryLimitBytes = GetGPUSharedMemoryLimit();
  static const size_t kGPULocalStackLimitBytes = GetGPULocalStackLimit();

  if (target == common::DefaultNVGPUTarget()) {
    if (IsGPUMemoryUsageExceedLimit(lowered_func,
                                    ir::MemoryType::GPUShared,
                                    kGPUSharedMemoryLimitBytes)) {
      VLOG(5) << ir::MemoryType::GPUShared
              << " memory usage exceeds limit, func:\n"
              << lowered_func;
      return true;
    }

    if (IsGPUMemoryUsageExceedLimit(
            lowered_func, ir::MemoryType::GPULocal, kGPULocalStackLimitBytes)) {
      VLOG(5) << ir::MemoryType::GPULocal
              << " memory usage exceeds limit, func:\n"
              << lowered_func;
      return true;
    }
  }
  return false;
}

}  // namespace auto_schedule
}  // namespace cinn
