/* Copyright 2016 The TensorFlow 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.
==============================================================================*/

#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_HLO_MEMORY_SCHEDULER_H_
#define TENSORFLOW_COMPILER_XLA_SERVICE_HLO_MEMORY_SCHEDULER_H_

#include <vector>

#include "absl/container/flat_hash_map.h"
#include "tensorflow/compiler/xla/service/hlo_instruction.h"
#include "tensorflow/compiler/xla/service/hlo_module.h"
#include "tensorflow/compiler/xla/service/hlo_ordering.h"
#include "tensorflow/compiler/xla/service/hlo_pass_interface.h"
#include "tensorflow/compiler/xla/service/hlo_schedule.h"
#include "tensorflow/compiler/xla/service/logical_buffer.h"
#include "tensorflow/compiler/xla/service/tuple_points_to_analysis.h"
#include "tensorflow/compiler/xla/statusor.h"
#include "tensorflow/compiler/xla/types.h"

namespace xla {

// A memory scheduler computes an execution sequence for the HLO instructions in
// 'computation' that minimizes peak memory, given a points-to analysis result
// that describes buffer aliasing, together with a target-specific size function
// that maps a tensor's logical size to its padded size.
typedef std::function<StatusOr<HloInstructionSequence>(
    const HloComputation&, const TuplePointsToAnalysis&,
    const LogicalBuffer::SizeFunction&,
    const absl::flat_hash_map<const HloComputation*, int64>&)>
    MemorySchedulerAlgorithm;

// List scheduler
StatusOr<HloInstructionSequence> ListMemoryScheduler(
    const HloComputation& computation,
    const TuplePointsToAnalysis& points_to_analysis,
    const LogicalBuffer::SizeFunction& size_function,
    const absl::flat_hash_map<const HloComputation*, int64>&
        memory_by_computation);

// DFS-order scheduler
StatusOr<HloInstructionSequence> DFSMemoryScheduler(
    const HloComputation& computation,
    const TuplePointsToAnalysis& points_to_analysis,
    const LogicalBuffer::SizeFunction& size_function,
    const absl::flat_hash_map<const HloComputation*, int64>&
        memory_by_computation);

// Naive Post Order scheduler
StatusOr<HloInstructionSequence> PostOrderMemoryScheduler(
    const HloComputation& computation,
    const TuplePointsToAnalysis& points_to_analysis,
    const LogicalBuffer::SizeFunction& size_function,
    const absl::flat_hash_map<const HloComputation*, int64>&
        memory_by_computation);

// The default scheduling algorithm. Runs both the list scheduler
// and the DFS scheduler, and chooses whichever returns a lower min-memory,
// not accounting for fragmentation.
StatusOr<HloInstructionSequence> DefaultMemoryScheduler(
    const HloComputation& computation,
    const TuplePointsToAnalysis& points_to_analysis,
    const LogicalBuffer::SizeFunction& size_function,
    const absl::flat_hash_map<const HloComputation*, int64>&
        memory_by_computation);

// Returns an HloSchedule which seeks to minimize the memory required for
// the computation. size_function is the function returning the number of bytes
// required for a LogicalBuffer.
StatusOr<HloSchedule> ScheduleModule(
    const HloModule& module, const LogicalBuffer::SizeFunction& size_function,
    const MemorySchedulerAlgorithm& algorithm = {});

// Computes the schedule for a single computation.
// Currently only used by the GPU backend.
StatusOr<HloInstructionSequence> ScheduleComputation(
    const HloComputation& computation,
    const LogicalBuffer::SizeFunction& size_function);

// A pass which schedules the HLO instructions in a module. The HloModule's
// schedule field is set to the resulting HloSchedule using
// HloModule::set_schedule.
class HloMemoryScheduler : public HloModulePass {
 public:
  // size_function is the function returning the number of bytes required for a
  // LogicalBuffer. algorithm is the memory scheduling algorithm to use. If not
  // specified, then DefaultMemoryScheduler is used.
  HloMemoryScheduler(const LogicalBuffer::SizeFunction& size_function,
                     const MemorySchedulerAlgorithm& algorithm = {});
  ~HloMemoryScheduler() override = default;
  absl::string_view name() const override { return "hlo-memory-scheduler"; }

  StatusOr<bool> Run(HloModule* module) override;

 private:
  LogicalBuffer::SizeFunction size_function_;
  MemorySchedulerAlgorithm algorithm_;
};

// A trivial pass which clears the schedule currently set on the
// HloModule. After this pass runs HloModudle::has_schedule will return false.
class HloDescheduler : public HloModulePass {
 public:
  HloDescheduler() = default;
  ~HloDescheduler() override = default;
  absl::string_view name() const override { return "hlo-descheduler"; }

  StatusOr<bool> Run(HloModule* module) override;
};

}  // namespace xla

#endif  // TENSORFLOW_COMPILER_XLA_SERVICE_HLO_MEMORY_SCHEDULER_H_