/* 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.
==============================================================================*/

#include "tensorflow/compiler/xla/service/gpu/fusion_merger.h"

#include <algorithm>
#include <vector>

#include "tensorflow/compiler/xla/service/gpu/instruction_fusion.h"
#include "tensorflow/compiler/xla/service/hlo_cost_analysis.h"
#include "tensorflow/compiler/xla/shape_util.h"
#include "tensorflow/compiler/xla/util.h"
#include "tensorflow/core/lib/core/errors.h"
#include "tensorflow/core/lib/strings/str_util.h"

namespace xla {
namespace gpu {

namespace {

// Traverses users of tuple shape, adding leaf instructions to 'instructions'.
void MaybeResolveTupleElements(HloInstruction* instruction,
                               std::vector<HloInstruction*>* instructions) {
  if (ShapeUtil::IsTuple(instruction->shape())) {
    for (auto tuple_user : instruction->users()) {
      MaybeResolveTupleElements(tuple_user, instructions);
    }
  } else {
    instructions->push_back(instruction);
  }
}

// Returns the bytes read by fusion parameter 'param', by returning the byte
// size of 'param' shape (or the cumulative byte sizes of all leaf tuple
// elements if 'param' is tuple-shaped).
//
// In the special case where all users of 'param' (or all users of a leaf
// tuple element if 'param' is tuple-shaped) are Slice instructions, the size
// of each slice instruction is accumulated instead, to give a more accurate
// value for bytes read.
double CalculateBytesReadByFusionParameter(HloInstruction* param) {
  CHECK_EQ(HloOpcode::kParameter, param->opcode());

  // Adds all leaf tuple elements to 'instructions' if 'param' is tuple-shaped.
  // Adds 'param' to 'instructions' otherwise.
  std::vector<HloInstruction*> instructions;
  MaybeResolveTupleElements(param, &instructions);

  // Iterate through 'instructions' accumulating byte sizes of each instruction
  // shape. For each 'instruction' in 'instructions', if all users of
  // 'instruction' are Slice instructions, accumuates the byte sizes of each
  // Slice for a more accurate estimate of bytes read.
  double bytes = 0.0;
  for (auto& instruction : instructions) {
    if (c_all_of(instruction->users(), [](const HloInstruction* instruction) {
          return instruction->opcode() == HloOpcode::kSlice ||
                 instruction->opcode() == HloOpcode::kDynamicSlice;
        })) {
      // All users are slice: accumulate bytes of all user slice instructions.
      for (auto& user : instruction->users()) {
        bytes += ShapeUtil::ByteSizeOf(user->shape());
      }
    } else {
      // Some users are not slice: accumulate full size of 'instruction'.
      bytes += ShapeUtil::ByteSizeOf(instruction->shape());
    }
  }
  return bytes;
}

// Returns the bytes read by all fusion parameters of instruction 'fusion'.
double CalculateBytesReadByFusionInstruction(HloInstruction* fusion) {
  double bytes = 0.0;
  for (auto* fused_instruction : fusion->fused_instructions()) {
    if (fused_instruction->opcode() != HloOpcode::kParameter) {
      continue;
    }
    bytes += CalculateBytesReadByFusionParameter(fused_instruction);
  }
  return bytes;
}

// Returns the flops to bytes transferred ratio of instruction 'fusion'.
double CalculateFlopsToBytesRatio(HloInstruction* fusion) {
  CHECK_EQ(HloOpcode::kFusion, fusion->opcode());
  // Calculate total bytes transferred in/out.
  double bytes = CalculateBytesReadByFusionInstruction(fusion);
  // Add bytes written to root instructions buffer.
  if (fusion->IsMultiOutputFusion()) {
    for (auto& operand : fusion->fused_expression_root()->operands()) {
      bytes += ShapeUtil::ByteSizeOf(operand->shape());
    }
  } else {
    bytes += ShapeUtil::ByteSizeOf(fusion->fused_expression_root()->shape());
  }
  // Calculate flops for all fused instructions. Use a null shape size function
  // because we don't care about bytes accessed by the ops.
  HloCostAnalysis analysis([](const Shape& shape) { return 0; });
  TF_CHECK_OK(fusion->fused_expression_root()->Accept(&analysis));
  // Return flops / bytes.
  return bytes > 0.0 ? analysis.flop_count() / bytes : analysis.flop_count();
}

// Returns bytes transferred by instruction 'fusion', including the bytes
// that would be read by all users.
double GetCurrentBytesTransferred(HloInstruction* fusion) {
  CHECK_EQ(HloOpcode::kFusion, fusion->opcode());
  const double bytes_read = CalculateBytesReadByFusionInstruction(fusion);
  double bytes_written = 0;
  if (fusion->IsMultiOutputFusion()) {
    for (auto& operand : fusion->fused_expression_root()->operands()) {
      bytes_written += ShapeUtil::ByteSizeOf(operand->shape());
    }
  } else {
    bytes_written =
        ShapeUtil::ByteSizeOf(fusion->fused_expression_root()->shape());
  }
  // Current bytes transferred (ignoring non 'fusion' user operands) is bytes
  // read and written by 'fusion', plus reads of size 'bytes_written' for each
  // user.
  return bytes_read + bytes_written * (fusion->user_count() + 1);
}

// Returns bytes transferred if 'fusion' were to be merged into its users.
double GetMergedBytesTransferred(HloInstruction* fusion) {
  CHECK_EQ(HloOpcode::kFusion, fusion->opcode());
  return CalculateBytesReadByFusionInstruction(fusion) * fusion->user_count();
}

}  // anonymous namespace

// FusionInstructionMerger visits all fusion instructions in 'computation'
// in post order, attempting to merge each into all of its users.
// Accumulates and reports stats on successful/failed merge attempts.
class FusionInstructionMerger {
 public:
  explicit FusionInstructionMerger(HloComputation* computation)
      : computation_(computation) {}

  Status Run();

  bool changed() const { return changed_; }

 private:
  Status HandleFusion(HloInstruction* fusion);

  HloComputation* computation_;
  bool changed_ = false;

  // Fusion instruction merge stats.
  int total_visited_ = 0;
  int total_merged_ = 0;
  int num_fail_no_users_ = 0;
  int num_fail_not_loop_fusion_ = 0;
  int num_fail_merge_all_users_ = 0;
  int num_fail_expensive_fused_instruction_ = 0;
  int num_fail_flops_to_byte_ratio_ = 0;
  int num_fail_net_bytes_transferred_ratio_ = 0;

  TF_DISALLOW_COPY_AND_ASSIGN(FusionInstructionMerger);
};

Status FusionInstructionMerger::Run() {
  for (auto* instruction : computation_->MakeInstructionPostOrder()) {
    if (instruction->opcode() == HloOpcode::kFusion) {
      TF_RETURN_IF_ERROR(HandleFusion(instruction));
    }
  }

  VLOG(1) << "FusionInstructionMerger EXIT"
          << " computation: " << computation_->name()
          << " total_visited: " << total_visited_
          << " total_merged: " << total_merged_ << " merge failures { "
          << " no_users: " << num_fail_no_users_
          << " not_loop_fusion: " << num_fail_not_loop_fusion_
          << " merge_all_users: " << num_fail_merge_all_users_
          << " expensive_instruction: " << num_fail_expensive_fused_instruction_
          << " flops_to_byte_ratio: " << num_fail_flops_to_byte_ratio_
          << " net_bytes_transferred: " << num_fail_net_bytes_transferred_ratio_
          << " }";
  return Status::OK();
}

Status FusionInstructionMerger::HandleFusion(HloInstruction* fusion) {
  VLOG(3) << "FusionInstructionMerger ENTRY fusion: " << fusion->name()
          << " flops_to_bytes_ratio: " << CalculateFlopsToBytesRatio(fusion);
  ++total_visited_;
  // Skip 'fusion' instruction if there are no users into which we can merge.
  if (fusion->users().empty()) {
    VLOG(3) << "Not merging " << fusion->name() << ": Has no users.";
    ++num_fail_no_users_;
    return Status::OK();
  }

  // Skip 'fusion' instruction if it is not a loop fusion. Library fusion
  // instructions match specific patterns, so they shouldn't be further fused.
  // Input fusion instructions need to be rooted at a particular HLO (e.g.
  // kReduce), so they shouldn't be further fused either.
  if (fusion->fusion_kind() != HloInstruction::FusionKind::kLoop) {
    VLOG(3) << "Not merging " << fusion->name() << ": Is not loop fusion.";
    ++num_fail_not_loop_fusion_;
    return Status::OK();
  }

  // Skip multiple output fusion. It's not yet supported.
  if (fusion->IsMultiOutputFusion()) {
    VLOG(3) << "Not merging " << fusion->name() << ": Is multi-output fusion.";
    ++num_fail_not_loop_fusion_;
    return Status::OK();
  }
  // Skip 'fusion' instruction if we cannot merge into all of its users.
  // Merging into all users enables the removal of 'fusion' from the
  // computation.
  if (!c_all_of(fusion->users(), [](const HloInstruction* user) {
        return user->opcode() == HloOpcode::kFusion &&
               (user->fusion_kind() == HloInstruction::FusionKind::kLoop ||
                user->fusion_kind() == HloInstruction::FusionKind::kInput);
      })) {
    VLOG(3) << "Not merging " << fusion->name()
            << ": Some of its users are not loop/input fusion kernels.";
    ++num_fail_merge_all_users_;
    return Status::OK();
  }

  // Skip 'fusion' instruction if any of its fused instructions are expensive.
  // This is done to avoid the duplication of expensive instructions, which
  // would occur if 'fusion' were merged into multiple users.
  //
  // If 'fusion' has just one user, then an earlier fusion pass chose not to
  // fuse this producer/comsumer pair (likely because of expensive instruction
  // re-use by the consumer), and so we honor that choice here as well.
  if (c_any_of(fusion->fused_instructions(),
               [](const HloInstruction* instruction) {
                 return instruction->opcode() != HloOpcode::kParameter &&
                        GpuInstructionFusion::IsExpensive(*instruction);
               })) {
    VLOG(3) << "Not merging " << fusion->name()
            << ": Contains one or more expensive instructions.";
    ++num_fail_expensive_fused_instruction_;
    return Status::OK();
  }

  // Skip 'fusion' instruction if its flops to bytes transferred ratio
  // exceeds the threshold value.
  if (CalculateFlopsToBytesRatio(fusion) >
      FusionMerger::GetThresholdFlopsToBytesRatio()) {
    VLOG(3) << "Not merging " << fusion->name()
            << ": flops-to-bytes ratio is not favorable.";
    ++num_fail_flops_to_byte_ratio_;
    return Status::OK();
  }
  // Skip 'fusion' instruction if merging it into all users would result in a
  // net increase in bytes transferred (currently allowing the net bytes
  // transferred to be exceeded up to ~10% in exhange for eliminating the
  // overhead from a GPU kernel launch).
  const double current_bytes_transferred = GetCurrentBytesTransferred(fusion);
  const double merged_bytes_transferred = GetMergedBytesTransferred(fusion);
  const double merged_to_current_bytes_ratio =
      merged_bytes_transferred / std::max(1.0, current_bytes_transferred);
  if (merged_to_current_bytes_ratio > 1.10) {
    VLOG(3) << "Not merging " << fusion->name()
            << ": merged-to-current-bytes-ratio of "
            << merged_to_current_bytes_ratio << " is not favorable.";
    ++num_fail_net_bytes_transferred_ratio_;
    return Status::OK();
  }
  // Merge fused instructions from 'fusion' into each user.
  std::vector<HloInstruction*> users = fusion->users();
  for (HloInstruction* user : users) {
    user->MergeFusionInstruction(fusion);
    changed_ = true;
  }
  ++total_merged_;
  VLOG(2) << "Merged fusion instruction: " << fusion->name()
          << " flops_to_bytes_ratio: " << CalculateFlopsToBytesRatio(fusion)
          << " merged_to_current_bytes_ratio: " << merged_to_current_bytes_ratio
          << " into users { "
          << tensorflow::str_util::Join(users, ", ",
                                        [](string* out, HloInstruction* user) {
                                          tensorflow::strings::StrAppend(
                                              out, user->name());
                                        })
          << " }";
  // Remove 'fusion' instruction.
  CHECK_EQ(0, fusion->user_count());
  return computation_->RemoveInstruction(fusion);
}

StatusOr<bool> FusionMerger::Run(HloModule* module) {
  bool changed = false;
  VLOG(2) << "FusionMerger for module: " << module->name();
  for (auto* computation : module->MakeNonfusionComputations()) {
    VLOG(1) << "Before running FusionInstructionMerger for computation: "
            << computation->name();
    XLA_VLOG_LINES(3, computation->ToString());

    FusionInstructionMerger fusion_merger(computation);
    TF_RETURN_IF_ERROR(fusion_merger.Run());
    changed |= fusion_merger.changed();

    VLOG(1) << "After running FusionInstructionMerger for computation: "
            << computation->name() << " changed: " << changed;
    XLA_VLOG_LINES(3, computation->ToString());
  }
  return changed;
}

}  // namespace gpu
}  // namespace xla
