android-12.0.0_r34/s

/* Copyright 2019 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/horizontal_loop_fusion.h"

#include <algorithm>

#include "absl/container/flat_hash_set.h"
#include "absl/types/span.h"
#include "tensorflow/compiler/xla/layout_util.h"
#include "tensorflow/compiler/xla/service/gpu/gpu_fusible.h"
#include "tensorflow/compiler/xla/service/hlo_creation_utils.h"
#include "tensorflow/core/platform/errors.h"
#include "tensorflow/core/util/env_var.h"

namespace xla {
namespace gpu {

namespace {

absl::InlinedVector<HloInstruction*, 2> GetOutputsOfFusion(
    const HloInstruction& instr) {
  CHECK(instr.opcode() == HloOpcode::kFusion);
  HloInstruction* root = instr.fused_expression_root();
  if (root->opcode() != HloOpcode::kTuple) {
    return {root};
  } else {
    return root->operands();
  }
}

// Returns the number of outputs of the fused computation.
size_t GetOutputSizeOfFusion(const HloInstruction& instr) {
  CHECK(instr.opcode() == HloOpcode::kFusion);
  const HloInstruction* root = instr.fused_expression_root();
  if (root->opcode() != HloOpcode::kTuple) {
    return 1;
  } else {
    return ShapeUtil::TupleElementCount(root->shape());
  }
}

PrimitiveType GetUniqueOutputTypeOfFusion(const HloInstruction& instr) {
  auto outputs = GetOutputsOfFusion(instr);
  CHECK(!outputs.empty());
  PrimitiveType first_output_type = outputs[0]->shape().element_type();
  for (size_t i = 1; i < outputs.size(); ++i) {
    PrimitiveType cur_output_type = outputs[i]->shape().element_type();
    CHECK(first_output_type == cur_output_type)
        << "Output types are expected to be unique, but see "
        << PrimitiveType_Name(first_output_type) << " and "
        << PrimitiveType_Name(cur_output_type);
  }

  return first_output_type;
}

class HorizontalLoopFusionImpl {
 public:
  explicit HorizontalLoopFusionImpl(HloComputation* computation)
      : computation_(computation) {}

  ~HorizontalLoopFusionImpl() {}

  StatusOr<bool> Run();

 private:
  Status Fuse(absl::Span<HloInstruction*> fused_fusion_instrs);

  // Horizontally fuses `fused_fusion_instrs`. It is required that each of
  // `fused_fusion_instrs` is a kLoop fusion. Also, we require their numbers of
  // outputs to be the same, so that each output will be fused/concatenated with
  // the same number of outputs from other fused fusion instrs. Then, all the
  // fused outputs still have the same shapes for kernel generation.
  //
  // Returns the fused computation in `uniq_computation` and the operands that
  // are used by `uniq_computation`.
  Status CreateFusedComputation(
      absl::Span<HloInstruction*> fused_fusion_instrs,
      std::unique_ptr<HloComputation>* uniq_computation,
      std::vector<HloInstruction*>* bound_operands);

  // FusionCandidates collects profitable candidates for a given consumer
  // instruction. GetNextSpanOfFusions() can then be iteratively invoked to
  // acquire the next set of fusion candidates based on some heuristics.
  class FusionCandidates {
   public:
    explicit FusionCandidates(HloInstruction* consumer)
        : fusion_instrs_(), pos_(0) {
      Initialize(consumer);
    }

    // Gets a span of fusions to be fused.
    absl::Span<HloInstruction*> GetNextSpanOfFusions();

   private:
    void Initialize(HloInstruction*);

    std::vector<HloInstruction*> fusion_instrs_;
    // `pos_` points to the start position of the next span.
    size_t pos_;
  };

  HloComputation* computation_;
};  // HorizontalLoopFusionImpl

bool IsFusionSupported(const HloInstruction& instr) {
  // Support only kLoop fusion now.
  if (!instr.IsLoopFusion()) {
    return false;
  }

  // Cannot support fusion who has multiple output types, because the
  // concatenate (inserted for horizontal fusion) requires the same type
  // for all of its operands.
  auto outputs = GetOutputsOfFusion(instr);
  CHECK(!outputs.empty());
  const HloInstruction* first_output = outputs[0];
  for (size_t i = 1; i < outputs.size(); ++i) {
    if (first_output->shape().element_type() !=
        outputs[i]->shape().element_type()) {
      return false;
    }
  }

  return true;
}

// Returns whether `instr` is a profitable candidate to be horizontally fused.
// Since the primary benefit of horizontal fusion comes from reducing the
// kernel launch overhead, we want to exclude the instructions with
// insignificant kernel launch overhead. In other words, we exclude instructions
// if their computation latencies are longer than launch latencies. We estimate
// the computation latency of a given instruction by its shapes and the
// instruction count in its fused computation. We roughly observe that if a
// fusion instruction has shapes smaller than `kShapeThreshold` and has fewer
// instructions than `kInstrCountThreshold`, it is launch-latency-bound and
// profitable by horizontal fusion.
bool IsProfitableFusionCandidate(const HloInstruction& instr) {
  CHECK(instr.opcode() == HloOpcode::kFusion);
  constexpr int64 kShapeThreshold = 128 * 2048;
  constexpr int64 kInstrCountThreshold = 30;
  auto root = instr.fused_expression_root();

  // Too large shapes are not easily profitable.
  if (root->opcode() == HloOpcode::kTuple) {
    // Since all output shapes are the same, use the first shape as the
    // representative.
    auto shape = root->operand(0)->shape();
    if (ShapeUtil::ElementsIn(shape) > kShapeThreshold) {
      return false;
    }
  } else {
    auto shape = root->shape();
    if (ShapeUtil::ElementsIn(shape) > kShapeThreshold) {
      return false;
    }
  }

  // Having too many instructions is not easily profitable.
  if (instr.fused_instruction_count() > kInstrCountThreshold) {
    return false;
  }

  // We can emit DUS in-place, horizontally fusing it makes the emitter no
  // longer recognize that it can be done in-place. This creates much slower
  // code. This restriction could be lifted if buffer assignment would recognize
  // that the DUS can be done in-place even inside of a horizontal fusion.
  if (root->opcode() == HloOpcode::kDynamicUpdateSlice) {
    return false;
  }

  return true;
}

// Returns whether `fusion_instr` has only row-major layouts.
// The horizontal fusion excludes computations with non-row-major layouts,
// because fusing computations with different layouts can result in uncoalesced
// memory accesses and cause great performance overhead.
bool HasOnlyRowMajorLayout(const HloInstruction& fusion_instr) {
  CHECK(fusion_instr.opcode() == HloOpcode::kFusion);
  auto instrs = fusion_instr.fused_instructions_computation()->instructions();
  for (auto instr : instrs) {
    if (instr->shape().layout().format() != DENSE) {
      continue;
    }
    if (!LayoutUtil::IsMonotonicWithDim0Major(instr->shape().layout())) {
      return false;
    }
  }
  return true;
}

void HorizontalLoopFusionImpl::FusionCandidates::Initialize(
    HloInstruction* consumer) {
  // First, find out all fusion instructions. We will filter out
  // unsupported/non-profitable cases below.
  absl::flat_hash_set<HloInstruction*> fusion_instrs;
  for (auto opnd : consumer->operands()) {
    auto predecessor = opnd->LatestNonGteAncestor();
    if (predecessor->opcode() == HloOpcode::kFusion) {
      fusion_instrs.insert(predecessor);
    }
  }

  for (auto instr : fusion_instrs) {
    if (!IsFusionSupported(*instr)) {
      VLOG(2) << "Reject unsupported fusion instr " << instr->ToString();
      continue;
    } else if (!IsConsumerTheOnlyNonRootUser(*instr, *consumer)) {
      VLOG(2) << "Reject maybe illegal instr " << instr->ToString()
              << "; including it may create cycles in HLO.";
      continue;
    } else if (!IsProfitableFusionCandidate(*instr)) {
      VLOG(2) << "Reject may-not-be profitable fusion instr "
              << instr->ToString();
      continue;
    } else if (!HasOnlyRowMajorLayout(*instr)) {
      VLOG(2) << "Reject non-row-major fusion instr " << instr->ToString();
      continue;
    } else {
      VLOG(2) << "Find a fusion candidate " << instr->ToString();
      fusion_instrs_.push_back(instr);
    }
  }

  // Sort `fusion_instrs` according to output types, the number of outputs,
  // and instruction counts, because we only fuse instructions with the same
  // number/type of outputs and whose computations have the same instruction
  // count.
  std::sort(
      fusion_instrs_.begin(), fusion_instrs_.end(),
      [&](const HloInstruction* a, const HloInstruction* b) {
        if (GetUniqueOutputTypeOfFusion(*a) !=
            GetUniqueOutputTypeOfFusion(*b)) {
          return GetUniqueOutputTypeOfFusion(*a) <
                 GetUniqueOutputTypeOfFusion(*b);
        } else if (GetOutputSizeOfFusion(*a) != GetOutputSizeOfFusion(*b)) {
          return GetOutputSizeOfFusion(*a) < GetOutputSizeOfFusion(*b);
        } else {
          return a->fused_instruction_count() < b->fused_instruction_count();
        }
      });
}

// Gets a next span of fusion instructions to be fused.
absl::Span<HloInstruction*>
HorizontalLoopFusionImpl::FusionCandidates::GetNextSpanOfFusions() {
  if (pos_ >= fusion_instrs_.size()) {
    return absl::Span<HloInstruction*>();
  }

  // Fusing too many computations at a time may not be easily profitable and
  // may increase compile time due to large kernels. Set a limit to it.
  constexpr int64 kMaxFusionBatchSize = 32;
  // CUDA has a parameter size limit of ~4k bytes.
  constexpr int64 kMaxCudaParamSize = 4000;
  size_t accum_io_size = 0;
  auto reach_max_fusion_batch_size = [&](size_t left, size_t right) -> bool {
    if (right - left >= kMaxFusionBatchSize) {
      return true;
    }

    accum_io_size += fusion_instrs_.at(right)->fused_parameters().size() +
                     GetOutputSizeOfFusion(*fusion_instrs_.at(right));

    if (accum_io_size * 8 >= kMaxCudaParamSize) {
      return true;
    }

    return false;
  };

  size_t left = pos_;
  size_t right = pos_ + 1;
  size_t first_output_size = GetOutputSizeOfFusion(*fusion_instrs_[left]);
  PrimitiveType first_output_type =
      GetUniqueOutputTypeOfFusion(*fusion_instrs_[left]);
  for (; right < fusion_instrs_.size(); ++right) {
    PrimitiveType cur_output_type =
        GetUniqueOutputTypeOfFusion(*fusion_instrs_[right]);
    if (first_output_type != cur_output_type) {
      // Cannot fuse computations who have multiple output types.
      break;
    } else if (first_output_size !=
               GetOutputSizeOfFusion(*fusion_instrs_[right])) {
      // Cannot fuse computations who have different numbers of outputs.
      break;
    } else if (fusion_instrs_[left]->fused_instruction_count() !=
               fusion_instrs_[right]->fused_instruction_count()) {
      // Do not fuse computations of different instruction counts as it may
      // introduce control divergence. This is a very simple heuristic to avoid
      // fusing computations with too much discrepancy and we may improve it
      // when the needs arise.
      break;
    } else if (reach_max_fusion_batch_size(left, right)) {
      // Hit max fusion batch size.
      break;
    }
  }

  pos_ = right;
  return absl::MakeSpan(fusion_instrs_).subspan(left, right - left);
}

Status HorizontalLoopFusionImpl::CreateFusedComputation(
    absl::Span<HloInstruction*> fused_fusion_instrs,
    std::unique_ptr<HloComputation>* uniq_computation,
    std::vector<HloInstruction*>* bound_operands) {
  // First, build a computation with only params.
  HloComputation::Builder b("horizontally_fused_computation");
  size_t fused_comp_param_id = 0;
  for (size_t i = 0; i < fused_fusion_instrs.size(); ++i) {
    auto old_params = fused_fusion_instrs[i]->fused_parameters();
    for (size_t j = 0; j < old_params.size(); ++j) {
      auto bound_opnd = fused_fusion_instrs[i]->mutable_operand(j);
      // in a form of param_i_j
      b.AddInstruction(HloInstruction::CreateParameter(
          fused_comp_param_id++, bound_opnd->shape(),
          absl::StrCat("param_", i, "_", j)));
      bound_operands->push_back(bound_opnd);
    }
  }
  // Always create a dummy tuple instruction to serve as the root of the
  // computation, as the existence of a root instruction is required by the
  // HloComputation. The real root instruction will replace it below.
  auto dummy_root = b.AddInstruction(
      HloInstruction::CreateTuple(std::vector<HloInstruction*>{}));
  *uniq_computation = b.Build(dummy_root);
  auto* comp = uniq_computation->get();

  // Preparing clone_map, which maps old operand to new operand.
  absl::flat_hash_map<HloInstruction*, HloInstruction*> clone_map;
  size_t new_param_id = 0;
  for (size_t i = 0; i < fused_fusion_instrs.size(); ++i) {
    auto old_params = fused_fusion_instrs[i]->fused_parameters();
    for (size_t j = 0; j < old_params.size(); ++j) {
      auto old_param = old_params[j];
      auto new_param = comp->parameter_instruction(new_param_id++);
      clone_map.insert({old_param, new_param});
    }
  }

  // Clone every fused computation.
  for (size_t i = 0; i < fused_fusion_instrs.size(); ++i) {
    auto def_to_use_order = fused_fusion_instrs[i]
                                ->fused_instructions_computation()
                                ->MakeInstructionPostOrder();
    for (auto old_instr : def_to_use_order) {
      if (old_instr->opcode() == HloOpcode::kParameter) {
        // Parameters have been created.
        continue;
      }
      std::vector<HloInstruction*> new_opnds;
      for (auto old_opnd : old_instr->operands()) {
        CHECK(clone_map.find(old_opnd) != clone_map.end());
        new_opnds.push_back(clone_map[old_opnd]);
      }
      auto new_instr = comp->AddInstruction(
          old_instr->CloneWithNewOperands(old_instr->shape(), new_opnds));
      clone_map.insert({old_instr, new_instr});
    }
  }

  std::vector<HloInstruction*> concated_outputs;
  // Since we require each fusion to have the same number of outputs, we can
  // simply use the first fusion as the representative for output size.
  size_t fused_instr_output_size =
      GetOutputSizeOfFusion(*fused_fusion_instrs[0]);
  for (size_t i = 0; i < fused_instr_output_size; ++i) {
    std::vector<HloInstruction*> reshapes(fused_fusion_instrs.size());
    for (size_t j = 0; j < fused_fusion_instrs.size(); ++j) {
      auto old_output = GetOutputsOfFusion(*fused_fusion_instrs[j])[i];
      auto new_output = clone_map[old_output];
      TF_ASSIGN_OR_RETURN(
          reshapes[j],
          MakeReshapeHlo(ShapeUtil::MakeShapeWithLayout(
                             new_output->shape().element_type(),
                             {ShapeUtil::ElementsIn(new_output->shape())},
                             /*minor_to_major=*/std::vector<int64>(1, 0)),
                         new_output));
    }
    TF_ASSIGN_OR_RETURN(auto concated_output, MakeConcatHlo(reshapes, 0));
    concated_outputs.push_back(concated_output);
  }

  // Make slices of outputs.
  std::vector<HloInstruction*> output_slices(concated_outputs.size() *
                                             fused_fusion_instrs.size());
  for (size_t i = 0; i < concated_outputs.size(); ++i) {
    auto concated_output = concated_outputs[i];
    int64 slice_start = 0;
    // Create a slice per fused computation.
    for (size_t j = 0; j < fused_fusion_instrs.size(); ++j) {
      auto old_output = GetOutputsOfFusion(*fused_fusion_instrs[j])[i];
      auto shape = old_output->shape();
      int64 slice_limit = slice_start + ShapeUtil::ElementsIn(shape);
      TF_ASSIGN_OR_RETURN(
          output_slices[concated_outputs.size() * j + i],
          MakeSliceHlo(concated_output, {slice_start}, {slice_limit},
                       /*strides=*/{1}));
      slice_start = slice_limit;
    }
  }

  // Make a tuple of output_slices.
  auto tuple = comp->AddInstruction(HloInstruction::CreateTuple(output_slices));
  comp->set_root_instruction(tuple, /*accept_different_shape=*/true);
  TF_RETURN_IF_ERROR(comp->RemoveInstruction(dummy_root));

  return Status::OK();
}

Status HorizontalLoopFusionImpl::Fuse(
    absl::Span<HloInstruction*> fused_fusion_instrs) {
  // Fuse fused_fusion_instrs and replace them with the new fused computation.
  std::unique_ptr<HloComputation> uniq_computation;
  std::vector<HloInstruction*> bound_operands;
  TF_RETURN_IF_ERROR(CreateFusedComputation(
      fused_fusion_instrs, &uniq_computation, &bound_operands));
  auto fused_comp = computation_->parent()->AddEmbeddedComputation(
      std::move(uniq_computation));
  auto hori_fusion_instr =
      computation_->AddInstruction(HloInstruction::CreateFusion(
          fused_comp->root_instruction()->shape(),
          HloInstruction::FusionKind::kInput, bound_operands, fused_comp));
  fused_comp->SetFusionInstruction(hori_fusion_instr);

  // Insert bitcasts and replace corresponding users. Note that we do not insert
  // the bitcasts in the fused computation as it does not fit into the slice
  // input fusion pattern. However, inserting bitcasts outside the fused
  // computation creates no performance cost.
  size_t total_output_id = 0;
  for (size_t i = 0; i < fused_fusion_instrs.size(); ++i) {
    std::vector<HloInstruction*> bitcasts;
    auto fused_instr = fused_fusion_instrs[i];
    auto num_outputs = GetOutputSizeOfFusion(*fused_instr);
    for (size_t j = 0; j < num_outputs; ++j) {
      auto output = GetOutputsOfFusion(*fused_instr)[j];
      TF_ASSIGN_OR_RETURN(auto gep, MakeGetTupleElementHlo(hori_fusion_instr,
                                                           total_output_id++));
      bitcasts.push_back(computation_->AddInstruction(
          HloInstruction::CreateBitcast(output->shape(), gep)));
    }
    auto bitcast_or_tuple = (bitcasts.size() == 1)
                                ? bitcasts.at(0)
                                : computation_->AddInstruction(
                                      HloInstruction::CreateTuple(bitcasts));
    TF_RETURN_IF_ERROR(
        computation_->ReplaceInstruction(fused_instr, bitcast_or_tuple));
  }

  return Status::OK();
}

StatusOr<bool> HorizontalLoopFusionImpl::Run() {
  bool changed = false;
  XLA_VLOG_LINES(3, computation_->ToString());

  // Using def-to-use order is sound since we do not modify users.
  std::vector<HloInstruction*> def_to_use_order =
      computation_->MakeInstructionPostOrder();
  for (size_t i = 0; i < def_to_use_order.size(); ++i) {
    auto consumer = def_to_use_order[i];
    HorizontalLoopFusionImpl::FusionCandidates fusion_candidates(consumer);
    while (true) {
      auto fusions = fusion_candidates.GetNextSpanOfFusions();
      if (fusions.empty()) {
        break;
      } else if (fusions.size() == 1) {
        // Skip; there is just one fused_instr.
        continue;
      }

      changed = true;
      TF_RETURN_IF_ERROR(Fuse(fusions));
    }
  }

  return changed;
}

}  // namespace

StatusOr<bool> GpuHorizontalLoopFusion::RunOnComputation(
    HloComputation* computation) {
  HorizontalLoopFusionImpl horizontal_fusion_impl(computation);
  return horizontal_fusion_impl.Run();
}

StatusOr<bool> GpuHorizontalLoopFusion::Run(HloModule* module) {
  bool changed = false;
  VLOG(2) << "Run horizontal fusion.";
  for (auto* comp : module->MakeNonfusionComputations()) {
    TF_ASSIGN_OR_RETURN(changed, RunOnComputation(comp));
  }

  return changed;
}

}  // namespace gpu
}  // namespace xla