/* Copyright 2017 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.
==============================================================================*/

// Defines the data returned by the XLA buffer assignment packages.

#include "tensorflow/compiler/xla/service/buffer_assignment.h"

#include <algorithm>
#include <deque>
#include <ostream>
#include <utility>

#include "tensorflow/compiler/xla/legacy_flags/buffer_assignment_flags.h"
#include "tensorflow/compiler/xla/map_util.h"
#include "tensorflow/compiler/xla/ptr_util.h"
#include "tensorflow/compiler/xla/service/heap_simulator.h"
#include "tensorflow/compiler/xla/service/hlo_opcode.h"
#include "tensorflow/compiler/xla/service/tuple_points_to_analysis.h"
#include "tensorflow/compiler/xla/shape_util.h"
#include "tensorflow/compiler/xla/status_macros.h"
#include "tensorflow/compiler/xla/types.h"
#include "tensorflow/compiler/xla/util.h"
#include "tensorflow/core/lib/core/errors.h"
#include "tensorflow/core/lib/hash/hash.h"
#include "tensorflow/core/lib/strings/numbers.h"
#include "tensorflow/core/lib/strings/str_util.h"
#include "tensorflow/core/lib/strings/strcat.h"
#include "tensorflow/core/lib/strings/stringprintf.h"

namespace xla {

using ::tensorflow::gtl::FlatMap;
using ::tensorflow::gtl::FlatSet;
using ::tensorflow::strings::Appendf;
using ::tensorflow::strings::HumanReadableNumBytes;

size_t BufferAllocation::Slice::Hasher::operator()(Slice s) const {
  uint64 h = std::hash<int64>()(s.index());
  h = tensorflow::Hash64Combine(h, std::hash<int64>()(s.offset()));
  h = tensorflow::Hash64Combine(h, std::hash<int64>()(s.size()));
  return h;
}

string BufferAllocation::Slice::ToString() const {
  return tensorflow::strings::StrCat("{index:", index(), ", offset:", offset_,
                                     ", size:", size_, "}");
}

BufferAllocation::Slice BufferAllocation::GetSlice(
    const LogicalBuffer& buffer) const {
  const OffsetSize os = FindOrDie(assigned_buffers_, &buffer);
  return Slice(this, os.offset, os.size);
}

void BufferAllocation::AddAssignment(const LogicalBuffer& buffer, int64 offset,
                                     int64 size) {
  CHECK(assigned_buffers_.count(&buffer) == 0)
      << "LogicalBuffer " << buffer << " already assigned to allocation "
      << index_;
  CHECK_LE(offset, size_) << "LogicalBuffer " << buffer
                          << " offset out of range";
  CHECK_LE(offset + size, size_)
      << "LogicalBuffer " << buffer << " size out of range";
  OffsetSize offset_size;
  offset_size.offset = offset;
  offset_size.size = size;
  assigned_buffers_.emplace(&buffer, offset_size);
}

string BufferAllocation::ToString() const {
  string output;
  tensorflow::strings::StrAppend(
      &output, tensorflow::strings::Printf("allocation %lld: %p, size %lld",
                                           index_, this, size()));
  if (is_entry_computation_parameter()) {
    tensorflow::strings::StrAppend(&output, ", parameter ", parameter_number());
  }
  if (is_thread_local()) {
    tensorflow::strings::StrAppend(&output, ", thread-local");
  }
  if (maybe_live_out()) {
    tensorflow::strings::StrAppend(&output, ", maybe-live-out");
  }
  if (IsPreallocatedTempBuffer()) {
    tensorflow::strings::StrAppend(&output, ", preallocated-temp");
  }
  tensorflow::strings::StrAppend(&output, ":\n");
  // Dump the assigned buffers ordered by id.
  std::vector<const LogicalBuffer*> sorted_buffers;
  for (const auto& buffer_offset_size : assigned_buffers_) {
    sorted_buffers.push_back(buffer_offset_size.first);
  }
  std::sort(sorted_buffers.begin(), sorted_buffers.end(),
            [](const LogicalBuffer* a, const LogicalBuffer* b) {
              return a->id() < b->id();
            });
  for (const LogicalBuffer* buffer : sorted_buffers) {
    const OffsetSize& offset_size = FindOrDie(assigned_buffers_, buffer);
    tensorflow::strings::StrAppend(
        &output,
        tensorflow::strings::Printf(
            "  %s [%lld,%lld]: %s\n", buffer->ToString().c_str(),
            offset_size.offset, offset_size.size,
            ShapeUtil::HumanStringWithLayout(buffer->shape()).c_str()));
  }
  return output;
}

std::ostream& operator<<(std::ostream& out, const BufferAllocation& buffer) {
  out << buffer.ToString();
  return out;
}

std::ostream& operator<<(std::ostream& out, const BufferAllocation::Slice& s) {
  out << s.ToString();
  return out;
}

const PointsToSet& BufferAssignment::GetPointsToSet(
    const HloInstruction* instruction) const {
  return points_to_analysis().GetPointsToSet(instruction);
}

bool BufferAssignment::HasAllocation(const LogicalBuffer& buffer) const {
  TF_CHECK_OK(points_to_analysis().VerifyBuffer(buffer));
  return allocation_index_for_buffer_.count(&buffer) > 0;
}

const BufferAllocation& BufferAssignment::GetAssignedAllocation(
    const LogicalBuffer& buffer) const {
  CHECK(HasAllocation(buffer));
  return GetAllocation(allocation_index_for_buffer_.at(&buffer));
}

BufferAllocation* BufferAssignment::GetMutableAssignedAllocation(
    const LogicalBuffer& buffer) {
  return const_cast<BufferAllocation*>(&GetAssignedAllocation(buffer));
}

std::set<BufferAllocation::Slice> BufferAssignment::GetAllSlices(
    const HloInstruction* instruction, const ShapeIndex& index) const {
  std::set<BufferAllocation::Slice> result;
  for (const LogicalBuffer* buffer : GetSourceBuffers(instruction, index)) {
    if (HasAllocation(*buffer)) {
      result.insert(GetAssignedAllocation(*buffer).GetSlice(*buffer));
    }
  }
  return result;
}

const BufferAllocation& BufferAssignment::GetAllocation(
    BufferAllocation::Index index) const {
  CHECK_GE(index, 0);
  CHECK_LT(index, allocations_.size());
  return allocations_[index];
}

BufferAllocation* BufferAssignment::GetMutableAllocation(
    BufferAllocation::Index index) {
  return const_cast<BufferAllocation*>(&GetAllocation(index));
}

bool BufferAssignment::HasTopLevelAllocation(
    const HloInstruction* instruction) const {
  for (const LogicalBuffer* buffer :
       GetPointsToSet(instruction).element(/*index=*/{})) {
    if (allocation_index_for_buffer_.count(buffer) > 0) {
      return true;
    }
  }
  return false;
}

StatusOr<BufferAllocation::Slice> BufferAssignment::GetUniqueSlice(
    const HloInstruction* instruction, const ShapeIndex& index) const {
  BufferAllocation::Slice result;
  for (const LogicalBuffer* buffer :
       GetPointsToSet(instruction).element(index)) {
    if (HasAllocation(*buffer)) {
      const BufferAllocation::Slice slice =
          GetAssignedAllocation(*buffer).GetSlice(*buffer);
      if (result.allocation() == nullptr) {
        result = slice;
      } else if (result != slice) {
        return FailedPrecondition(
            "BufferAllocation::Slice for instruction %s at index %s cannot "
            "be determined at compile-time.",
            instruction->name().c_str(), index.ToString().c_str());
      }
    }
  }
  if (result.allocation() == nullptr) {
    return FailedPrecondition(
        "BufferAllocation::Slice not assigned for instruction %s at index %s",
        instruction->name().c_str(), index.ToString().c_str());
  }
  return result;
}

StatusOr<BufferAllocation::Slice> BufferAssignment::GetUniqueTopLevelSlice(
    const HloInstruction* instruction) const {
  return GetUniqueSlice(instruction, /*index=*/{});
}

bool BufferAssignment::SharesSliceAtIndex(
    const HloInstruction* hlo_a, const ShapeIndex& shape_index_a,
    const HloInstruction* hlo_b, const ShapeIndex& shape_index_b) const {
  return GetUniqueSlice(hlo_a, shape_index_a).ConsumeValueOrDie() ==
         GetUniqueSlice(hlo_b, shape_index_b).ConsumeValueOrDie();
}

StatusOr<BufferAllocation::Slice>
BufferAssignment::GetUniqueTopLevelOutputSlice() const {
  return GetUniqueTopLevelSlice(
      module_->entry_computation()->root_instruction());
}

BufferAllocation* BufferAssignment::NewEmptyAllocation(int64 size,
                                                       bool is_thread_local,
                                                       bool is_reusable) {
  BufferAllocation::Index index = allocations_.size();
  allocations_.emplace_back(index, size, is_thread_local, is_reusable);
  BufferAllocation* allocation = &allocations_.back();
  return allocation;
}

BufferAllocation* BufferAssignment::NewAllocation(const LogicalBuffer& buffer,
                                                  int64 size,
                                                  bool is_thread_local,
                                                  bool is_reusable) {
  BufferAllocation* allocation =
      NewEmptyAllocation(size, is_thread_local, is_reusable);
  AddAssignment(allocation, buffer, /*offset=*/0, size);
  return allocation;
}

// Adds an instruction to the set assigned to the given buffer.
void BufferAssignment::AddAssignment(BufferAllocation* allocation,
                                     const LogicalBuffer& buffer, int64 offset,
                                     int64 size) {
  CHECK_EQ(0, allocation_index_for_buffer_.count(&buffer))
      << "LogicalBuffer " << buffer << " already has an allocation.";
  CHECK(allocation->is_reusable() || allocation->assigned_buffers().empty())
      << "Non-reusable allocation already assigned a buffer";

  TF_CHECK_OK(points_to_analysis().VerifyBuffer(buffer));

  allocation->AddAssignment(buffer, offset, size);
  allocation_index_for_buffer_[&buffer] = allocation->index();
}

// Combines allocations of temporary buffers into one big BufferAllocation.
void BufferAssignment::CombineTempAllocations() {
  // Move all temp allocations into a single run at the end of the allocations
  // vector, and combine them into the first allocation of the run.
  const auto first_temp_it =
      std::partition(allocations_.begin(), allocations_.end(),
                     [](const BufferAllocation& allocation) {
                       return !allocation.IsPreallocatedTempBuffer();
                     });
  if (first_temp_it != allocations_.end()) {
    BufferAllocation* combined = &*first_temp_it;
    const auto second_temp_it = std::next(first_temp_it);
    for (auto it = second_temp_it; it != allocations_.end(); ++it) {
      // Each temp allocation is placed end-to-end, accounting for alignment.
      // The offset of each buffer in the combined allocation is computed from
      // the base offset of the allocation.
      const int64 base = RoundUpToNearest(combined->size(), alignment_);
      combined->set_size(base + it->size());
      for (const auto& buffer_offset_size : it->assigned_buffers_) {
        const LogicalBuffer* buffer = buffer_offset_size.first;
        const int64 offset = buffer_offset_size.second.offset;
        const int64 size = buffer_offset_size.second.size;
        combined->AddAssignment(*buffer, base + offset, size);
      }
    }
    allocations_.erase(second_temp_it, allocations_.end());
    temp_allocation_ = combined;
  }

  // Update allocation indices to their new positions.
  allocation_index_for_buffer_.clear_no_resize();
  for (size_t index = 0; index < allocations_.size(); ++index) {
    BufferAllocation* allocation = &allocations_[index];
    allocation->set_index(index);
    for (const auto& buffer_offset_size : allocation->assigned_buffers_) {
      const LogicalBuffer* buffer = buffer_offset_size.first;
      allocation_index_for_buffer_[buffer] = index;
    }
  }
}

Status BufferAssignment::ComputeSummaryStats(
    const LogicalBuffer::SizeFunction& buffer_size) {
  for (auto& allocation : Allocations()) {
    if (allocation.is_entry_computation_parameter()) {
      stats_.parameter_allocation_count++;
      stats_.parameter_allocation_bytes += allocation.size();
    }
    if (allocation.maybe_live_out()) {
      stats_.maybe_live_out_allocation_count++;
      stats_.maybe_live_out_allocation_bytes += allocation.size();
    }
    if (allocation.IsPreallocatedTempBuffer()) {
      stats_.preallocated_temp_allocation_count++;
      stats_.preallocated_temp_allocation_bytes += allocation.size();
    }
    stats_.total_allocation_count++;
    stats_.total_allocation_bytes += allocation.size();
  }

  // Only compute total fragmentation if all computations are sequential.
  SequentialHloOrdering::HloModuleSequence module_sequence;
  for (const auto& computation : module_->computations()) {
    const std::vector<const HloInstruction*>* sequence =
        liveness_->hlo_ordering().SequentialOrder(*computation);
    if (sequence != nullptr) {
      module_sequence.emplace(computation.get(), *sequence);
    }
  }
  if (module_sequence.size() == module_->computations().size()) {
    TF_ASSIGN_OR_RETURN(const int64 min_size,
                        MinimumMemoryForSequence(module_sequence, buffer_size));
    stats_.total_fragmentation_bytes = stats_.total_allocation_bytes - min_size;
  }

  return Status::OK();
}

string BufferAssignment::Stats::ToString() const {
  string s;
  Appendf(&s, "BufferAssignment stats:\n");
  Appendf(&s, "             parameter allocation: %10s\n",
          HumanReadableNumBytes(parameter_allocation_bytes).c_str());
  Appendf(&s, "        maybe_live_out allocation: %10s\n",
          HumanReadableNumBytes(maybe_live_out_allocation_bytes).c_str());
  Appendf(&s, "     preallocated temp allocation: %10s\n",
          HumanReadableNumBytes(preallocated_temp_allocation_bytes).c_str());
  if (preallocated_temp_fragmentation_bytes >= 0) {
    const double percent = 100. * preallocated_temp_fragmentation_bytes /
                           preallocated_temp_allocation_bytes;
    Appendf(
        &s, "  preallocated temp fragmentation: %10s (%.2f%%)\n",
        HumanReadableNumBytes(preallocated_temp_fragmentation_bytes).c_str(),
        percent);
  }
  Appendf(&s, "                 total allocation: %10s\n",
          HumanReadableNumBytes(total_allocation_bytes).c_str());
  if (total_fragmentation_bytes >= 0) {
    const double percent =
        100. * total_fragmentation_bytes / total_allocation_bytes;
    Appendf(&s, "              total fragmentation: %10s (%.2f%%)\n",
            HumanReadableNumBytes(total_fragmentation_bytes).c_str(), percent);
  }
  return s;
}

string BufferAssignment::ToString() const {
  string output;
  tensorflow::strings::StrAppend(&output, "BufferAssignment:\n");
  for (auto& allocation : allocations_) {
    tensorflow::strings::StrAppend(&output, allocation.ToString());
  }
  return output;
}

namespace {

// Walk the call graph of the HLO module and place each computation into either
// thread_local_computations or global_computations depending upon whether the
// computation requires thread-local allocations or global allocations. The
// elements in thread_local_computations and global_computations are in post
// order (if computation A has an instruction which calls computation B, then A
// will appear after B in the vector).
Status GatherComputationsByAllocationType(
    const HloModule* module,
    std::vector<const HloComputation*>* thread_local_computations,
    std::vector<const HloComputation*>* global_computations) {
  // Create a worklist of computations paired with whether the allocation must
  // be thread-local.
  std::deque<std::pair<HloComputation*, bool>> worklist;
  worklist.push_back(std::make_pair(module->entry_computation(),
                                    /*is_thread_local*/ false));

  // Sets for quickly checking membership. Computations are returned in vectors
  // for stable iteration.
  FlatSet<HloComputation*> thread_local_set;
  FlatSet<HloComputation*> global_set;

  while (!worklist.empty()) {
    auto worklist_front = worklist.front();
    worklist.pop_front();
    HloComputation* computation = worklist_front.first;
    bool is_thread_local = worklist_front.second;
    bool in_thread_local_set = thread_local_set.count(computation) > 0;
    bool in_global_set = global_set.count(computation) > 0;

    // If the computation has already been added to the respective set, then
    // nothing to do.
    if ((is_thread_local && in_thread_local_set) ||
        (!is_thread_local && in_global_set)) {
      continue;
    }

    // If the computation has already been added to the other set this is an
    // error condition because the global call to the computation (eg,
    // while/call) may return a reference to one of the thread-local buffers to
    // the calling computation which will become a dangling reference when the
    // thread-local is deallocated with the call return.
    if ((is_thread_local && in_global_set) ||
        (!is_thread_local && in_thread_local_set)) {
      return InvalidArgument(
          "computation %s has conflicting allocation requirements (global "
          "and thread-local)",
          computation->name().c_str());
    }

    if (is_thread_local) {
      thread_local_set.insert(computation);
    } else {
      global_set.insert(computation);
    }

    for (auto& instruction : computation->instructions()) {
      for (HloComputation* subcomputation :
           instruction->called_computations()) {
        switch (instruction->opcode()) {
          case HloOpcode::kCall:
          case HloOpcode::kWhile:
            // Call and while must be called from a computation with global
            // allocations as they may return references to buffers inside the
            // called computation which cannot be thread-local.
            if (is_thread_local) {
              return InvalidArgument(
                  "computation %s cannot contain call/while op because it "
                  "requires thread-local buffer allocations",
                  computation->name().c_str());
            }
            worklist.push_back(std::make_pair(subcomputation,
                                              false));  // Not thread local.
            break;
          case HloOpcode::kMap:
          case HloOpcode::kReduce:
          case HloOpcode::kReduceWindow:
          case HloOpcode::kSelectAndScatter:
          case HloOpcode::kFusion:
            // Map/reduce etc computations are always thread-local.
            worklist.push_back(std::make_pair(subcomputation,
                                              true));  // Thread local.
            break;
          default:
            return InternalError(
                "Unexpected calling opcode: %s",
                HloOpcodeString(instruction->opcode()).c_str());
        }
      }
    }
  }

  // Add the computations to the vectors in post order.
  for (auto* computation : module->MakeComputationPostOrder()) {
    if (thread_local_set.count(computation) > 0) {
      thread_local_computations->push_back(computation);
    } else if (global_set.count(computation) > 0) {
      global_computations->push_back(computation);
    }
    // If the computation is not reachable from the entry computation, then it
    // will not appear in either thread_local_set or global_set. We don't bother
    // assigning buffers for these.
  }
  return Status::OK();
}

}  // namespace

/* static */
StatusOr<std::unique_ptr<BufferAssignment>> BufferAssigner::Run(
    const HloModule* module, std::unique_ptr<HloOrdering> hlo_ordering,
    LogicalBuffer::SizeFunction buffer_size, int64 alignment) {
  BufferAssigner assigner(std::move(buffer_size), alignment);
  return assigner.CreateAssignment(module, std::move(hlo_ordering));
}

bool BufferAssigner::MaybeAssignBuffer(BufferAllocation* allocation,
                                       const LogicalBuffer& buffer,
                                       BufferAssignment* assignment) {
  CHECK(!assignment->HasAllocation(buffer))
      << "buffer " << buffer << " already has an allocation assigned.";

  VLOG(4) << "Trying to assign " << buffer << " to allocation: " << *allocation;

  if (buffer_size_(buffer) > allocation->size()) {
    VLOG(4) << "Can't assign: buffer is larger than allocation ("
            << buffer_size_(buffer) << " > " << allocation->size() << ")";
    return false;
  }

  if (allocation->is_entry_computation_parameter()) {
    VLOG(4) << "Can't assign: allocation holds parameter";
    return false;
  }

  if (!allocation->is_reusable()) {
    VLOG(4) << "Can't assign: allocation is not reusable";
    return false;
  }

  for (const auto& buffer_offset_size : allocation->assigned_buffers()) {
    const LogicalBuffer& assigned_buffer = *buffer_offset_size.first;
    if (assignment->liveness().MayInterfere(assigned_buffer, buffer)) {
      VLOG(4) << "Can't assign: assignee " << assigned_buffer
              << " may interfere with " << buffer;
      return false;
    }
  }

  // If the buffer is live out of the computation then it should only be
  // assigned a buffer which exactly fits the result to avoid wasting memory
  // (result buffers can have arbitrary lifetimes).
  if (assignment->liveness().MaybeLiveOut(buffer) &&
      allocation->size() != buffer_size_(buffer)) {
    VLOG(4) << "Can't assign: buffer " << buffer
            << "is live out and size not the same as allocation";
    return false;
  }

  assignment->AddAssignment(allocation, buffer, /*offset=*/0,
                            buffer_size_(buffer));
  return true;
}

Status BufferAssigner::AssignBuffersForComputation(
    const HloComputation* computation, bool is_thread_local,
    const FlatSet<const LogicalBuffer*>& colocated_buffers,
    const FlatSet<BufferAllocation::Index>& colocated_allocations,
    FlatMap<const HloComputation*, FlatSet<const LogicalBuffer*>>*
        buffers_to_assign_sequentially,
    BufferAssignment* assignment) {
  // Buffers are sorted and assigned to BufferAllocations in decreasing order of
  // size.
  std::vector<const LogicalBuffer*> sorted_buffers;
  for (auto& instruction : computation->instructions()) {
    // Add all buffers which this instruction defines. Instruction which don't
    // define buffers (eg, bitcast which just forwards a pointer) don't need
    // any allocations.
    for (const LogicalBuffer* buffer :
         assignment->points_to_analysis().GetBuffersDefinedByInstruction(
             instruction.get())) {
      sorted_buffers.push_back(buffer);
    }
  }

  // Generate a post order sort of instructions for sorting of the
  // LogicalBuffers.
  FlatMap<const HloInstruction*, int> post_order_position;
  int position = 0;
  for (auto* instruction : computation->MakeInstructionPostOrder()) {
    post_order_position.emplace(instruction, position);
    position++;
  }

  // If there is a sequential instruction ordering, we'll delay assignment of
  // temp buffers until after the main assignment loop.
  const BufferLiveness& liveness = assignment->liveness();
  const bool has_sequential_order =
      liveness.hlo_ordering().SequentialOrder(*computation) != nullptr;
  if (has_sequential_order && buffers_to_assign_sequentially != nullptr) {
    // Every sequential computation must get an entry in the
    // buffers_to_assign_sequentially map, even if we end up with an empty set
    // of buffers. This ensures we can correctly determine whether to run
    // whole-module heap simulation.
    buffers_to_assign_sequentially->emplace(computation,
                                            FlatSet<const LogicalBuffer*>());
  }

  // Sort the LogicalBuffers first by size. We assign the larger LogicalBuffers
  // first for simplicity. This means any previously created BufferAllocation is
  // necessarily large enough to hold the output of the current Buffer in
  // consideration.
  //
  // As a secondary sorting criteria, if the instructions are sequentially
  // ordered, we assign live-out buffers before others. Note that for sequential
  // computations, we'll take temp buffers that can't re-use any allocations and
  // assign them via a heap scheduler. By assigning live-out buffers first, we
  // increase the odds that temp buffers can re-use an allocation.
  //
  // As a final tiebreaker use post order position of the HLO instruction which
  // defines the buffer. This means an instruction will appear after its
  // operands (assuming operands are the same/larger size) enabling the
  // important reuse case where an elementwise instruction reuses one of its
  // operand's buffer. This improves locality.
  std::sort(sorted_buffers.begin(), sorted_buffers.end(),
            [this, has_sequential_order, &liveness, &post_order_position](
                const LogicalBuffer* a, const LogicalBuffer* b) {
              // Primary sort is by decreasing buffer size.
              const int64 a_size = buffer_size_(*a);
              const int64 b_size = buffer_size_(*b);
              if (a_size != b_size) {
                return a_size > b_size;  // use ">" for decreasing size.
              }
              // Otherwise live out buffers come before others, if the
              // instructions are sequentially ordered.
              if (has_sequential_order) {
                const bool a_live_out = liveness.MaybeLiveOut(*a);
                const bool b_live_out = liveness.MaybeLiveOut(*b);
                if (a_live_out != b_live_out) {
                  return a_live_out;
                }
              }
              // Final tiebreaker is in instruction post order.
              return post_order_position.at(a->instruction()) <
                     post_order_position.at(b->instruction());
            });

  // BufferAllocations are necessarily created in decreasing size order. Keep
  // indices of previously created BufferAllocations in allocation_indices.
  std::vector<BufferAllocation::Index> allocation_indices;
  for (const LogicalBuffer* buffer : sorted_buffers) {
    VLOG(3) << "Assigning allocation to: " << *buffer;
    if (colocated_buffers.count(buffer) > 0) {
      // Colocated buffers are currently assigned in an earlier pass.
      VLOG(3) << "Skipping colocated buffer: " << *buffer;
      continue;
    }

    TF_RET_CHECK(!assignment->HasAllocation(*buffer));

    const HloInstruction* instruction = buffer->instruction();
    if (instruction->opcode() == HloOpcode::kConstant) {
      // No BufferAllocations for constants.
      // TODO(b/32248867): For consistency, constants should get allocations.
      VLOG(3) << "Skipping constant: " << *buffer;
      continue;
    }

    const int64 buffer_size = buffer_size_(*buffer);

    const bool is_entry_parameter =
        instruction->opcode() == HloOpcode::kParameter &&
        computation == computation->parent()->entry_computation();
    if (is_entry_parameter) {
      // If the LogicalBuffer is part of an external parameter, creates a new
      // allocation and sets its parameter number. Parameters of non-entry
      // computations do not need special allocations because they live inside
      // callers.
      BufferAllocation* allocation =
          assignment->NewAllocation(*buffer, buffer_size,
                                    /*is_thread_local=*/false,
                                    /*is_reusable=*/false);
      allocation->set_entry_computation_parameter(
          instruction->parameter_number());
      VLOG(3) << "New allocation #" << allocation->index()
              << " for entry computation parameter: " << *buffer;
      continue;
    }

    legacy_flags::BufferAssignmentFlags* flags =
        legacy_flags::GetBufferAssignmentFlags();
    if (!flags->xla_enable_buffer_reuse || is_thread_local ||
        instruction->opcode() == HloOpcode::kCustomCall) {
      // Custom call operations never have reusable buffers. Also we do not
      // reuse thread-local buffers for now, because they are dynamically
      // allocated and their lifetimes are hard to compute.
      BufferAllocation* allocation = assignment->NewAllocation(
          *buffer, buffer_size, is_thread_local, /*is_reusable=*/false);
      VLOG(3) << "New allocation #" << allocation->index()
              << " for thread-local/CustomCall: " << *buffer;
      continue;
    }

    if (ShapeUtil::IsTuple(buffer->shape())) {
      // TODO(b/34669761): Don't reuse tuple buffers because the GPU backend
      // assumes longer buffer liveness than indicated by the analysis.
      BufferAllocation* allocation = assignment->NewAllocation(
          *buffer, buffer_size, is_thread_local, /*is_reusable=*/false);
      VLOG(3) << "New allocation #" << allocation->index()
              << " for tuple-shaped buffer: " << *buffer;
      continue;
    }

    // First try to assign a LogicalBuffer to one of its operand allocations to
    // improve locality. This is only possible with elementwise operations
    // (checked in liveness analysis) which are necessarily top-level
    // array-shaped buffers.
    if (buffer->IsTopLevel() && !buffer->IsTuple()) {
      for (auto* operand : instruction->operands()) {
        bool assigned_operand = false;
        for (const auto& operand_slice :
             assignment->GetAllSlices(operand, /*index=*/{})) {
          BufferAllocation* allocation =
              assignment->GetMutableAllocation(operand_slice.index());
          if (colocated_allocations.count(allocation->index()) == 0) {
            // TODO(b/32491382) Colocated buffers are currently assigned in an
            // earlier pass, and so can break the "increasing allocation size"
            // invariant in this function (causing this CHECK to fail). However,
            // the call to MaybeAssignBuffer is safe as it returns false if
            // allocation.size < buffer.size.
            CHECK_GE(allocation->size(), buffer_size);
          }
          if (MaybeAssignBuffer(allocation, *buffer, assignment)) {
            VLOG(3) << "Reusing (operand) allocation #" << allocation->index()
                    << " for: " << *buffer;
            assigned_operand = true;
            break;
          }
        }
        if (assigned_operand) {
          break;
        }
      }
    }

    if (!assignment->HasAllocation(*buffer)) {
      // Find the smallest buffer which can be reused iterating from end of
      // allocation_indices (smallest) to beginning (largest).
      for (int allocation_index = allocation_indices.size() - 1;
           allocation_index >= 0; allocation_index--) {
        BufferAllocation* allocation = assignment->GetMutableAllocation(
            allocation_indices[allocation_index]);
        // Instructions are iterated in increasing buffer size, so any
        // previously create allocation must be large enough to hold this
        // instruction's output (with the exception of colocated buffers).
        if (colocated_allocations.count(allocation->index()) == 0) {
          // TODO(b/32491382) Colocated buffers are currently assigned in an
          // earlier pass, and so can break the "increasing allocation size"
          // invariant in this function (causing this CHECK to fail). However,
          // the call to MaybeAssignBuffer is safe as it returns false if
          // allocation.size < buffer.size.
          CHECK_GE(allocation->size(), buffer_size);
        }

        if (MaybeAssignBuffer(allocation, *buffer, assignment)) {
          VLOG(3) << "Reusing allocation #" << allocation->index()
                  << " for: " << *buffer;
          break;
        }
      }
    }

    if (!assignment->HasAllocation(*buffer) && has_sequential_order &&
        !liveness.MaybeLiveOut(*buffer)) {
      // There is a sequential instruction ordering, so we delay assignment of
      // temp buffers until after the loop. We do this right before we decide to
      // create a new allocation, to ensure we've exhausted all the buffer
      // re-use cases above.
      //
      // Entry parameters and thread local buffers were already handled earlier
      // in this loop iteration.  See BufferAllocation::IsPreallocatedTempBuffer
      // for the definition of temp buffers.
      CHECK(!is_entry_parameter) << *buffer;
      CHECK(!is_thread_local) << *buffer;
      (*buffers_to_assign_sequentially)[computation].insert(buffer);
      VLOG(3) << "Delaying assignment of temp buffer: " << *buffer;
      continue;
    }

    if (!assignment->HasAllocation(*buffer)) {
      BufferAllocation* allocation = assignment->NewAllocation(
          *buffer, buffer_size, is_thread_local, /*is_reusable=*/true);
      allocation_indices.push_back(allocation->index());
      VLOG(3) << "New allocation #" << allocation->index()
              << " for: " << *buffer;
    }
  }

  return Status::OK();
}

Status BufferAssigner::AssignBuffersWithSequentialOrdering(
    const FlatMap<const HloComputation*, FlatSet<const LogicalBuffer*>>&
        buffers_to_assign_sequentially,
    bool run_whole_module_heap_simulation, BufferAssignment* assignment) {
  // Run the sequence of instructions through the heap simulator.  The heuristic
  // that seems to give the best results is lazy-best-fit, with all runs of
  // alloc / free calls sorted in decreasing size order.
  const HloOrdering& hlo_ordering = assignment->liveness().hlo_ordering();
  if (run_whole_module_heap_simulation) {
    // Run the heap simulation over the whole module. This reduces memory usage,
    // since buffers for kCall and kWhile sub-computations are only live for the
    // duration of their calling instructions.
    VLOG(1) << "Running whole-module heap simulation";
    SequentialHloOrdering::HloModuleSequence module_sequence;
    FlatSet<const LogicalBuffer*> all_buffers_to_assign;
    for (const auto& pair : buffers_to_assign_sequentially) {
      const HloComputation* computation = pair.first;
      const FlatSet<const LogicalBuffer*>& buffers_to_assign = pair.second;
      const std::vector<const HloInstruction*>* instruction_sequence =
          hlo_ordering.SequentialOrder(*computation);
      CHECK(instruction_sequence != nullptr) << computation->name();
      module_sequence[computation] = *instruction_sequence;
      all_buffers_to_assign.insert(buffers_to_assign.begin(),
                                   buffers_to_assign.end());
    }
    TF_ASSIGN_OR_RETURN(
        const HeapSimulator::Result result,
        HeapSimulator::Run(MakeUnique<DecreasingSizeRunsHeap>(
                               MakeUnique<LazyBestFitHeap>(alignment_)),
                           assignment->module(), module_sequence,
                           assignment->points_to_analysis(), buffer_size_,
                           &all_buffers_to_assign));
    AssignBuffersFromHeapSimulator(result, assignment);
  } else {
    // Run the heap-simulation on a per-computation basis. Buffers for
    // sub-computations are assigned disjoint BufferAllocations, assuming the
    // worst-case that they may all be live concurrently.
    VLOG(1) << "Running per-computation heap simulation";
    for (const auto& pair : buffers_to_assign_sequentially) {
      const HloComputation* computation = pair.first;
      const FlatSet<const LogicalBuffer*>& buffers_to_assign = pair.second;
      const std::vector<const HloInstruction*>* instruction_sequence =
          hlo_ordering.SequentialOrder(*computation);
      CHECK(instruction_sequence != nullptr) << computation->name();
      TF_ASSIGN_OR_RETURN(
          const HeapSimulator::Result result,
          HeapSimulator::Run(MakeUnique<DecreasingSizeRunsHeap>(
                                 MakeUnique<LazyBestFitHeap>(alignment_)),
                             *computation, *instruction_sequence,
                             assignment->points_to_analysis(), buffer_size_,
                             &buffers_to_assign));
      AssignBuffersFromHeapSimulator(result, assignment);
    }
  }
  return Status::OK();
}

void BufferAssigner::AssignBuffersFromHeapSimulator(
    const HeapSimulator::Result& result, BufferAssignment* assignment) {
  if (assignment->stats_.preallocated_temp_fragmentation_bytes == -1) {
    assignment->stats_.preallocated_temp_fragmentation_bytes =
        result.fragmentation_size;
  } else {
    assignment->stats_.preallocated_temp_fragmentation_bytes +=
        result.fragmentation_size;
  }

  BufferAllocation* allocation = assignment->NewEmptyAllocation(
      result.heap_size, /*is_thread_local=*/false, /*is_reusable=*/true);
  for (const auto& buffer_chunk : result.chunk_map) {
    const LogicalBuffer& buffer = *buffer_chunk.first;
    const HeapSimulator::Chunk& chunk = buffer_chunk.second;
    assignment->AddAssignment(allocation, buffer, chunk.offset, chunk.size);
  }
}

// Adds the 'colocated_set' of buffers to 'colocated_buffer_sets', maintaining
// the invariant that all sets in 'colocated_buffer_sets' are disjoint.
//
// A practical example of when this is necessary is a chain of kCall ops:
//   computation.entry
//     %a = call() -> computation.1
//   computation.1
//     %b = call() -> computation.2
//   computation.2
//     %c = parameter()
// This yields the logical sets {%a,%b} {%b,%c} {%c}, which need to be merged
// into a single set {%a,%b,%c}
void BufferAssigner::AddSetToColocatedBufferSets(
    const std::vector<const LogicalBuffer*>& colocated_set,
    std::vector<ColocatedBufferSet>* colocated_buffer_sets) {
  if (colocated_set.empty()) {
    return;
  }

  // Find existing sets that overlap with at least one buffer from the
  // colocated_set. The resulting 'overlap_set_indices' will have at most
  // colocated_buffer_sets->size() entries, and will be in increasing order.
  std::vector<size_t> overlap_set_indices;
  for (size_t index = 0; index < colocated_buffer_sets->size(); ++index) {
    for (const LogicalBuffer* buffer : colocated_set) {
      if ((*colocated_buffer_sets)[index].count(buffer) > 0) {
        overlap_set_indices.push_back(index);
        break;
      }
    }
  }

  // If there is no overlap with existing sets, create a new set.
  if (overlap_set_indices.empty()) {
    colocated_buffer_sets->emplace_back();
    colocated_buffer_sets->back().insert(colocated_set.begin(),
                                         colocated_set.end());
    return;
  }

  // Merge all overlap sets and the colocated set into the first overlap set.
  ColocatedBufferSet* first = &(*colocated_buffer_sets)[overlap_set_indices[0]];
  for (size_t index = 1; index < overlap_set_indices.size(); ++index) {
    const ColocatedBufferSet& overlap_set =
        (*colocated_buffer_sets)[overlap_set_indices[index]];
    first->insert(overlap_set.begin(), overlap_set.end());
  }
  first->insert(colocated_set.begin(), colocated_set.end());

  // Remove overlap sets that we just merged. The offset accounts for the fact
  // that as elements are erased, the indices need to be adjusted. Keep in mind
  // that overlap_set_indices is in increasing order.
  for (size_t index = 1; index < overlap_set_indices.size(); ++index) {
    const size_t offset = overlap_set_indices[index] - index + 1;
    colocated_buffer_sets->erase(colocated_buffer_sets->begin() + offset);
  }
}

// Conceptually the same as AddSetToColocatedBufferSets, but specific to the
// colocated buffers for while instructions. 'colocated_set' contains the
// buffers for a single while instruction that must be colocated. The idea here
// is to apply a memory-saving heuristic for separate while instructions whose
// buffers are disjoint in liveness, by using the colocation mechanism to force
// buffer sharing. This often reduces memory for multi-layer RNNs.
//
// TODO(b/32491382): We should be able to remove this heuristic after we
// implement module-level liveness analysis, which would let us directly detect
// buffer sharing opportunities between the while instruction buffer and the
// buffers from the predicate and body computation, as well as sharing across
// different while instructions.
void BufferAssigner::AddWhileSetToColocatedBufferSets(
    const std::vector<const LogicalBuffer*>& colocated_set,
    const LogicalBuffer* while_init_buffer, const HloInstruction* while_hlo,
    const HloComputation& computation, const BufferLiveness& buffer_liveness,
    std::vector<ColocatedBufferSet>* colocated_buffer_sets) {
  CHECK(!colocated_set.empty());
  const TuplePointsToAnalysis& points_to_analysis =
      buffer_liveness.points_to_analysis();

  // Parallel while loops cannot safely share colocated buffer sets.
  if (buffer_liveness.hlo_ordering().SequentialOrder(computation) == nullptr) {
    AddSetToColocatedBufferSets(colocated_set, colocated_buffer_sets);
    return;
  }

  // Scan 'colocated_buffer_sets' in reverse order for locality; colocated sets
  // are added in postorder over computations and instructions.
  const int64 init_buffer_size = buffer_size_(*while_init_buffer);
  for (int i = colocated_buffer_sets->size() - 1; i >= 0; --i) {
    const ColocatedBufferSet& predecessor_set = (*colocated_buffer_sets)[i];

    // Skip predecessor sets not associated with while loops.
    if (std::all_of(predecessor_set.begin(), predecessor_set.end(),
                    [](const LogicalBuffer* buffer) {
                      return buffer->instruction()->opcode() !=
                             HloOpcode::kWhile;
                    })) {
      continue;
    }

    // Skip predecessor sets already associated with 'while_hlo'.
    if (std::any_of(predecessor_set.begin(), predecessor_set.end(),
                    [&while_hlo](const LogicalBuffer* buffer) {
                      return buffer->instruction() == while_hlo;
                    })) {
      continue;
    }

    // Build vector of predecessor while result and init buffers, which are
    // checked for liveness interference below. We must check both the result
    // and init buffers because they're aliased together, but
    // TuplePointsToAnalysis is unaware of this aliasing.
    std::vector<const LogicalBuffer*> predecessor_while_buffers;
    for (const LogicalBuffer* buffer : predecessor_set) {
      const HloInstruction* instruction = buffer->instruction();
      if (instruction->opcode() == HloOpcode::kWhile &&
          buffer_size_(*buffer) == init_buffer_size &&
          instruction->parent() == &computation) {
        predecessor_while_buffers.push_back(buffer);
        // Add the init buffer at the same index, which must also exist in the
        // predecessor set, and must be unambiguous.
        const PointsToSet& init_points_to =
            points_to_analysis.GetPointsToSet(instruction->operand(0));
        const std::vector<const LogicalBuffer*>& init_buffers =
            init_points_to.element(buffer->index());
        CHECK_EQ(init_buffers.size(), 1);
        CHECK_GT(predecessor_set.count(init_buffers[0]), 0);
        predecessor_while_buffers.push_back(init_buffers[0]);
      }
    }
    if (predecessor_while_buffers.empty()) {
      continue;
    }

    // Skip predecessor set if the live range of any predecessor buffers
    // overlaps with 'while_init_buffer'. Note that tuple element buffer
    // forwarding can cause the same buffer to appear on both sides of the
    // interference comparison below.
    if (std::any_of(
            predecessor_while_buffers.begin(), predecessor_while_buffers.end(),
            [while_init_buffer, &buffer_liveness](const LogicalBuffer* buffer) {
              return while_init_buffer->id() != buffer->id() &&
                     buffer_liveness.MayInterfere(*while_init_buffer, *buffer);
            })) {
      continue;
    }

    // All our checks have passed; merge 'predecessor_set' with 'colocated_set',
    // and add the merged set to 'colocated_buffer_sets'. This forces the
    // colocation of buffers across different while instructions.
    FlatSet<const LogicalBuffer*> unique;
    unique.insert(predecessor_set.begin(), predecessor_set.end());
    unique.insert(colocated_set.begin(), colocated_set.end());
    std::vector<const LogicalBuffer*> merged_set(unique.begin(), unique.end());
    AddSetToColocatedBufferSets(merged_set, colocated_buffer_sets);
    return;
  }

  // Failed to merge into predecessor set; add 'colocated_set' as-is.
  AddSetToColocatedBufferSets(colocated_set, colocated_buffer_sets);
}

namespace {

// Checks that points-to set of 'instruction' is unambiguous and distinct
// (ensured by CopyInsertion), then adds the buffer from the points-to set at
// 'index' to 'colocated_set'.
const LogicalBuffer* AddBufferToColocatedSet(
    const HloInstruction* instruction, const ShapeIndex& index,
    const TuplePointsToAnalysis& points_to_analysis,
    std::vector<const LogicalBuffer*>* colocated_set) {
  // CopyInsertion ensures root points-to set is unambiguous and distinct.
  const auto& points_to = points_to_analysis.GetPointsToSet(instruction);
  CHECK(!points_to.IsAmbiguous());
  CHECK(points_to.IsDistinct());
  colocated_set->push_back(points_to.element(index)[0]);
  return colocated_set->back();
}

}  // namespace

// Builds sets of buffers in 'colocated_buffer_sets' which should be colocated
// in the same allocation (currently just supports kWhile and kCall).
void BufferAssigner::BuildColocatedBufferSets(
    const HloModule* module, const BufferLiveness& buffer_liveness,
    std::vector<ColocatedBufferSet>* colocated_buffer_sets) {
  const TuplePointsToAnalysis& points_to_analysis =
      buffer_liveness.points_to_analysis();
  for (const HloComputation* computation : module->MakeComputationPostOrder()) {
    for (const HloInstruction* instruction :
         computation->MakeInstructionPostOrder()) {
      const HloOpcode opcode = instruction->opcode();
      if (opcode == HloOpcode::kWhile) {
        const HloInstruction* while_hlo = instruction;
        TF_CHECK_OK(ShapeUtil::ForEachSubshape(
            while_hlo->shape(),
            [this, while_hlo, &points_to_analysis, &buffer_liveness,
             computation, colocated_buffer_sets](const Shape& /*subshape*/,
                                                 const ShapeIndex& index) {
              std::vector<const LogicalBuffer*> colocated_set;
              // Add while.init.
              auto* init_buffer =
                  AddBufferToColocatedSet(while_hlo->operand(0), index,
                                          points_to_analysis, &colocated_set);
              // Add while.result.
              AddBufferToColocatedSet(while_hlo, index, points_to_analysis,
                                      &colocated_set);
              // Add while.cond.parameter.
              AddBufferToColocatedSet(
                  while_hlo->while_condition()->parameter_instruction(0), index,
                  points_to_analysis, &colocated_set);
              // Add while.body.parameter.
              AddBufferToColocatedSet(
                  while_hlo->while_body()->parameter_instruction(0), index,
                  points_to_analysis, &colocated_set);
              // Add while.body.root.
              AddBufferToColocatedSet(
                  while_hlo->while_body()->root_instruction(), index,
                  points_to_analysis, &colocated_set);
              AddWhileSetToColocatedBufferSets(
                  colocated_set, init_buffer, while_hlo, *computation,
                  buffer_liveness, colocated_buffer_sets);
              return Status::OK();
            }));
      } else if (opcode == HloOpcode::kCall) {
        const HloInstruction* call_hlo = instruction;
        const HloInstruction* root_hlo =
            call_hlo->to_apply()->root_instruction();
        TF_CHECK_OK(ShapeUtil::ForEachSubshape(
            call_hlo->shape(),
            [this, call_hlo, root_hlo, &points_to_analysis,
             colocated_buffer_sets](const Shape& /*subshape*/,
                                    const ShapeIndex& index) {
              std::vector<const LogicalBuffer*> colocated_set;
              // Add call.result.
              AddBufferToColocatedSet(call_hlo, index, points_to_analysis,
                                      &colocated_set);
              // Add call.subcomputation.root.
              AddBufferToColocatedSet(root_hlo, index, points_to_analysis,
                                      &colocated_set);
              AddSetToColocatedBufferSets(colocated_set, colocated_buffer_sets);
              return Status::OK();
            }));
      }
    }
  }
}

// Assigns all colocated buffer sets in 'colocated_buffer_sets' to the same
// allocation in 'assignment'.
void BufferAssigner::AssignColocatedBufferSets(
    const std::vector<ColocatedBufferSet>& colocated_buffer_sets,
    BufferAssignment* assignment,
    FlatSet<const LogicalBuffer*>* colocated_buffers,
    FlatSet<BufferAllocation::Index>* colocated_allocations) {
  for (const ColocatedBufferSet& colocated_buffer_set : colocated_buffer_sets) {
    BufferAllocation* allocation = nullptr;
    for (const LogicalBuffer* buffer : colocated_buffer_set) {
      if (allocation == nullptr) {
        // TODO(b/32491382) Avoid current trivial solution of using new
        // allocations for each colocated buffer set. When liveness has
        // module-level scope, we can allow buffers to be shared across
        // computations (in some cases).
        allocation = assignment->NewAllocation(*buffer, buffer_size_(*buffer),
                                               /*is_thread_local=*/false,
                                               /*is_reusable=*/true);
        colocated_allocations->insert(allocation->index());
      } else {
        assignment->AddAssignment(allocation, *buffer, /*offset=*/0,
                                  buffer_size_(*buffer));
      }
      colocated_buffers->insert(buffer);

      // Each entry parameter must reside in its own BufferAllocation. As a
      // result, it doesn't make sense for entry parameters to appear in a
      // colocated buffer set, since the only correct scenario would be a
      // degenerate colocated set that only contains the entry parameter.
      const HloInstruction* instruction = buffer->instruction();
      const HloComputation* computation = instruction->parent();
      const bool is_entry_parameter =
          instruction->opcode() == HloOpcode::kParameter &&
          computation == computation->parent()->entry_computation();
      CHECK(!is_entry_parameter)
          << "allocation: " << *allocation << " instruction: " << *buffer << " "
          << instruction->ToString();
    }
  }
}

StatusOr<std::unique_ptr<BufferAssignment>> BufferAssigner::CreateAssignment(
    const HloModule* module, std::unique_ptr<HloOrdering> hlo_ordering) {
  TF_ASSIGN_OR_RETURN(std::unique_ptr<BufferLiveness> liveness,
                      BufferLiveness::Run(module, std::move(hlo_ordering)));

  VLOG(1) << "Assigning buffers to module " << module->name();
  XLA_VLOG_LINES(2, module->ToString());
  XLA_VLOG_LINES(3, liveness->ToString());
  XLA_VLOG_LINES(3, liveness->points_to_analysis().ToString());

  // Can't use MakeUnique because BufferAssignment constructor is private.
  std::unique_ptr<BufferAssignment> assignment(
      new BufferAssignment(module, std::move(liveness), alignment_));

  // Assign buffers with the tightest constraints first (colocated buffer sets).
  // Once b/32491382 enables module-level liveness analysis, we may be able
  // to assign colocated buffers (or at least reuse their allocation for
  // buffers outside of the set) in AssignBuffersForComputation.
  FlatSet<const LogicalBuffer*> colocated_buffers;
  FlatSet<BufferAllocation::Index> colocated_allocations;
  std::vector<ColocatedBufferSet> colocated_buffer_sets;
  BuildColocatedBufferSets(module, assignment->liveness(),
                           &colocated_buffer_sets);
  AssignColocatedBufferSets(colocated_buffer_sets, assignment.get(),
                            &colocated_buffers, &colocated_allocations);

  std::vector<const HloComputation*> thread_local_computations;
  std::vector<const HloComputation*> global_computations;
  TF_RETURN_IF_ERROR(GatherComputationsByAllocationType(
      module, &thread_local_computations, &global_computations));

  // First assign buffers for global computatations. Temporary buffers for
  // sequential computations are collected in 'buffers_to_assign_sequentially'.
  FlatMap<const HloComputation*, FlatSet<const LogicalBuffer*>>
      buffers_to_assign_sequentially;
  for (auto* computation : global_computations) {
    TF_RETURN_IF_ERROR(AssignBuffersForComputation(
        computation, /*is_thread_local=*/false, colocated_buffers,
        colocated_allocations, &buffers_to_assign_sequentially,
        assignment.get()));
  }
  // Assign buffers with sequential ordering, if any. If all global computations
  // are sequential, we can run heap simuation on the whole module, which
  // reduces memory usage.
  const bool run_whole_module_heap_simulation =
      buffers_to_assign_sequentially.size() == global_computations.size();
  TF_RETURN_IF_ERROR(AssignBuffersWithSequentialOrdering(
      buffers_to_assign_sequentially, run_whole_module_heap_simulation,
      assignment.get()));

  // Now assign buffers for thread-local computations. All LogicalBuffers get
  // their own BufferAllocation.
  for (auto* computation : thread_local_computations) {
    TF_RET_CHECK(computation != module->entry_computation());
    TF_RETURN_IF_ERROR(AssignBuffersForComputation(
        computation, /*is_thread_local=*/true, colocated_buffers,
        colocated_allocations, /*buffers_to_assign_sequentially=*/nullptr,
        assignment.get()));
  }

  // Mark all buffers which may be live out of the entry computation as
  // "liveout".
  for (const LogicalBuffer* buffer :
       assignment->liveness().maybe_live_out_buffers()) {
    VLOG(3) << "maybe_live_out LogicalBuffer: " << *buffer;
    if (assignment->HasAllocation(*buffer)) {
      BufferAllocation* alloc =
          assignment->GetMutableAssignedAllocation(*buffer);
      alloc->set_maybe_live_out(true);
      VLOG(3) << "maybe_live_out BufferAllocation: " << *alloc;
    }
  }

  // Combines allocations of temporary buffers into one big BufferAllocation.
  // This can only be performed after all buffers have been assigned, and after
  // maybe_live_out is marked, since it is used to determine whether an
  // allocation contains temporary buffers or not.
  assignment->CombineTempAllocations();

  XLA_VLOG_LINES(2, assignment->ToString());
  TF_RETURN_IF_ERROR(assignment->ComputeSummaryStats(buffer_size_));
  XLA_VLOG_LINES(1, assignment->GetStats().ToString());
  return std::move(assignment);
}

}  // namespace xla
