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

// HLO instructions are in DAG form and represent the computations that the user
// has built up via the XLA service interface. They are ultimately lowered
// in a platform-aware way by traversing the HLO DAG and emitting a lowered
// form; e.g. see DfsHloVisitor.

#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_HLO_INSTRUCTION_H_
#define TENSORFLOW_COMPILER_XLA_SERVICE_HLO_INSTRUCTION_H_

#include <functional>
#include <iosfwd>
#include <list>
#include <memory>
#include <set>
#include <string>
#include <tuple>
#include <unordered_map>
#include <unordered_set>
#include <vector>

#include "tensorflow/compiler/xla/iterator_util.h"
#include "tensorflow/compiler/xla/literal_util.h"
#include "tensorflow/compiler/xla/map_util.h"
#include "tensorflow/compiler/xla/service/dfs_hlo_visitor.h"
#include "tensorflow/compiler/xla/service/hlo.pb.h"
#include "tensorflow/compiler/xla/service/hlo_clone_context.h"
#include "tensorflow/compiler/xla/service/hlo_domain_metadata.h"
#include "tensorflow/compiler/xla/service/hlo_opcode.h"
#include "tensorflow/compiler/xla/service/hlo_sharding.h"
#include "tensorflow/compiler/xla/service/name_uniquer.h"
#include "tensorflow/compiler/xla/types.h"
#include "tensorflow/compiler/xla/xla_data.pb.h"
#include "tensorflow/core/lib/core/status.h"
#include "tensorflow/core/lib/core/stringpiece.h"
#include "tensorflow/core/lib/gtl/array_slice.h"
#include "tensorflow/core/lib/gtl/flatmap.h"
#include "tensorflow/core/lib/gtl/inlined_vector.h"
#include "tensorflow/core/lib/gtl/iterator_range.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/platform/macros.h"
#include "tensorflow/core/platform/protobuf.h"
#include "tensorflow/core/platform/types.h"

namespace xla {

class HloComputation;
class HloModule;

// A bunch of switches that control how the hlo text should be printed.
class HloPrintOptions {
 public:
  enum class PrintSubcomputationMode {
    kOff,         // Do not print anything about subcomputations.
    kNameOnly,    // Only print the name of subcomputations.
    kFullBodies,  // Print the full bodies of subcomputations.
  };

  // Constructs the default print options: don't print large constants, don't
  // compact operands, no indentation.
  HloPrintOptions()
      : print_large_constants_(false),
        print_subcomputation_mode_(PrintSubcomputationMode::kNameOnly),
        print_metadata_(true),
        print_backend_config_(true),
        compact_operands_(false),
        print_operand_shape_(true),
        print_program_shape_(true),
        print_percent_(true),
        canonicalize_instruction_names_(false),
        indent_amount_(0),
        is_in_nested_computation_(false) {}

  static HloPrintOptions ShortParsable() {
    return HloPrintOptions()
        .set_print_large_constants(true)
        .set_print_subcomputation_mode(PrintSubcomputationMode::kNameOnly)
        .set_print_metadata(false)
        .set_print_backend_config(false)
        .set_print_operand_shape(false)
        .set_print_program_shape(false)
        .set_print_percent(false);
  }

  // Options to produce the canonical string representing an isomorphic
  // computation graph.
  static HloPrintOptions Canonical() {
    return HloPrintOptions()
        .set_print_subcomputation_mode(PrintSubcomputationMode::kFullBodies)
        .set_print_metadata(false)
        .set_compact_operands(true)
        .set_print_operand_shape(true)
        .set_print_program_shape(false)
        .set_print_percent(false)
        .set_canonicalize_instruction_names(true);
  }

  // If true, large constants will be printed out.
  HloPrintOptions& set_print_large_constants(bool value) {
    print_large_constants_ = value;
    return *this;
  }

  HloPrintOptions& set_print_subcomputation_mode(
      PrintSubcomputationMode value) {
    print_subcomputation_mode_ = value;
    return *this;
  }

  // If true, metadata will be printed.
  HloPrintOptions& set_print_metadata(bool value) {
    print_metadata_ = value;
    return *this;
  }

  // If true, backend_config will be printed.
  HloPrintOptions& set_print_backend_config(bool value) {
    print_backend_config_ = value;
    return *this;
  }

  // If true, operands' shapes will be printed.
  HloPrintOptions& set_print_operand_shape(bool value) {
    print_operand_shape_ = value;
    return *this;
  }

  // If true, program shape of hlo computations will be printed.
  HloPrintOptions& set_print_program_shape(bool value) {
    print_program_shape_ = value;
    return *this;
  }

  // If true, names will be printed with prefix '%'.
  HloPrintOptions& set_print_percent(bool value) {
    print_percent_ = value;
    return *this;
  }

  // If true, only a part of operands will be printed out, and their names will
  // be omitted (note that in this case the text will not be parsable).
  HloPrintOptions& set_compact_operands(bool value) {
    compact_operands_ = value;
    return *this;
  }

  // If true, canonicalizes instructions' name. Instead of using "%foo.1" as
  // the name of an instruction, we use "%tmp_1", "%tmp_2" etc.
  HloPrintOptions& set_canonicalize_instruction_names(bool value) {
    canonicalize_instruction_names_ = value;
    return *this;
  }

  // The indent of the hlo text block.
  HloPrintOptions& set_indent_amount(int value) {
    indent_amount_ = value;
    return *this;
  }

  // If true, indicates the instruction being printed is inside a nested
  // computation.
  HloPrintOptions& set_is_in_nested_computation(bool value) {
    is_in_nested_computation_ = value;
    return *this;
  }

  bool print_large_constants() const { return print_large_constants_; }
  PrintSubcomputationMode print_subcomputation_mode() const {
    return print_subcomputation_mode_;
  }
  bool print_metadata() const { return print_metadata_; }
  bool print_backend_config() const { return print_metadata_; }
  bool compact_operands() const { return compact_operands_; }
  bool print_operand_shape() const { return print_operand_shape_; }
  bool print_program_shape() const { return print_program_shape_; }
  bool print_percent() const { return print_percent_; }
  bool canonicalize_instruction_names() const {
    return canonicalize_instruction_names_;
  }
  int indent_amount() const { return indent_amount_; }
  int is_in_nested_computation() const { return is_in_nested_computation_; }

 private:
  bool print_large_constants_;
  PrintSubcomputationMode print_subcomputation_mode_;
  bool print_metadata_;
  bool print_backend_config_;
  bool compact_operands_;
  bool print_operand_shape_;
  bool print_program_shape_;
  bool print_percent_;
  bool canonicalize_instruction_names_;
  int indent_amount_;
  bool is_in_nested_computation_;
};

// For canonical string output, we need to have a canonical way to rename
// each instruction and its operands. Each operand is renamed as "tmp_<xxx>",
// where <xxx> is an index starting from 0.
class CanonicalNameMap {
 public:
  CanonicalNameMap() : index(0) {}

  string LookupOrInsert(const string& old_name) {
    auto iter = canonical_name_map.find(old_name);
    if (iter != canonical_name_map.end()) {
      return iter->second;
    }

    string new_name = tensorflow::strings::StrCat("tmp_", index++);
    canonical_name_map[old_name] = new_name;
    return new_name;
  }
  void Clear() {
    canonical_name_map.clear();
    index = 0;
  }

 private:
  int64 index;
  tensorflow::gtl::FlatMap<string, string> canonical_name_map;
};

// HLO instructions are the atomic unit of the high-level compiler's IR.
//
// HloInstructions live inside of an HloComputation, which is analogous to a
// function in other programming languages.  Nodes have no total order within
// their computation.  Instead, they have a partial ordering determined by their
// data and control dependencies.
//
// HLO does not have basic blocks or explicit "branch" instructions.  Instead,
// certain HloInstructions -- namely, kWhile, kConditional, and kCall -- encode
// control flow.  For example, the kConditional HLO executes one of two possible
// computations, depending on the runtime value of a predicate.
//
// HLO is pure (mostly).  It has no concept of mutable state.  Instead, data
// values are produced by one HLO and flow into consumers across dependency
// edges.
class HloInstruction {
 public:
  // A fusion node computes the same value a call to its fusion computation
  // would compute.  However, the choice of fusion kind dictates codegen
  // strategy for the backend.
  //
  // To generate code for a kFusion HloInstruction, most backends do something
  // like the following:
  //
  // 1) Identify the "primary" HloInstruction of the fused computation.
  // 2) Emit code that does the work of the primary node, creating its inputs
  //    and transforming its outputs as specified by the fused computation.
  //
  // In step (2), the code emitted is usually similar to the code that would be
  // emitted for an *unfused* version of the primary node, except that
  //
  //  - when the primary node reads an element of one of its operands, instead
  //    of loading the value from memory, it *computes* the value based on the
  //    contents of the fused computation.
  //  - when the primary node outputs a value, instead of storing it to memory,
  //    it forwards the value to its users, which then perform additional
  //    computations before the value is finally stored to memory at the root of
  //    the fusion node.
  //
  // An HloInstruction's FusionKind helps us find the kFusion instruction's
  // primary node, and can also affect how we generate code in step (2).
  //
  //  - kInput: The primary node is the root of the fused instruction.
  //
  //  - kOutput: The primary node is not the root of the fused instruction.
  //    This fusion kind requires that one operand buffer of the fusion
  //    instruction be able to alias the output buffer.  This constraint is
  //    usually enough to let backends find the primary node unambiguously.
  //
  //  - kLoop: The primary node is the root of the fused computation, but,
  //    unlike in input fusion, we prescribe a specific implementation for
  //    codegen.  Rather than generating code that looks like the code we'd emit
  //    for an unfused version of the primary/root node, we emit code that
  //    generates one element of the root at a time.
  //
  //  - kCustom: Custom category for backend-specific fusions that don't fit
  //    into the above patterns.
  //
  // Not all backends support all fusion kinds, and given a particular fused
  // computation, it's not in general safe to change its fusion kind.  Creation
  // of fusion nodes is always backend-specific.
  //
  // For elementwise ops (e.g. kAdd), most backends would emit a
  // one-element-at-a-time implementation for the unfused version, so loop
  // fusion and input fusion are probably equivalent if the root node is
  // elementwise.  They're not necessarily equivalent e.g. for kReduce, where an
  // implementation might emit something more sophisticated for an unfused or
  // input-fusion reduce, but will emit the naive code that reduces one element
  // at a time for loop fusion with a reduce as the root.
  //
  // Another way to think of loop fusion is that it's equivalent to input
  // fusion, but where the root node is an implicit identity node, whose
  // unfused implementation is "read one element, write one element".
  //
  // TODO(b/79869434): This categorization scheme is not great.  For one thing,
  // input and loop fusion are basically the same thing: There is no reason for
  // the HLO to encode backend-specific decisions about how e.g. a reduce that's
  // the root of a fusion should be lowered.  In addition, this scheme as
  // written doesn't work for multi-output fusion, where the primary node is
  // never actually the root (which is a kTuple instruction that gathers the
  // multiple outputs of the fusion).
  enum class FusionKind {
    kLoop,
    kInput,
    kOutput,
    kCustom,
  };

  ~HloInstruction();

  // Creates an instruction from the given proto. Arguments:
  //
  //   proto: the proto to convert from.
  //   instruction_map: a map from instruction id to HloInstruction*. This map
  //     must contain all operands of the newly constructed instruction.
  //   computation_map: a map from computation id to HloComputation*. This map
  //     must contain all computations which the newly constructed instruction
  //     calls.
  static StatusOr<std::unique_ptr<HloInstruction>> CreateFromProto(
      const HloInstructionProto& proto,
      const tensorflow::gtl::FlatMap<int64, HloInstruction*>& instruction_map,
      const tensorflow::gtl::FlatMap<int64, HloComputation*>& computation_map);

  // Creates a parameter-retrieving instruction.
  static std::unique_ptr<HloInstruction> CreateParameter(int64 parameter_number,
                                                         const Shape& shape,
                                                         const string& name);

  // Creates a literal constant instruction.
  static std::unique_ptr<HloInstruction> CreateConstant(
      std::unique_ptr<Literal> literal);

  // Creates a get tuple element instruction.
  static std::unique_ptr<HloInstruction> CreateGetTupleElement(
      const Shape& shape, HloInstruction* operand, int64 index);

  // Creates a trace instruction that logs the input operand in the computation.
  static std::unique_ptr<HloInstruction> CreateTrace(const string& tag,
                                                     HloInstruction* operand);

  // Creates a random number generation instruction that fills a shape with
  // random numbers from a given distribution.
  static std::unique_ptr<HloInstruction> CreateRng(
      const Shape& shape, RandomDistribution distribution,
      tensorflow::gtl::ArraySlice<HloInstruction*> parameters);

  // Creates a unary instruction (one operand).
  // Precondition: opcode must be a legitimate unary operation.
  static std::unique_ptr<HloInstruction> CreateUnary(const Shape& shape,
                                                     HloOpcode opcode,
                                                     HloInstruction* operand);

  // Creates a binary instruction (two operands).
  // Precondition: opcode must be a legitimate binary operation.
  static std::unique_ptr<HloInstruction> CreateBinary(const Shape& shape,
                                                      HloOpcode opcode,
                                                      HloInstruction* lhs,
                                                      HloInstruction* rhs);

  // Creates a ternary instruction (three operands).
  // Precondition: opcode must be a legitimate ternary operation.
  static std::unique_ptr<HloInstruction> CreateTernary(const Shape& shape,
                                                       HloOpcode opcode,
                                                       HloInstruction* lhs,
                                                       HloInstruction* rhs,
                                                       HloInstruction* ehs);

  // Creates a variadic instruction (variable number of operands).
  // Precondition: opcode must be a legitimate variadic operation.
  static std::unique_ptr<HloInstruction> CreateVariadic(
      const Shape& shape, HloOpcode opcode,
      tensorflow::gtl::ArraySlice<HloInstruction*> operands);

  // Creates a map instruction, where the computation (given by the handle) is
  // applied element-wise to every element in operands (across the operands,
  // at a given index) with the same `static_operands`.
  static std::unique_ptr<HloInstruction> CreateMap(
      const Shape& shape, tensorflow::gtl::ArraySlice<HloInstruction*> operands,
      HloComputation* map_computation,
      tensorflow::gtl::ArraySlice<HloInstruction*> static_operands = {});

  // Creates a convolution op, where rhs is the convolutional filter
  // and window describes how the filter is applied to lhs.
  static std::unique_ptr<HloInstruction> CreateConvolve(
      const Shape& shape, HloInstruction* lhs, HloInstruction* rhs,
      const Window& window,
      const ConvolutionDimensionNumbers& dimension_numbers);

  // Creates an FFT op, of the type indicated by fft_type.
  static std::unique_ptr<HloInstruction> CreateFft(
      const Shape& shape, HloInstruction* operand, FftType fft_type,
      tensorflow::gtl::ArraySlice<int64> fft_length);

  // Creates a dot op with operands 'lhs' and 'rhs' with contracting and batch
  // dimensions specified in 'dimension_numbers'.
  static std::unique_ptr<HloInstruction> CreateDot(
      const Shape& shape, HloInstruction* lhs, HloInstruction* rhs,
      const DotDimensionNumbers& dimension_numbers);

  // Creates a dot op with operands 'lhs' and 'rhs' that contracts dimension 1
  // of the LHS with dimension 0 of the RHS with no batch dimensions.  Both LHS
  // and the RHS must be of rank 2.
  static std::unique_ptr<HloInstruction> CreateCanonicalDot(
      const Shape& shape, HloInstruction* lhs, HloInstruction* rhs);

  // Creates a reduce-precision op, where operand is the data to reduce in
  // precision, and exponent_bits and mantissa_bits describe the precision to
  // reduce it to.
  static std::unique_ptr<HloInstruction> CreateReducePrecision(
      const Shape& shape, HloInstruction* operand, const int exponent_bits,
      const int mantissa_bits);

  // Creates a cross replica sum op.
  static std::unique_ptr<HloInstruction> CreateCrossReplicaSum(
      const Shape& shape,
      tensorflow::gtl::ArraySlice<HloInstruction*> operands);

  // Creates a conversion instruction, where operand is the data to convert and
  // shape is the target shape for the conversion.
  static std::unique_ptr<HloInstruction> CreateConvert(const Shape& shape,
                                                       HloInstruction* operand);

  // Creates a bitcast conversion instruction, where operand is the data to
  // convert and shape is the target shape for the conversion.
  static std::unique_ptr<HloInstruction> CreateBitcastConvert(
      const Shape& shape, HloInstruction* operand);

  // Creates an infeed instruction, which reads data of the given shape from the
  // Infeed interface of the device.
  static std::unique_ptr<HloInstruction> CreateInfeed(const Shape& shape,
                                                      const string& config);

  // Creates an outfeed instruction, which outputs data.
  static std::unique_ptr<HloInstruction> CreateOutfeed(
      const Shape& shape, HloInstruction* operand,
      tensorflow::StringPiece outfeed_config);

  // Creates an asynchronous send instruction with the given channel id, which
  // initiates sending the operand data to a unique receive instruction in
  // another computation that has the same channel id.
  static std::unique_ptr<HloInstruction> CreateSend(HloInstruction* operand,
                                                    int64 channel_id);

  // Blocks until data transfer for the Send instruction (operand) is complete.
  // The operand must be kSend.
  static std::unique_ptr<HloInstruction> CreateSendDone(
      HloInstruction* operand);

  // Creates an asynchronous receive instruction with the given channel id,
  // which allocates resources to receive data of the given shape from a unique
  // send instruction in another computation that has the same channel id.
  static std::unique_ptr<HloInstruction> CreateRecv(const Shape& shape,
                                                    int64 channel_id);

  // Blocks until data transfer for the Recv instruction (operand) is complete
  // and returns the receive buffer. The operand must be kRecv.
  static std::unique_ptr<HloInstruction> CreateRecvDone(
      HloInstruction* operand);

  // Creates a slice instruction, where the operand is sliced by the given
  // start/limit indices.
  static std::unique_ptr<HloInstruction> CreateSlice(
      const Shape& shape, HloInstruction* operand,
      tensorflow::gtl::ArraySlice<int64> start_indices,
      tensorflow::gtl::ArraySlice<int64> limit_indices,
      tensorflow::gtl::ArraySlice<int64> strides);

  // Creates a slice instruction, where the first operand is sliced by
  // start indices specified in the second operand, and by size specified in
  // 'slice_sizes'.
  static std::unique_ptr<HloInstruction> CreateDynamicSlice(
      const Shape& shape, HloInstruction* operand,
      HloInstruction* start_indices,
      tensorflow::gtl::ArraySlice<int64> slice_sizes);

  // Creates a dynamic update slice instruction, which updates a slice
  // of 'operand' with 'update' and 'start_indices'.
  static std::unique_ptr<HloInstruction> CreateDynamicUpdateSlice(
      const Shape& shape, HloInstruction* operand, HloInstruction* update,
      HloInstruction* start_indices);

  // Creates a concatenate instruction, where the operands are concatenated on
  // the provided dimension.
  static std::unique_ptr<HloInstruction> CreateConcatenate(
      const Shape& shape, tensorflow::gtl::ArraySlice<HloInstruction*> operands,
      int64 dimension);

  // Creates a reduce instruction, where the computation (given by the handle)
  // is applied successively to every element in operand. That is, if f is the
  // function to apply (which either takes 2 [accumulator, value] or 3
  // [accumulator, index, value] arguments) and init is a reduction operator
  // specified initial value (for example, 0 for addition), then this operation
  // will compute:
  //   f(f(init, [index0], value0), [index1], value1), ...)
  static std::unique_ptr<HloInstruction> CreateReduce(
      const Shape& shape, HloInstruction* operand, HloInstruction* init_value,
      tensorflow::gtl::ArraySlice<int64> dimensions_to_reduce,
      HloComputation* reduce_computation);

  // Creates a reduce-window instruction, where the computation (given
  // by the handle) is applied window-wise at each valid window
  // position in the operand.
  static std::unique_ptr<HloInstruction> CreateReduceWindow(
      const Shape& shape, HloInstruction* operand, HloInstruction* init_value,
      const Window& window, HloComputation* reduce_computation);

  // Creates a batch-norm-training instruction.
  static std::unique_ptr<HloInstruction> CreateBatchNormTraining(
      const Shape& shape, HloInstruction* operand, HloInstruction* scale,
      HloInstruction* offset, float epsilon, int64 feature_index);

  // Creates a batch-norm-inference instruction.
  static std::unique_ptr<HloInstruction> CreateBatchNormInference(
      const Shape& shape, HloInstruction* operand, HloInstruction* scale,
      HloInstruction* offset, HloInstruction* mean, HloInstruction* variance,
      float epsilon, int64 feature_index);

  // Creates a batch-norm-grad instruction.
  static std::unique_ptr<HloInstruction> CreateBatchNormGrad(
      const Shape& shape, HloInstruction* operand, HloInstruction* scale,
      HloInstruction* mean, HloInstruction* variance,
      HloInstruction* grad_output, float epsilon, int64 feature_index);

  // Creates a scatter computation that scatters the `source` array to the
  // selected indices of each window.
  static std::unique_ptr<HloInstruction> CreateSelectAndScatter(
      const Shape& shape, HloInstruction* operand, HloComputation* select,
      const Window& window, HloInstruction* source, HloInstruction* init_value,
      HloComputation* scatter);

  // Creates a broadcast instruction.
  static std::unique_ptr<HloInstruction> CreateBroadcast(
      const Shape& shape, HloInstruction* operand,
      tensorflow::gtl::ArraySlice<int64> broadcast_dimensions);

  // Creates a sequence of instructions that performs an explicit broadcast of
  // the operand to the target shape.
  //
  // Interior HLOs are passed to "adder", but the "root" HLO of the sequence is
  // returned as a unique_ptr for API consistency with other factory methods in
  // this interface.
  //
  // TODO(b/72173833) Ideally HloComputations would always be present, and so
  // the adder being passed by the caller would not be necessary.
  static std::unique_ptr<HloInstruction> CreateBroadcastSequence(
      const Shape& output_shape, HloInstruction* operand,
      const std::function<HloInstruction*(std::unique_ptr<HloInstruction>)>&
          adder);

  // Creates a pad instruction, where the operand is padded on the edges and
  // between the elements with the given padding value.
  static std::unique_ptr<HloInstruction> CreatePad(
      const Shape& shape, HloInstruction* operand,
      HloInstruction* padding_value, const PaddingConfig& padding_config);

  // Creates a reshape instruction, where the operand is flattened row-major
  // order and then reshaped to the given result shape.
  static std::unique_ptr<HloInstruction> CreateReshape(const Shape& shape,
                                                       HloInstruction* operand);

  // Creates a transpose instruction which permutes the operand dimensions.
  static std::unique_ptr<HloInstruction> CreateTranspose(
      const Shape& shape, HloInstruction* operand,
      tensorflow::gtl::ArraySlice<int64> dimensions);

  // Creates a while instruction, given a condition computation, a body
  // computation, and the initial value for the input of the computations. For
  // example, shape: S32, condition: i -> i < 1000, body: i -> i * 2, init: 1
  // corresponds to the C code below.
  // int32 i = 1; int32 result = while(i < 1000) { i = i * 2 }
  static std::unique_ptr<HloInstruction> CreateWhile(const Shape& shape,
                                                     HloComputation* condition,
                                                     HloComputation* body,
                                                     HloInstruction* init);

  static std::unique_ptr<HloInstruction> CreateConditional(
      const Shape& shape, HloInstruction* pred,
      HloInstruction* true_computation_arg, HloComputation* true_computation,
      HloInstruction* false_computation_arg, HloComputation* false_computation);

  static std::unique_ptr<HloInstruction> CreateGather(
      const Shape& shape, HloInstruction* operand,
      HloInstruction* gather_indices,
      const GatherDimensionNumbers& gather_dim_numbers,
      tensorflow::gtl::ArraySlice<int64> window_bounds);

  // Creates a kDomain instruction which delimits an HLO domain which have
  // the provided user and operand side metadata.
  static std::unique_ptr<HloInstruction> CreateDomain(
      const Shape& shape, HloInstruction* operand,
      std::unique_ptr<DomainMetadata> operand_side_metadata,
      std::unique_ptr<DomainMetadata> user_side_metadata);

  // Creates a fusion instruction. A fusion instruction contains one or more
  // fused instructions forming an expression with a single root
  // "fused_root". Additional instructions can be added to the fusion
  // instruction with the method FuseInstruction.
  static std::unique_ptr<HloInstruction> CreateFusion(
      const Shape& shape, FusionKind fusion_kind, HloInstruction* fused_root);

  static std::unique_ptr<HloInstruction> CreateFusion(
      const Shape& shape, FusionKind fusion_kind,
      tensorflow::gtl::ArraySlice<HloInstruction*> operands,
      HloComputation* fusion_computation);

  // Creates a call instruction that applies the given computation on the given
  // operands. "shape" is the resultant shape.
  static std::unique_ptr<HloInstruction> CreateCall(
      const Shape& shape, tensorflow::gtl::ArraySlice<HloInstruction*> operands,
      HloComputation* computation);

  // Creates a custom call instruction that applies the given custom call target
  // to the given operands. "shape" is the resultant shape.
  static std::unique_ptr<HloInstruction> CreateCustomCall(
      const Shape& shape, tensorflow::gtl::ArraySlice<HloInstruction*> operands,
      tensorflow::StringPiece custom_call_target);

  // Creates a HostCompute instruction, which records host-side control and
  // data dependencies for use in instruction scheduling.
  static std::unique_ptr<HloInstruction> CreateHostCompute(
      const Shape& shape, tensorflow::gtl::ArraySlice<HloInstruction*> operands,
      tensorflow::StringPiece channel_name, const int64 cost_estimate_ns);

  // Creates a tuple instruction with the given elements. This is a convenience
  // wrapper around CreateVariadic.
  static std::unique_ptr<HloInstruction> CreateTuple(
      tensorflow::gtl::ArraySlice<HloInstruction*> elements);

  // Creates a reverse instruction, which reverses the order of the elements
  // in the specified dimensions.
  static std::unique_ptr<HloInstruction> CreateReverse(
      const Shape& shape, HloInstruction* operand,
      tensorflow::gtl::ArraySlice<int64> dimensions);

  // Creates an instance of GatherDimensionNumbers.
  static GatherDimensionNumbers MakeGatherDimNumbers(
      tensorflow::gtl::ArraySlice<int64> output_window_dims,
      tensorflow::gtl::ArraySlice<int64> elided_window_dims,
      tensorflow::gtl::ArraySlice<int64> gather_dims_to_operand_dims,
      int64 index_vector_dim);

  // Returns the opcode for this instruction.
  HloOpcode opcode() const { return opcode_; }

  // Returns true if this instruction has a side effect, irrespective of whether
  // any called computations may contain an instruction with side effects.
  bool HasSideEffectNoRecurse() const;

  // Returns true if this instruction has a side effect. An instruction has a
  // side effect if it uses certain opcodes or calls a computation with a side
  // effect.
  bool HasSideEffect() const;

  // Returns the result shape of this instruction.
  const Shape& shape() const;

  // Returns the (mutable) result shape of this instruction.
  Shape* mutable_shape() { return &shape_; }

  // Returns the ith operand to this instruction.
  const HloInstruction* operand(int64 i) const;

  // Returns the ith operand to this instruction.
  HloInstruction* mutable_operand(int64 i);

  // Returns the number of operands to this instruction.
  int64 operand_count() const { return operands_.size(); }

  // Returns the vector of operands of this instruction.
  using InstructionVector = tensorflow::gtl::InlinedVector<HloInstruction*, 2>;
  const InstructionVector& operands() const { return operands_; }

  // Returns the vector of unique operands, in the same order they are found
  // within the operand vector.
  InstructionVector unique_operands() const;

  // Returns the index of 'target' in the operands sequence.
  // Precondition: target must be an operand (or a fatal error will occur).
  int64 operand_index(const HloInstruction* target) const;

  // Returns the number of users of this instruction.
  int64 user_count() const { return users_.size(); }

  // Returns the users of this instruction.
  const std::vector<HloInstruction*>& users() const { return users_; }

  // Returns true if this instruction is a user of 'instruction'.
  bool IsUserOf(const HloInstruction* instruction) const {
    return ContainsKey(instruction->user_set_, this);
  }

  // Adds a control dependency from this instruction to the given
  // instruction. This instruction becomes a control predecessor of
  // 'instruction', and 'instruction' becomes a control successor of this
  // instruction. Returns an error status if either of the given instructions
  // does not belong to the same computation.
  //
  // This is used to enforce an additional ordering requirement that is not
  // captured by normal data dependencies, such as ordering among Send or Recv
  // operations to avoid deadlock.
  Status AddControlDependencyTo(HloInstruction* instruction);

  // Removes a previously added control dependency from this instruction to
  // 'instruction'.
  Status RemoveControlDependencyTo(HloInstruction* instruction);

  // Drops all control predecessors and successors from this HLO instruction.
  Status DropAllControlDeps();

  // Copies the control predecessors and successors on this HLO instruction to
  // `inst`.  Does not do a deep copy so this makes sense only if `inst` and
  // this HLO are in the same module.
  //
  // Depending on the use cases we see in practice, in the future we may
  // consider folding the logic here into Clone, CloneWithNewOperands and
  // ReplaceAllUsesWith by treating control dependencies like data dependencies.
  Status CopyAllControlDepsFrom(const HloInstruction* inst);

  // Returns the set of control predecessors (successors) of this
  // instruction. Control predecessors (successors) must execute before (after)
  // the current instruction.
  const std::vector<HloInstruction*>& control_predecessors() const {
    return control_predecessors_;
  }
  const std::vector<HloInstruction*>& control_successors() const {
    return control_successors_;
  }

  // Returns true if "other" performs the same computation as this instruction.
  bool Identical(
      const HloInstruction& other,
      const std::function<bool(const HloInstruction*, const HloInstruction*)>&
          eq_operands = std::equal_to<const HloInstruction*>(),
      const std::function<bool(const HloComputation*, const HloComputation*)>&
          eq_computations = std::equal_to<const HloComputation*>(),
      bool layout_sensitive = true) const {
    // An instruction is always identical to itself.
    if (this == &other) {
      return true;
    }

    // Identical instruction must have the same opcode, shape, and identical
    // operands.
    if (opcode() != other.opcode()) {
      return false;
    }
    if (!(layout_sensitive ? ShapeUtil::Equal(shape(), other.shape())
                           : ShapeUtil::Compatible(shape(), other.shape()))) {
      return false;
    }
    if (operands().size() != other.operands().size()) {
      return false;
    }

    // Use an explicit loop rather than ContainerEquals, because copying around
    // std::functions may be too expensive in some cases.
    for (size_t i = 0; i < operands().size(); ++i) {
      if (!eq_operands(operand(i), other.operand(i))) {
        return false;
      }
    }

    if (backend_config_ != other.backend_config_) {
      return false;
    }

    return IdenticalSlowPath(other, eq_computations);
  }

  // Returns whether the instruction has a constant operand.
  bool HasConstantOperand() const;

  // Returns whether this instruction does a rank-2 transposition.
  bool IsRank2Transpose() const;

  // Replaces the use of this instruction in "user" with "new_producer". Note
  // that there might be multiple uses of this instruction in "user"; all will
  // be replaced.
  Status ReplaceUseWith(HloInstruction* user, HloInstruction* new_producer);

  // Replaces the specified operand with new_operand.
  Status ReplaceOperandWith(int64 operand_no, HloInstruction* new_operand);

  // Replaces all uses of this instruction with the new producer. If
  // new_producer is a user of this instruction then new_producer remains a use
  // of this instruction to avoid introducing cycles into the graph.
  //
  // If this instruction is the root of its computation, sets the computation's
  // root to new_producer.
  Status ReplaceAllUsesWith(HloInstruction* new_producer);

  // Detaches an instruction from its operands. That is, remove the instruction
  // from each operand's user set. This should only be called prior to
  // deallocating the instruction.
  //
  // TODO(b/78305363): Make this automatic when deleting an instruction.
  void DetachFromOperands();

  // Performs a postorder DFS visit using this node as the root. If
  // call_finish_visit is true, then DfsHloVisitor::FinishVisit is called when
  // complete. If ignore_control_predecessors is true, instructions only
  // reachable via control dependencies will not be visited, and the postorder
  // will not take control dependencies into account. It is as if the control
  // dependencies didn't exist in the graph at all.
  template <typename HloInstructionPtr>
  Status Accept(DfsHloVisitorBase<HloInstructionPtr>* visitor,
                bool call_finish_visit = true,
                bool ignore_control_predecessors = false);
  Status Accept(ConstDfsHloVisitor* visitor, bool call_finish_visit = true,
                bool ignore_control_predecessors = false) const {
    return const_cast<HloInstruction*>(this)->Accept(
        visitor, call_finish_visit, ignore_control_predecessors);
  }

  // Same as Accept() above, but the order of operand and control predecessor
  // visitation is determined by the given operand order; if compare(A, B) ==
  // true, A is visited before B.
  using CompareFunction =
      std::function<bool(const HloInstruction*, const HloInstruction*)>;
  Status AcceptWithOperandOrder(DfsHloVisitor* visitor,
                                const CompareFunction& operand_order,
                                bool call_finish_visit = true);

  // Performs a postorder DFS visit using this node as the root. Calls the given
  // visitor function at each instruction.
  Status Accept(const std::function<Status(HloInstruction*)>& visitor_func);
  Status Accept(
      const std::function<Status(const HloInstruction*)>& visitor_func) const;

  // Visits all instructions rooted at this instruction using the given visitor
  // in the given order. 'order' must contain at least the set of instructions
  // rooted at this node (ie, those accessible from a DFS traversal from this
  // instruction). Instructions contained in 'order' which are not in the set of
  // instructions rooted at this node are ignored. 'order' must also be a valid
  // topological sort of these instructions (defs appear before uses) though
  // need not be a DFS post-order.
  Status AcceptOrdered(DfsHloVisitor* visitor,
                       const std::vector<const HloInstruction*>& order);

  // Visit this instruction and only this instruction with the given visitor.
  template <typename HloInstructionPtr>
  Status Visit(DfsHloVisitorBase<HloInstructionPtr>* visitor);

  // Returns the literal associated with this instruction.
  //
  // Note: only constant and parameter opcodes have an associated literal.
  const Literal& literal() const;

  // Returns whether there is literal associated with this instruction.
  bool HasLiteral() const;

  // Returns the parameter number associated with this instruction.
  //
  // Note: only parameter opcodes have an associated parameter number.
  int64 parameter_number() const {
    CHECK_EQ(HloOpcode::kParameter, opcode_);
    return parameter_number_;
  }

  // Returns the dimension sizes or numbers associated with this instruction.
  //
  // Precondition: opcode() is one of: concatenate, reduce, broadcast, reshape,
  // and reverse.
  const std::vector<int64>& dimensions() const;
  int64 dimensions(int64 index) const;

  // Accessor for the dimension in which a concatenate HLO should occur.
  // Precondition: opcode() == HloOpcode::kConcatenate
  int64 concatenate_dimension() const;

  // Returns the tuple index associated with this instruction.
  //
  // Precondition: opcode() == HloOpcode::kGetTupleElement
  int64 tuple_index() const;

  // Returns the first non-GetTupleElement ancestor instruction of 'hlo'.
  // If the first non-GTE ancestor is tuple-shaped, populates 'index' with the
  // (possibly nested) tuple indices used on the path from ancestor to 'hlo'.
  std::pair<const HloInstruction*, ShapeIndex> LatestNonGteAncestorAndIndex()
      const;

  std::pair<HloInstruction*, ShapeIndex> LatestNonGteAncestorAndIndex() {
    auto rv =
        const_cast<const HloInstruction*>(this)->LatestNonGteAncestorAndIndex();
    return {const_cast<HloInstruction*>(rv.first), rv.second};
  }

  // Same as LatestNonGteAncestorAndIndex, but just returns the HloInstruction.
  const HloInstruction* LatestNonGteAncestor() const;

  HloInstruction* LatestNonGteAncestor() {
    return const_cast<HloInstruction*>(
        const_cast<const HloInstruction*>(this)->LatestNonGteAncestor());
  }

  // Gets/sets the to_apply HloComputation for Call, Map, Reduce, etc.
  // The setter should only be called by HloModule or HloComputation methods.
  //
  // Precondition: The instruction has a valid to_apply_ field.
  HloComputation* to_apply() const;
  void set_to_apply(HloComputation* to_apply);

  // Returns the custom_call_target for CustomCall.
  // Precondition: opcode() == HloOpcode::kCustomCall
  const string& custom_call_target() const;

  // Returns the config for the Outfeed instruction.
  // Precondition: opcode() == HloOpcode::kOutfeed
  const string& outfeed_config() const;

  // Returns the shape for the Outfeed instruction.
  // Precondition: opcode() == HloOpcode::kOutfeed
  const Shape& outfeed_shape() const;

  // Gets/sets the while_condition or while_body HloComputation for While. The
  // setters should only be called by HloModule or HloComputation methods.
  //
  // Precondition: The instruction is a While instruction.
  HloComputation* while_condition() const;
  HloComputation* while_body() const;
  void set_while_condition(HloComputation* while_condition);
  void set_while_body(HloComputation* while_body);

  // Gets/sets the select or scatter HloComputation for SelectAndScatter. The
  // setters should only be called by HloModule or HloComputation methods.
  //
  // Precondition: opcode() == HloOpcode::kSelectAndScatter.
  HloComputation* select() const;
  HloComputation* scatter() const;
  void set_select(HloComputation* select);
  void set_scatter(HloComputation* scatter);

  // Gets/sets the true and false HloComputation for Conditional. The setters
  // should only be called by HloModule or HloComputation methods.
  //
  // Precondition: The instruction is a Conditional instruction.
  HloComputation* true_computation() const;
  HloComputation* false_computation() const;
  void set_true_computation(HloComputation* true_computation);
  void set_false_computation(HloComputation* false_computation);

  // Returns a string for the signature of this instruction if considered as a
  // function, e.g. the signature of an F32 add is (F32, F32) -> F32.
  string SignatureString() const;

  // Returns a debugging string that represents this instruction.
  //
  // (We express the default options using an overload rather than a default
  // param because gdb ignores default params, but does resolve overloads.)
  //
  // TODO(b/73348663): Make ToString() adaptive to the size of the string by
  // default, backing off on providing full information for very large strings,
  // or provide a different name for a ToString-like function that does that.
  string ToString() const { return ToString(HloPrintOptions()); }
  string ToString(const HloPrintOptions& options) const;

  // Components of the ToString() representation:

  // Returns a string representation of the operand list.
  string OperandsToString(const HloPrintOptions& options) const;

  // Returns string representation of op-specific attributes.
  std::vector<string> ExtraAttributesToString(
      const HloPrintOptions& options) const;

  // As ToString, but returns a shorter string.
  string ToShortString() const;

  // Returns a serialized representation of this instruction.
  HloInstructionProto ToProto() const;

  // Returns a category for the HLO. This could be something like "convolution"
  // or "elementwise".
  string ToCategory() const;

  // Returns a logging instruction, if the output of this instruction is logged.
  //
  // Postcondition: retval == nullptr || retval->opcode() == HloOpcode::kTrace
  HloInstruction* tracing() const;
  void set_tracing(HloInstruction* trace_instruction);

  // Returns the channel id associated with the instruction. The id is
  // shared between each Send/Recv pair and is globally unique to identify each
  // channel.
  //
  // Precondition: opcode() == HloOpcode::kSend or HloOpcode::kRecv
  int64 channel_id() const { return channel_id_; }

  // Returns the channel name associated with the instruction. The name is
  // used to identify host Send/Recv operations.
  //
  // Precondition: opcode() == HloOpcode::kHostCompute
  string channel_name() const { return channel_name_; }

  // Returns feature_index field associated with the instruction. The index
  // represents the index of the feature dimension.
  //
  // Precondition: opcode() is one of kBatchNormTraining, kBatchNormInference,
  // or kBatchNormGrad.
  int64 feature_index() const { return feature_index_; }

  // Returns a epsilon value associated with the instruction. The is a small
  // number added to the variance to avoid divide-by-zero error.
  //
  // Precondition: opcode() is one of kBatchNormTraining, kBatchNormInference,
  // or kBatchNormGrad.
  float epsilon() const { return epsilon_; }

  // Returns the infeed configuration string. The infeed configuration includes
  // any metadata needed for the backend compiler (e.g., infeed buffer address)
  // and is target-dependent.
  string infeed_config() const { return infeed_config_; }
  void set_infeed_config(const string& config) { infeed_config_ = config; }

  // Returns a tag to be used in tracing.
  //
  // Precondition: opcode() == HloOpcode::kTrace
  string TracingTag() const;

  // Returns whether the instruction is a constant.
  bool IsConstant() const;

  // Returns true if this instruction is fused, ie contained within a fusion
  // instruction.
  bool IsFused() const;

  // Returns the computation for this fused instruction.
  //
  // Precondition: opcode() == HloOpcode::kFusion
  HloComputation* fused_instructions_computation() const;

  // Returns true if this instruction can be legally fused into a fusion
  // instruction.
  bool IsFusable() const;

  // Returns the root instruction of the fused expression contained within this
  // fusion instruction.
  //
  // Precondition: opcode() == HloOpcode::kFusion
  HloInstruction* fused_expression_root() const;

  // Returns the list of fused instructions inside this fusion instruction.  The
  // returned type is a range of HloInstruction*s.
  //
  // Precondition: opcode() == HloOpcode::kFusion
  const tensorflow::gtl::iterator_range<UnwrappingIterator<
      std::list<std::unique_ptr<HloInstruction>>::const_iterator>>
  fused_instructions() const;

  const tensorflow::gtl::iterator_range<
      UnwrappingIterator<std::list<std::unique_ptr<HloInstruction>>::iterator>>
  fused_instructions();

  // Gets the number of instructions inside this fusion instruction.
  //
  // Precondition: opcode() == HloOpcode::kFusion
  int64 fused_instruction_count() const;

  // Returns the fused parameter instruction in this fusion instruction
  // corresponding to the given parameter number.
  //
  // Precondition: opcode() == HloOpcode::kFusion
  HloInstruction* fused_parameter(int64 parameter_number) const;

  // Returns the vector of fused parameters inside this fusion instruction.
  //
  // Precondition: opcode() == HloOpcode::kFusion
  const std::vector<HloInstruction*>& fused_parameters() const;

  // Returns true if this instruction is a fusion instruction that generates
  // multiple outputs.
  const bool IsMultiOutputFusion() const {
    return opcode() == HloOpcode::kFusion &&
           fused_expression_root()->opcode() == HloOpcode::kTuple;
  }

  FusionKind fusion_kind() const {
    CHECK_EQ(HloOpcode::kFusion, opcode_);
    return fusion_kind_;
  }

  void set_fusion_kind(FusionKind kind) {
    CHECK_EQ(HloOpcode::kFusion, opcode_);
    fusion_kind_ = kind;
  }

  // Returns the sharding applied to this operator.
  // REQUIRES: has_sharding() is true.
  const HloSharding& sharding() const {
    CHECK(has_sharding());
    return *sharding_;
  }
  // Returns the sharding applied to this operator, or default_ if none exists.
  const HloSharding& sharding_or_default(const HloSharding& default_) const {
    return sharding_ ? *sharding_ : default_;
  }
  // Returns the sharding unique device, if any.
  tensorflow::gtl::optional<int64> sharding_unique_device() const {
    if (sharding_ == nullptr) {
      return tensorflow::gtl::optional<int64>();
    }
    auto device = sharding_->UniqueDevice();
    return device.ok() ? device.ValueOrDie()
                       : tensorflow::gtl::optional<int64>();
  }
  // Sets the sharding of this operator. Should only be called by HloModule or
  // HloComputation methods.
  void set_sharding(const HloSharding& sharding) {
    sharding_ = MakeUnique<HloSharding>(sharding);
  }
  // Sets a sharding that assigns the current instruction to device.
  void set_device_sharding(int64 device);
  // Remove any sharding from this operator.
  void clear_sharding() { sharding_ = nullptr; }
  // Return true if this operator has a sharding assigned.
  bool has_sharding() const { return sharding_ != nullptr; }
  // Checks whether the instruction has compatible sharding with the other
  // instruction.
  bool has_compatible_sharding(const HloInstruction* other) const {
    if (!has_sharding()) {
      return !other->has_sharding();
    }
    return other->has_sharding() ? sharding() == other->sharding() : false;
  }

  // Retrieves the operand side metadata of a kDomain instruction.
  const DomainMetadata& operand_side_metadata() const {
    return *operand_side_metadata_;
  }
  // Retrieves the user side metadata of a kDomain instruction.
  const DomainMetadata& user_side_metadata() const {
    return *user_side_metadata_;
  }

  // When creating a new instruction which either replaces, or shifts up (kCopy
  // insertion case), another instruction, we need to make sure the certain
  // properties of the new instruction are copied into the derived one. As of
  // today, the metadata and sharding will be propagated to the derived
  // instruction.
  void SetupDerivedInstruction(HloInstruction* derived_instruction) const;

  // Adds a new operand the fusion instruction.
  HloInstruction* AddFusionOperand(HloInstruction* new_operand);

  // Merges the fused instructions from 'instruction_to_merge' into the
  // fused instruction set of 'this', updating operands as necessary.
  //
  // Precondition: opcode() == HloOpcode::kFusion
  // Predondition: 'instruction_to_merge' must be an operand of 'this'.
  void MergeFusionInstruction(HloInstruction* instruction_to_merge);

  // Merges the fused instructions from instruction_to_merge into the fused
  // instruction set of 'this' and generates multioutput fusion instructions.
  // All the users of instruction_to_merge will be redirected to 'this'
  // instruction. instruction_to_merge will be removed from its parent
  // computation.
  //
  // Precondition: opcode() == HloOpcode::kFusion
  void MergeFusionInstructionIntoMultiOutput(
      HloInstruction* instruction_to_merge);

  // Fuses the given instruction in this fusion instruction. instruction_to_fuse
  // is cloned and the clone is placed in the fusion
  // instruction. instruction_to_fuse is unchanged. Instruction is cloned rather
  // than moved to cleanly handle the case where the instruction has a use
  // outside the fusion instruction. Moving such an instruction into a fusion
  // instruction would violate the single-result invariant of HLO instructions
  // and significantly complicate code generation.
  //
  // Precondition: this->opcode() == HloOpcode::kFusion
  HloInstruction* FuseInstruction(HloInstruction* instruction_to_fuse) {
    return FuseInstructionInternal(instruction_to_fuse);
  }

  // Fuses the given instruction in this fusion instruction and generate
  // multioutput fusion instruction. A clone of the instruction_to_fuse will
  // be part of the output of fusion instructions. The users of
  // instruction_to_fuse will be redirected to this fusion instructions.
  // instruction_to_fuse will be removed from its parent computation.
  //
  // Precondition: this->opcode() == HloOpcode::kFusion
  HloInstruction* FuseInstructionIntoMultiOutput(
      HloInstruction* instruction_to_fuse) {
    return FuseInstructionInternal(instruction_to_fuse, /* add_output */ true);
  }

  // Returns the start index in the given dimension for a slice node.
  //
  // Precondition: opcode() == HloOpcode::kSlice
  int64 slice_starts(int64 dimension) const {
    CHECK_EQ(HloOpcode::kSlice, opcode_);
    return slice_starts_[dimension];
  }
  const std::vector<int64>& slice_starts() const { return slice_starts_; }

  // Returns the (exclusive) limit index in the given dimension for a slice
  // node.
  //
  // Precondition: opcode() == HloOpcode::kSlice
  int64 slice_limits(int64 dimension) const {
    CHECK_EQ(HloOpcode::kSlice, opcode_);
    return slice_limits_[dimension];
  }
  const std::vector<int64>& slice_limits() const {
    CHECK_EQ(HloOpcode::kSlice, opcode_);
    return slice_limits_;
  }

  // Returns the stride in the given dimension for a slice node.
  //
  // Precondition: opcode() == HloOpcode::kSlice
  int64 slice_strides(int64 dimension) const {
    CHECK_EQ(HloOpcode::kSlice, opcode_);
    return slice_strides_[dimension];
  }
  const std::vector<int64>& slice_strides() const { return slice_strides_; }

  // Returns the flag that describes whether a slice must be lowered into an
  // offset into the original operand.
  bool IsInPlaceSlice() const { return is_in_place_slice_; }

  // Sets and returns the flag that describes whether a slice must be lowered
  // into an offset into the original operand.
  bool SetIsInPlaceSlice(bool value) {
    is_in_place_slice_ = value;
    return value;
  }

  // Returns the size of the slice in the given dimension for a dynamic
  // slice node.
  //
  // Precondition: opcode() == HloOpcode::kDynamicSlice
  int64 slice_sizes(int64 dimension) const {
    CHECK_EQ(HloOpcode::kDynamicSlice, opcode_);
    return dynamic_slice_sizes_[dimension];
  }
  const std::vector<int64>& dynamic_slice_sizes() const {
    CHECK_EQ(HloOpcode::kDynamicSlice, opcode_);
    return dynamic_slice_sizes_;
  }

  // Returns the number of exponent bits for a reduce-precision node.
  //
  // Precondition: opcode() == HloOpcode::kReducePrecision
  int32 exponent_bits() const {
    CHECK_EQ(HloOpcode::kReducePrecision, opcode_);
    return exponent_bits_;
  }

  // Returns the number of mantissa bits for a reduce-precision node.
  //
  // Precondition: opcode() == HloOpcode::kReducePrecision
  int32 mantissa_bits() const {
    CHECK_EQ(HloOpcode::kReducePrecision, opcode_);
    return mantissa_bits_;
  }

  // Returns data on the window in a windowed operation such as
  // convolution.
  const Window& window() const {
    CHECK(window_ != nullptr);
    return *window_;
  }

  // Sets the window data in a windowed operation such as convolution.
  void set_window(const Window& window) {
    window_ = MakeUnique<Window>(window);
  }

  // Returns the padding configuration for a pad node.
  //
  // Precondition: opcode() == HloOpcode::kPad
  const PaddingConfig& padding_config() const {
    CHECK(padding_config_ != nullptr);
    return *padding_config_;
  }

  // Returns data on the dimension numbers used for a convolution operation,
  // which may be a kConvolution instruction or a kCustomCall that implements a
  // convolution.
  const ConvolutionDimensionNumbers& convolution_dimension_numbers() const {
    CHECK(convolution_dimension_numbers_ != nullptr);
    return *convolution_dimension_numbers_;
  }

  // Sets the convolution dimension numbers on this instruction.  In general you
  // shouldn't need to call this; instead, specify the convolution dimension
  // numbers when you create the instruction.
  void set_convolution_dimension_numbers(
      const ConvolutionDimensionNumbers& dnums) {
    convolution_dimension_numbers_ =
        MakeUnique<ConvolutionDimensionNumbers>(dnums);
  }

  FftType fft_type() const {
    CHECK_EQ(HloOpcode::kFft, opcode_);
    return fft_type_;
  }

  const std::vector<int64>& fft_length() const {
    CHECK_EQ(HloOpcode::kFft, opcode_);
    return fft_length_;
  }

  // Returns data on the dimension numbers used for a dot operation.
  const DotDimensionNumbers& dot_dimension_numbers() const {
    CHECK(dot_dimension_numbers_ != nullptr);
    return *dot_dimension_numbers_;
  }

  // Returns the dump string of the dot dimension numbers.
  string DotDimensionNumbersToString() const;

  const GatherDimensionNumbers& gather_dimension_numbers() const {
    CHECK(gather_dimension_numbers_ != nullptr);
    return *gather_dimension_numbers_;
  }

  tensorflow::gtl::ArraySlice<int64> gather_window_bounds() const {
    CHECK_EQ(opcode(), HloOpcode::kGather);
    return gather_window_bounds_;
  }

  // Returns the dump string of the gather dimension numbers.
  string GatherDimensionNumbersToString() const;

  // Returns the random distribution for this rng node.
  //
  // Precondition: opcode() == HloOpcode::kRng
  RandomDistribution random_distribution() const;

  // Clones the HLO instruction. The clone will have the same opcode, shape, and
  // operands. After creation the clone has no uses. "this" (the instruction
  // cloned from) is not changed. Suffix is the string to append to the name of
  // the instruction to form the name of the cloned instruction.
  // Ignores the control predecessors and successors of this HLO instruction.
  std::unique_ptr<HloInstruction> Clone(
      const string& suffix = "clone", HloCloneContext* context = nullptr) const;

  // Clones the HLO instruction as above but with new shape and operands.
  std::unique_ptr<HloInstruction> CloneWithNewOperands(
      const Shape& shape, tensorflow::gtl::ArraySlice<HloInstruction*> operands,
      HloCloneContext* context = nullptr) const;

  // Returns the computations this instruction directly calls (if any).
  const std::vector<HloComputation*>& called_computations() const {
    return called_computations_;
  }

  // Replaces all called computations based on a map function. This is needed
  // when we clone hlo_computations and want to let the instructions to point
  // to the newly cloned nodes.
  void ReplaceCalledComputations(
      std::function<HloComputation*(HloComputation*)> map_function) {
    for (int64 i = 0; i < called_computations_.size(); ++i) {
      called_computations_[i] = map_function(called_computations_[i]);
    }
  }

  // Clears out the called computations.
  //
  // This is, in particular, necessary when inlining function bodies into their
  // caller. If there were side-effecting operations in the called computations,
  // the call itself is considered side-effecting and thus cannot be removed. By
  // clearing out the computations, we reflect the fact that all side-effecting
  // properties have been reflected in the caller, and make the call HLO
  // removable.
  void ClearCalledComputations() { called_computations_.clear(); }

  // Returns true if this instruction performs an elementwise operation on
  // `operand_idx`-th operand. An instruction is elementwise on an operand iff,
  // after performing necessary implicit broadcast
  // (cs/IrArray::EmitArrayElementAddress), to compute the output at index
  // {i_0,i_1,...,i_n}, the only element required from the operand (if any) is
  // the element at {i_0,i_1,...,i_n}.
  //
  // Note on performance: when this instruction is kFusion, this method, in the
  // worst case, scans all fused instructions. We could speed this up by
  // caching.
  bool IsElementwiseOnOperand(int64 operand_idx) const;

  // Returns true if this instruction is elementwise on all its operands.
  bool IsElementwise() const;

  // Returns true if this elementwise instruction implicitly broadcasts operand
  // `operand_idx`.
  //
  // Precondition: this instruction should be an elementwise operation.
  bool ImplicitlyBroadcastsOperand(int64 operand_idx) const;

  // Returns true if this instruction is binary and elementwise.
  bool IsElementwiseBinary() const;

  // Returns whether this instruction may reuse elements of its `i`th operand.
  bool ReusesOperandElements(int64 i) const {
    return OperandElementUse(i) == UseKind::kReuse;
  }

  // Returns the indices that the given operand appear in the operand list of
  // this instruction. Note that an instruction can use the same operand
  // multiple times.
  std::vector<int64> OperandIndices(const HloInstruction* operand) const;

  // Convenience helper for ShapeUtil::InsertedOrDeleted1SizedDimensions. If
  // this reshape merely inserts or deletes 1-sized dimensions, return the input
  // indices of the deleted dimensions and the output indices of the inserted
  // dimensions.
  //
  // Precondition: this op must be a reshape.
  std::tuple<bool, std::vector<int64>, std::vector<int64>>
  ReshapeMerelyInsertsOrDeletes1SizedDimensions() const;

  // Gets/sets the string identifier for this instruction.
  const string& name() const { return name_; }
  void set_name(tensorflow::StringPiece name) { name_ = std::string(name); }

  // Use the given NameUniquer to select a unique name for the instruction based
  // on the instruction's existing name.
  void UniquifyName(NameUniquer* name_uniquer);

  // Set the unique id for this instruction to "id"
  void SetUniqueId(int id) {
    CHECK_EQ(unique_id_, -1);  // Should not be assigned already
    CHECK_GE(id, 0);
    unique_id_ = id;
  }

  // Return the unique ID assigned to this node via SetUniqueId (or -1
  // if no id has been assigned yet).
  int unique_id() const { return unique_id_; }

  // Returns the backend-specific configuration for how a backend should compile
  // this HLO. The meaning of the field is backend specific. Not for use before
  // or during general HLO optimization, since HLO optimizations do not preserve
  // this field and they cannot interpret it due to its meaning being backend
  // specific.
  //
  // ConfigProto should be a protobuf Message type.
  template <typename ConfigProto>
  StatusOr<ConfigProto> backend_config() const {
    ConfigProto proto;
    TF_RETURN_IF_ERROR(GetBackendConfigInternal(&proto));
    return std::move(proto);
  }
  Status set_backend_config(const tensorflow::protobuf::Message& proto);

  // Getter/setter for raw JSON-encoded backend config.  Prefer the
  // functions above that deal in proto Messages where possible.
  const string& raw_backend_config_string() const { return backend_config_; }
  void set_raw_backend_config_string(string config_str) {
    backend_config_ = std::move(config_str);
  }

  // Returns a string representation of a proto in the format used by
  // raw_backend_config_string.
  //
  // This is morally equivalent to:
  //
  //   HloInstruction instr;
  //   TF_RETURN_IF_ERROR(instr.set_backend_config(proto));
  //   return instr.raw_backend_config_string();
  //
  static StatusOr<string> BackendConfigToRawString(
      const tensorflow::protobuf::Message& proto);

  // Sets the debug metadata for this instruction.
  void set_metadata(const OpMetadata& metadata) { metadata_ = metadata; }
  const OpMetadata& metadata() const { return metadata_; }

  // Set/get the computation containing this instruction. set_parent should only
  // be called by HloComputation methods which add/remove instructions to
  // computations.
  void set_parent(HloComputation* computation) { parent_ = computation; }
  const HloComputation* parent() const { return parent_; }
  HloComputation* parent() { return parent_; }

  // Returns the module for this instruction.
  HloModule* GetModule() const;

  // Returns whether we could assign input and output layouts to this
  // instruction to make it a bitcast.
  bool CouldBeBitcast() const;

  // Get/Set the number of partitions per outer dimension (in order, starting
  // with outer-most dimension first). Currently used by the parallel cpu
  // backend to partition HLOs into parallel tasks.
  //
  // TODO(b/62783254) Replace these methods with a more general way to
  // annotate HLOs with backend-specific information.
  const std::vector<int64>& outer_dimension_partitions() const {
    return outer_dimension_partitions_;
  }
  void set_outer_dimension_partitions(
      const std::vector<int64>& outer_dimension_partitions);

  // Change the layout for an Constant Hlo instruction to match new_layout.  For
  // tuple shaped constants shape_index is the path to the internal array
  // subshape whose layout needs to be changed.
  void RelayoutConstant(const Layout& new_layout,
                        const ShapeIndex& shape_index = {});

 private:
  // Prints an instruction to a string.
  //
  // The canonical string representation needs to name operands and instruction
  // names in a consistent way. This is implemented through the
  // canonical_name_map.
  string ToStringWithCanonicalNameMap(
      const HloPrintOptions& options,
      CanonicalNameMap* canonical_name_map) const;

  // Prints an operand to a string.
  string OperandsToStringWithCanonicalNameMap(
      const HloPrintOptions& options,
      CanonicalNameMap* canonical_name_map) const;

  // Allow HloInstruction to access the ToStringWithCanonicalNameMap() and
  // OperandsToStringWithCanonicalNameMap() functions.
  friend class HloComputation;

  enum class UseKind { kNoUse, kReuse, kUsePermutingElements, kUse };

  // Helper class for computing OperandElementUse for kFusion.
  class FusionReusesParamElements;

  // See comments on Identical().
  bool IdenticalSlowPath(
      const HloInstruction& other,
      const std::function<bool(const HloComputation*, const HloComputation*)>&
          eq_computations) const;

  // Creates an n-ary elementwise operation.
  static std::unique_ptr<HloInstruction> CreateNary(
      const Shape& shape, HloOpcode opcode,
      tensorflow::gtl::ArraySlice<HloInstruction*> operands);

  // Appends operand to the list of operands and adds this instruction as a user
  // of the operand.
  void AppendOperand(HloInstruction* operand);

  // Adds a user for this instruction.
  void AddUser(HloInstruction* user);

  // Removes a user for this instruction.
  void RemoveUser(HloInstruction* user);

  // Internal constructor for a given opcode/shape, other fields must be filled
  // by factory methods.
  HloInstruction(HloOpcode opcode, const Shape& shape);

  // Fuses the given instruction into this fusion instruction. When add_output
  // is false (which is the default), instruction_to_fuse is cloned and the
  // clone is placed in the fusion instruction. instruction_to_fuse is
  // unchanged.
  //
  // When add_output is true, a clone of the instruction_to_fuse will be part
  // of the output of fusion instructions. The users of instruction_to_fuse
  // will be redirected to this fusion instructions. instruction_to_fuse will
  // be removed from its parent computation.
  //
  // Precondition: this->opcode() == HloOpcode::kFusion
  HloInstruction* FuseInstructionInternal(HloInstruction* instruction_to_fuse,
                                          bool add_output = false);

  // Clones the given instruction_to_fuse and insert the clone into this fusion
  // instruction. If add_output is true, a clone of instruction_to_fuse will
  // be in the output of the this fusion instruction (part of the tuple of the
  // fusion root).
  //
  // Precondition: opcode() == HloOpcode::kFusion
  HloInstruction* CloneAndFuseInternal(HloInstruction* instruction_to_fuse,
                                       bool add_output = false);

  // Clones a fusion instruction with a new shape and operands.
  std::unique_ptr<HloInstruction> CloneFusionWithNewOperands(
      const Shape& shape, tensorflow::gtl::ArraySlice<HloInstruction*> operands,
      HloCloneContext* context = nullptr) const;

  // Returns true if this instruction can legally have the dimensions field
  // set. Used for checking precondition of dimensions field accessors.
  bool CanHaveDimensionsField() const;

  // Returns how this instruction uses elements of its `i`th operand.
  UseKind OperandElementUse(int64 i) const;

  // Helper for implementing backend_config().  Parses backend_config_ into the
  // given proto.
  Status GetBackendConfigInternal(tensorflow::protobuf::Message* proto) const;

  int unique_id_;  // Unique to this HloInstruction within a HloModule

  // Opcode for this instruction.
  HloOpcode opcode_;

  // Instruction operands.
  InstructionVector operands_;

  // The set of control predecessors of this instruction.
  std::vector<HloInstruction*> control_predecessors_;

  // The users of this instruction. Users are HLOs where this instruction is an
  // operand. The vector users_ and the set user_set_ contain identical
  // members. The set enables fast membership testing and the vector enables
  // fast, stable iteration.
  std::vector<HloInstruction*> users_;
  std::unordered_set<const HloInstruction*> user_set_;

  // The set of control successors of this instruction.
  std::vector<HloInstruction*> control_successors_;

  // The computation in which this instruction is contained.
  HloComputation* parent_ = nullptr;

  // Shape of outfeed request.
  Shape outfeed_shape_;

  // Result shape of this instruction.
  Shape shape_;

  // Literal, only present for kConstant.
  std::unique_ptr<Literal> literal_;

  // Constant index, only present for kGetTupleElement.
  int64 tuple_index_ = -1;

  // Dimensions present for some operations that require reshaping or
  // broadcasting, including Reshape, Reduce, ReduceWindow, and Reverse.
  std::vector<int64> dimensions_;

  // Describes the window in a windowed operation such as convolution.
  std::unique_ptr<Window> window_;

  // Describes the dimension numbers used for a convolution.
  std::unique_ptr<ConvolutionDimensionNumbers> convolution_dimension_numbers_;

  // Describes the dimension numbers used for a dot.
  std::unique_ptr<DotDimensionNumbers> dot_dimension_numbers_;

  std::unique_ptr<GatherDimensionNumbers> gather_dimension_numbers_;
  std::vector<int64> gather_window_bounds_;

  // Describes FFT type for an FFT instruction.
  FftType fft_type_ = FftType::FFT;

  // Indicates the FFT length for an FFT instruction.
  std::vector<int64> fft_length_;

  // Describes the [begin, end) index range for a slice.
  std::vector<int64> slice_starts_;
  std::vector<int64> slice_limits_;
  std::vector<int64> slice_strides_;

  // Describes whether the slice can be lowered to an offset into the operand.
  bool is_in_place_slice_ = false;

  // The bit sizes for a reduce-precision operation.
  int32 exponent_bits_ = 0;
  int32 mantissa_bits_ = 0;

  // Describes the [start, start + size) range size for a dynamic slice
  // ('start' is specified dynamically in the second operand of the operation).
  std::vector<int64> dynamic_slice_sizes_;

  // The padding configuration that describes the edge padding and interior
  // padding of this pad instruction. Only set for pad instructions.
  std::unique_ptr<PaddingConfig> padding_config_;

  // The type of the fusion. Used by kFusion only.
  FusionKind fusion_kind_;

  // The sharding, if one exists.
  std::unique_ptr<HloSharding> sharding_;

  // Fields used by the kDomain instruction.
  std::unique_ptr<DomainMetadata> operand_side_metadata_;
  std::unique_ptr<DomainMetadata> user_side_metadata_;

  // For parameter instructions this field holds the parameter number.
  int64 parameter_number_ = 0;

  // Name of a global symbol to call, only present for kCustomCall.
  string custom_call_target_;

  // Name to use for host send/recv channels, only present for kHostCompute.
  string channel_name_;

  // Estimate of the duration of a host computation in nanoseconds.
  int64 cost_estimate_ns_ = 0;

  // Computations called by this instruction.
  std::vector<HloComputation*> called_computations_;

  // Indices of computations in called_computations_ for instructions which call
  // multiple computations.
  enum {
    // kWhile computations.
    kBodyComputationIndex = 0,
    kConditionComputationIndex = 1,

    // kSelectAndScatter computations.
    kSelectComputationIndex = 0,
    kScatterComputationIndex = 1,

    // kConditional computations.
    kTrueComputationIndex = 0,
    kFalseComputationIndex = 1,
  };

  // Outfeed configuration information, only present for kOutfeed.
  string outfeed_config_;

  // A trace instruction that consumes this instruction.
  //
  // Invariant: if trace_instruction_ != nullptr, trace_instruction has this as
  // an operand.
  HloInstruction* trace_instruction_ = nullptr;

  // The distribution requested for random number generation.
  // Only present for kRng.
  RandomDistribution distribution_;

  // A small float number added to the variance to avoid divide-by-zero error.
  // Only present for kBatchNormTraining.
  float epsilon_ = 0.0f;

  // An integer value representing the index of the feature dimension.
  // Only present for kBatchNormTraining.
  int64 feature_index_ = -1;

  // Represents a unique identifier for each Send/Recv instruction pair.
  // Only present for kSend or kRecv.
  int64 channel_id_ = -1;

  // The string representation of the infeed configuration.
  string infeed_config_;

  // The backend-specific configuration for how a backend should compile this
  // HLO. See the documentation on backend_config().
  string backend_config_;

  // String identifier for instruction.
  string name_;

  // Metadata for debugging.
  OpMetadata metadata_;

  // The number of partitions per outer dimension (listed in order from
  // outer-most dimension first).
  std::vector<int64> outer_dimension_partitions_;

  TF_DISALLOW_COPY_AND_ASSIGN(HloInstruction);
};

string ToString(HloInstruction::FusionKind kind);
StatusOr<HloInstruction::FusionKind> StringToFusionKind(
    const string& kind_name);

// Custom (de)stringification functions for protos that live inside
// HloInstruction.
string PaddingConfigToString(const PaddingConfig& padding);
string OpMetadataToString(const OpMetadata& metadata);
string RandomDistributionToString(const RandomDistribution& distribution);
string ConvolutionDimensionNumbersToString(
    const ConvolutionDimensionNumbers& dnums);

StatusOr<RandomDistribution> StringToRandomDistribution(const string& name);

std::ostream& operator<<(std::ostream& os, HloInstruction::FusionKind kind);

// Map classes that guarantee a deterministic iteration order when the key is
// an HloInstruction* or a const HloInstruction*.
// To make the iteration order over the map deterministic, the comparator
// should not be using the pointer values, but rather an intrinsic property of
// the hlo. Exception: null pointer values compare less than non-null.
//
// Note that this cannot be used for HLO instructions across multiple modules
// since the id of HLO instructions are only unique within each HLO module.
struct HloPtrComparator {
  bool operator()(const HloInstruction* const& lhs,
                  const HloInstruction* const& rhs) const {
    if (rhs == nullptr) {
      // Nothing compares less than nullptr.
      return false;
    }
    if (lhs == nullptr) {
      return true;
    }
    return lhs->unique_id() < rhs->unique_id();
  }
};

template <typename ValueT>
using HloInstructionMap = std::map<HloInstruction*, ValueT, HloPtrComparator>;

template <typename ValueT>
using ConstHloInstructionMap =
    std::map<const HloInstruction*, ValueT, HloPtrComparator>;

using HloInstructionSet = std::set<HloInstruction*, HloPtrComparator>;
using ConstHloInstructionSet =
    std::set<const HloInstruction*, HloPtrComparator>;

}  // namespace xla

#endif  // TENSORFLOW_COMPILER_XLA_SERVICE_HLO_INSTRUCTION_H_
