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

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

#include <string>
#include <unordered_map>
#include <utility>

#include "tensorflow/core/platform/logging.h"
// IWYU pragma: no_include "llvm/IR/Intrinsics.gen.inc"
#include "absl/algorithm/container.h"
#include "llvm/IR/BasicBlock.h"
#include "llvm/IR/Constants.h"
#include "llvm/IR/DerivedTypes.h"
#include "llvm/IR/Instructions.h"
#include "llvm/IR/Module.h"
#include "tensorflow/compiler/xla/primitive_util.h"
#include "tensorflow/compiler/xla/service/elemental_ir_emitter.h"
#include "tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.h"
#include "tensorflow/compiler/xla/service/gpu/gpu_constants.h"
#include "tensorflow/compiler/xla/service/gpu/ir_emitter_nested.h"
#include "tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h"
#include "tensorflow/compiler/xla/service/gpu/launch_dimensions.h"
#include "tensorflow/compiler/xla/service/hlo_casting_utils.h"
#include "tensorflow/compiler/xla/service/hlo_computation.h"
#include "tensorflow/compiler/xla/service/hlo_instructions.h"
#include "tensorflow/compiler/xla/service/llvm_ir/buffer_assignment_util.h"
#include "tensorflow/compiler/xla/service/llvm_ir/fused_ir_emitter.h"
#include "tensorflow/compiler/xla/service/llvm_ir/ir_array.h"
#include "tensorflow/compiler/xla/service/llvm_ir/llvm_loop.h"
#include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h"
#include "tensorflow/compiler/xla/service/llvm_ir/loop_emitter.h"
#include "tensorflow/compiler/xla/service/llvm_ir/tuple_ops.h"
#include "tensorflow/compiler/xla/service/name_uniquer.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/compiler/xla/window_util.h"
#include "tensorflow/core/lib/core/errors.h"

// Convenient function to cast the provided llvm::Value* using IRBuilder
// to default address space. This is useful in particular for generating
// IR for AMDGPU target, as its kernel variables are in address space 5
// instead of the default address space.
static llvm::Value* AddrCastToDefault(llvm::Value* arg, llvm::IRBuilder<>& b) {
  llvm::Type* arg_type = arg->getType();
  CHECK(arg_type->isPointerTy());
  if (arg_type->getPointerAddressSpace() != 0) {
    llvm::Type* generic_arg_type =
        arg_type->getPointerElementType()->getPointerTo(0);
    llvm::Value* addrspacecast_arg =
        b.CreateAddrSpaceCast(arg, generic_arg_type);
    return addrspacecast_arg;
  }
  return arg;
}

namespace xla {

using llvm_ir::IrName;
using llvm_ir::SetToFirstInsertPoint;

namespace gpu {

IrEmitter::IrEmitter(const HloModuleConfig& hlo_module_config,
                     IrEmitterContext* ir_emitter_context, bool is_nested)
    : ir_emitter_context_(ir_emitter_context),
      module_(ir_emitter_context->llvm_module()),
      b_(module_->getContext()),
      bindings_(ir_emitter_context->hlo_module(),
                &ir_emitter_context->buffer_assignment(), &b_, module_,
                is_nested),
      hlo_module_config_(hlo_module_config) {
}

Status IrEmitter::DefaultAction(HloInstruction* hlo) {
  ElementalIrEmitter::HloToElementGeneratorMap operand_to_generator;
  for (const HloInstruction* operand : hlo->operands()) {
    operand_to_generator[operand] = [=](const llvm_ir::IrArray::Index& index) {
      return GetIrArray(*operand, *hlo)
          .EmitReadArrayElement(index, &b_, operand->name());
    };
  }
  return EmitTargetElementLoop(
      *hlo, GpuElementalIrEmitter(hlo_module_config_, module_, &b_,
                                  GetNestedComputer())
                .MakeElementGenerator(hlo, operand_to_generator));
}

Status IrEmitter::EmitConstants(const HloComputation& computation,
                                bool lookup_indices) {
  for (HloInstruction* instr : computation.instructions()) {
    if (instr->opcode() != HloOpcode::kConstant) {
      continue;
    }
    Literal& literal = *Cast<HloConstantInstruction>(instr)->mutable_literal();
    const bool should_emit_initializer = ShouldEmitLiteralInLlvmIr(literal);
    llvm::ArrayType* global_type =
        llvm::ArrayType::get(b_.getInt8Ty(), literal.size_bytes());
    llvm::Constant* initializer =
        should_emit_initializer
            ? llvm_ir::ConvertLiteralToIrConstant(literal, module_)
            : llvm::ConstantAggregateZero::get(global_type);
    if (should_emit_initializer) {
      VLOG(3) << "Emitted initializer for constant with shape "
              << ShapeUtil::HumanString(literal.shape());
    }

    // These globals will be looked up by name by GpuExecutable so we need to
    // give them an external linkage.  Not all of their uses are visible in
    // the LLVM IR (e.g. TupleThunk) so we can't give then a linkage that
    // merely preserves their names (like available_externally), we also need
    // to ensure that they stick around even if they're "unused".
    //
    // We may have to be more clever here in the future if we notice that we're
    // keeping around too many globals because of their linkage.
    unsigned global_address_space = llvm_ir::GetGlobalMemoryAddressSpace(
        *ir_emitter_context_->llvm_module());

    std::string global_name = llvm_ir::ConstantHloToGlobalName(*instr);

    llvm::GlobalVariable* global_for_const = new llvm::GlobalVariable(
        global_type, /*isConstant=*/should_emit_initializer,
        llvm::GlobalValue::ExternalLinkage,
        /*Initializer=*/initializer, global_name,
        /*TLMode=*/llvm::GlobalValue::NotThreadLocal,
        /*AddressSpace=*/global_address_space,
        /*isExternallyInitialized=*/false);
    global_for_const->setAlignment(llvm::Align(kConstantBufferAlignBytes));
    ir_emitter_context_->llvm_module()->getGlobalList().push_back(
        global_for_const);

    GpuExecutable::ConstantInfo info;
    info.symbol_name = global_name;

    if (!should_emit_initializer) {
      auto base = static_cast<const uint8*>(literal.untyped_data());
      info.content.assign(base, base + literal.size_bytes());
    }
    if (lookup_indices) {
      auto maybe_slice =
          ir_emitter_context_->buffer_assignment().GetUniqueSlice(instr, {});
      if (maybe_slice.ok()) {
        info.allocation_index = maybe_slice.ValueOrDie().index();
      }
    }
    ir_emitter_context_->constants().push_back(std::move(info));
  }
  return Status::OK();
}

Status IrEmitter::HandleConstant(HloInstruction* constant) {
  return Status::OK();
}

Status IrEmitter::HandleAddDependency(HloInstruction* add_dependency) {
  VLOG(2) << "HandleAddDependency: " << add_dependency->ToString();
  const HloInstruction* operand = add_dependency->operand(0);
  // Add_Dependency is a no-op, but we still want to bind it to an llvm::Value
  // sometimes, e.g., when it's operand is a constant or a bitcast of a
  // constant.
  if (bindings_.BoundToIrValue(*operand)) {
    bindings_.BindHloToIrValue(*add_dependency, GetBasePointer(*operand));
  }
  return Status::OK();
}

Status IrEmitter::HandleGetTupleElement(HloInstruction* get_tuple_element) {
  auto operand = get_tuple_element->operand(0);
  CHECK(bindings_.BoundToIrValue(*operand));
  bindings_.BindHloToIrValue(
      *get_tuple_element,
      llvm_ir::EmitGetTupleElement(
          get_tuple_element->shape(), get_tuple_element->tuple_index(),
          // TODO(b/26344050): tighten the alignment here
          // based on the real element type.
          /*alignment=*/1, GetBasePointer(*operand), &b_));
  return Status::OK();
}

Status IrEmitter::HandleSend(HloInstruction*) {
  return Unimplemented("Send is not implemented on GPU");
}

Status IrEmitter::HandleSendDone(HloInstruction*) {
  return Unimplemented("Send-Done is not implemented on GPU");
}

Status IrEmitter::HandleRecv(HloInstruction*) {
  return Unimplemented("Recv is not implemented on GPU");
}

Status IrEmitter::HandleRecvDone(HloInstruction*) {
  return Unimplemented("Recv-done is not implemented on GPU");
}

Status IrEmitter::HandleScatter(HloInstruction*) {
  return Unimplemented("Scatter is not implemented on GPUs.");
}

Status IrEmitter::HandleTuple(HloInstruction* tuple) {
  std::vector<llvm::Value*> base_ptrs;
  for (const HloInstruction* operand : tuple->operands()) {
    base_ptrs.push_back(GetBasePointer(*operand));
  }
  llvm_ir::EmitTuple(GetIrArray(*tuple, *tuple), base_ptrs, &b_);
  return Status::OK();
}

Status IrEmitter::EmitCallToNestedComputation(
    const HloComputation& nested_computation,
    absl::Span<llvm::Value* const> operands, llvm::Value* output) {
  TF_RET_CHECK(nested_computation.num_parameters() > 0);
  llvm::Function*& emitted_function =
      computation_to_ir_function_[&nested_computation];
  if (emitted_function == nullptr) {
    TF_ASSIGN_OR_RETURN(
        auto ir_emitter_nested,
        IrEmitterNested::Create(hlo_module_config_, nested_computation,
                                ir_emitter_context_));
    TF_RETURN_IF_ERROR(ir_emitter_nested->CodegenNestedComputation());
    emitted_function = ir_emitter_nested->GetEmittedFunction();
  }

  // Operands are in default address space for non-AMDGPU target.
  // However for AMDGPU target, addrspacecast alloca variables from
  // addrspace 5 to addrspace 0 is needed.
  std::vector<llvm::Value*> arguments;
  absl::c_transform(
      operands, std::back_inserter(arguments),
      [this](llvm::Value* arg) { return AddrCastToDefault(arg, b_); });

  llvm::Value* casted_output = AddrCastToDefault(output, b_);
  arguments.push_back(casted_output);

  Call(emitted_function, arguments);

  return Status::OK();
}

bool IrEmitter::MaybeEmitDirectAtomicOperation(
    const HloComputation& computation, llvm::Value* output_address,
    llvm::Value* source_address) {
  CHECK_EQ(2, computation.num_parameters());

  HloOpcode root_opcode = computation.root_instruction()->opcode();
  PrimitiveType element_type =
      computation.root_instruction()->shape().element_type();
  bool is_atomic_integral = element_type == S32 || element_type == U32 ||
                            element_type == S64 || element_type == U64;
  llvm::Value* source = Load(source_address, "source");

  // Just passing along RHS -> atomic store.
  if (computation.instruction_count() == 2 &&
      root_opcode == HloOpcode::kParameter &&
      (element_type == F32 || is_atomic_integral) &&
      computation.root_instruction()->parameter_number() == 1) {
    llvm::StoreInst* store = Store(source, output_address);
    store->setAtomic(llvm::AtomicOrdering::Unordered);
    // Derive a minimum alignment from the type. The optimizer can increase it
    // later.
    store->setAlignment(
        llvm::Align(ShapeUtil::ByteSizeOfPrimitiveType(element_type)));
    return true;
  }

  if (computation.instruction_count() != 3) {
    // We special-case only computations with one computing instruction for now.
    // Such computation has exactly three instructions given it has two
    // parameters.
    return false;
  }

  if (root_opcode == HloOpcode::kAdd) {
    llvm::Triple target_triple = llvm::Triple(module_->getTargetTriple());
    // NVPTX supports atomicAdd on F32 and integer types.
    if (target_triple.isNVPTX()) {
      // "atom.add.f64 requires sm_60 or higher."
      // https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#parallel-synchronization-and-communication-instructions-atom
      bool f64_atomic_add_supported =
          ir_emitter_context_->cuda_compute_capability()->cc_major >= 6;

      bool atomic_add_supported =
          element_type == F32 ||
          (f64_atomic_add_supported && element_type == F64);
      if (atomic_add_supported) {
        AtomicRMW(llvm::AtomicRMWInst::FAdd, output_address, source,
                  llvm::MaybeAlign(),
                  llvm::AtomicOrdering::SequentiallyConsistent);
        return true;
      }
    }

    if (is_atomic_integral) {
      // integral + integral
      AtomicRMW(llvm::AtomicRMWInst::Add, output_address, source,
                llvm::MaybeAlign(),
                llvm::AtomicOrdering::SequentiallyConsistent);
      return true;
    }
  }

  // NVPTX supports atomicMax and atomicMin only on integer types.
  if (root_opcode == HloOpcode::kMaximum && is_atomic_integral) {
    // max(integral, integral)
    auto opcode = primitive_util::IsSignedIntegralType(element_type)
                      ? llvm::AtomicRMWInst::Max
                      : llvm::AtomicRMWInst::UMax;
    AtomicRMW(opcode, output_address, source, llvm::MaybeAlign(),
              llvm::AtomicOrdering::SequentiallyConsistent);
    return true;
  }

  if (root_opcode == HloOpcode::kMinimum && is_atomic_integral) {
    // min(integral, integral)
    auto opcode = primitive_util::IsSignedIntegralType(element_type)
                      ? llvm::AtomicRMWInst::Min
                      : llvm::AtomicRMWInst::UMin;
    AtomicRMW(opcode, output_address, source, llvm::MaybeAlign(),
              llvm::AtomicOrdering::SequentiallyConsistent);
    return true;
  }

  return false;
}

// Implements atomic binary operations using atomic compare-and-swap
// (atomicCAS) as follows:
//   1. Reads the value from the memory pointed to by output_address and
//     records it as old_output.
//   2. Uses old_output as one of the source operand to perform the binary
//     operation and stores the result in new_output.
//   3. Calls atomicCAS which implements compare-and-swap as an atomic
//     operation. In particular, atomicCAS reads the value from the memory
//     pointed to by output_address, and compares the value with old_output. If
//     the two values equal, new_output is written to the same memory location
//     and true is returned to indicate that the atomic operation succeeds.
//     Otherwise, the new value read from the memory is returned. In this case,
//     the new value is copied to old_output, and steps 2. and 3. are repeated
//     until atomicCAS succeeds.
//
// On Nvidia GPUs, atomicCAS can only operate on 32 bit and 64 bit integers. If
// the element type of the binary operation is 32 bits or 64 bits, the integer
// type of the same size is used for the atomicCAS operation. On the other hand,
// if the element type is smaller than 32 bits, int32 is used for the atomicCAS
// operation. In this case, atomicCAS reads and writes 32 bit values from
// the memory, which is larger than the memory size required by the original
// atomic binary operation. We mask off the last two bits of the output_address
// and use the result as an address to read the 32 bit values from the memory.
// This can avoid out of bound memory accesses if tensor buffers are 4 byte
// aligned and have a size of 4N, an assumption that the runtime can guarantee.
//
// The pseudo code is shown below. Variables *_address are pointers to a memory
// region with a size equal to the size of the atomicCAS operation, with the
// exception that new_output_address is a pointer to a memory region with a size
// equal to the element size of the binary operation.
//
//   element_size = sizeof(element_type);
//   atomic_size = max(32, element_size);
//   cas_new_output_address = alloca(atomic_size);
//   cas_old_output_address = alloca(atomic_size);
//   if (atomic_size != element_size) {
//     atomic_address = output_address & ((int64)(-4));
//     new_output_address = cas_new_output_address + (output_address & 3);
//   } else {
//     atomic_address = output_address;
//     new_output_address = cas_new_output_address;
//   }
//
//   *cas_old_output_address = *atomic_address;
//   do {
//     *cas_new_output_address = *cas_old_output_address;
//     *new_output_address = operation(*new_output_address, *source_address);
//     (*cas_old_output_address, success) =
//       atomicCAS(atomic_address, *cas_old_output_address,
//       *cas_new_output_address);
//   } while (!success);
//
Status IrEmitter::EmitAtomicOperationUsingCAS(const HloComputation& computation,
                                              llvm::Value* output_address,
                                              llvm::Value* source_address) {
  llvm::PointerType* output_address_type =
      llvm::dyn_cast<llvm::PointerType>(output_address->getType());
  CHECK_NE(output_address_type, nullptr);

  // element_type is the data type for the binary operation.
  llvm::Type* element_type = output_address_type->getPointerElementType();
  int element_size = llvm_ir::GetSizeInBits(element_type);

  int atomic_size = (element_size < 32) ? 32 : element_size;
  llvm::Type* atomic_type = b_.getIntNTy(atomic_size);
  llvm::Type* atomic_address_type =
      atomic_type->getPointerTo(output_address_type->getPointerAddressSpace());

  // cas_old_output_address and cas_new_output_address point to the scratch
  // memory where we store the old and new values for the repeated atomicCAS
  // operations.
  llvm::Value* cas_old_output_address = llvm_ir::EmitAllocaAtFunctionEntry(
      atomic_type, "cas_old_output_address", &b_);
  llvm::Value* cas_new_output_address = llvm_ir::EmitAllocaAtFunctionEntry(
      atomic_type, "cas_new_output_address", &b_);

  // Emit preparation code to the preheader.
  llvm::BasicBlock* loop_preheader_bb = b_.GetInsertBlock();

  llvm::Value* atomic_memory_address;
  // binop_output_address points to the scratch memory that stores the
  // result of the binary operation.
  llvm::Value* binop_output_address;
  if (element_size < 32) {
    // Assume the element size is an integer number of bytes.
    CHECK_EQ((element_size % sizeof(char)), 0);
    llvm::Type* address_int_type =
        module_->getDataLayout().getIntPtrType(output_address_type);
    atomic_memory_address = PtrToInt(output_address, address_int_type);
    llvm::Value* mask = llvm::ConstantInt::get(address_int_type, 3);
    llvm::Value* offset = And(atomic_memory_address, mask);
    mask = llvm::ConstantInt::get(address_int_type, -4);
    atomic_memory_address = And(atomic_memory_address, mask);
    atomic_memory_address =
        IntToPtr(atomic_memory_address, atomic_address_type);
    binop_output_address =
        Add(PtrToInt(cas_new_output_address, address_int_type), offset);
    binop_output_address = IntToPtr(
        binop_output_address,
        llvm::PointerType::get(
            element_type,
            cas_new_output_address->getType()->getPointerAddressSpace()));
  } else {
    atomic_memory_address = b_.CreatePointerBitCastOrAddrSpaceCast(
        output_address, atomic_address_type);
    binop_output_address = b_.CreatePointerBitCastOrAddrSpaceCast(
        cas_new_output_address,
        llvm::PointerType::get(
            element_type,
            cas_new_output_address->getType()->getPointerAddressSpace()));
  }

  // Use the value from the memory that atomicCAS operates on to initialize
  // cas_old_output.
  llvm::Value* cas_old_output = Load(atomic_memory_address, "cas_old_output");
  Store(cas_old_output, cas_old_output_address);

  llvm::BasicBlock* loop_exit_bb = loop_preheader_bb->splitBasicBlock(
      b_.GetInsertPoint(), "atomic_op_loop_exit");
  llvm::BasicBlock* loop_body_bb = llvm::BasicBlock::Create(
      b_.getContext(), "atomic_op_loop_body", b_.GetInsertBlock()->getParent());
  b_.SetInsertPoint(loop_body_bb);
  // Change preheader's successor from loop_exit_bb to loop_body_bb.
  loop_preheader_bb->getTerminator()->setSuccessor(0, loop_body_bb);

  // Emit the body of the loop that repeatedly invokes atomicCAS.
  //
  // Use cas_old_output to initialize cas_new_output.
  cas_old_output = Load(cas_old_output_address, "cas_old_output");
  Store(cas_old_output, cas_new_output_address);
  // Emits code to calculate new_output = operation(old_output, source);
  TF_RETURN_IF_ERROR(EmitCallToNestedComputation(
      computation, {binop_output_address, source_address},
      binop_output_address));

  llvm::Value* cas_new_output = Load(cas_new_output_address, "cas_new_output");

  // Emit code to perform the atomicCAS operation
  // (cas_old_output, success) = atomicCAS(memory_address, cas_old_output,
  //                                       cas_new_output);
  llvm::Value* ret_value = AtomicCmpXchg(
      atomic_memory_address, cas_old_output, cas_new_output, llvm::MaybeAlign(),
      llvm::AtomicOrdering::SequentiallyConsistent,
      llvm::AtomicOrdering::SequentiallyConsistent);

  // Extract the memory value returned from atomicCAS and store it as
  // cas_old_output.
  Store(ExtractValue(ret_value, 0, "cas_old_output"), cas_old_output_address);
  // Extract the success bit returned from atomicCAS and generate a
  // conditional branch on the success bit.
  CondBr(ExtractValue(ret_value, 1, "success"), loop_exit_bb, loop_body_bb);

  // Set the insertion point to the exit basic block so that the caller of
  // this method can continue emitting code to the right place.
  SetToFirstInsertPoint(loop_exit_bb, &b_);
  return Status::OK();
}

Status IrEmitter::EmitAtomicOperationForNestedComputation(
    const HloComputation& computation, llvm::Value* output_address,
    llvm::Value* source_address) {
  if (computation.num_parameters() != 2) {
    // TODO(b/30258929): We only accept binary computations so far.
    return Unimplemented(
        "We only support atomic functions with exactly two parameters, but "
        "computation %s has %d.",
        computation.name(), computation.num_parameters());
  }

  if (MaybeEmitDirectAtomicOperation(computation, output_address,
                                     source_address)) {
    return Status::OK();
  }

  return EmitAtomicOperationUsingCAS(computation, output_address,
                                     source_address);
}

Status IrEmitter::HandleTupleSelect(HloInstruction* tuple_select) {
  return InternalError(
      "Dynamic selection of tuples is not supported. Please file a bug against "
      "XLA/GPU if you need it");
}

namespace {
llvm::Value* Real(llvm::Value* x, llvm::IRBuilder<>* b) {
  return b->CreateExtractValue(x, {0});
}

llvm::Value* Imag(llvm::Value* x, llvm::IRBuilder<>* b) {
  return b->CreateExtractValue(x, {1});
}

std::pair<llvm::Value*, llvm::Value*> MultiplyComplex(llvm::Value* lhs_value,
                                                      llvm::Value* rhs_value,
                                                      llvm::IRBuilder<>* b) {
  llvm::Value* lhs_real = Real(lhs_value, b);
  llvm::Value* lhs_imag = Imag(lhs_value, b);
  llvm::Value* rhs_real = Real(rhs_value, b);
  llvm::Value* rhs_imag = Imag(rhs_value, b);
  llvm::Value* real_result1 = b->CreateFMul(lhs_real, rhs_real);
  llvm::Value* real_result2 = b->CreateFMul(lhs_imag, rhs_imag);
  llvm::Value* real_result = b->CreateFSub(real_result1, real_result2);
  llvm::Value* imag_result1 = b->CreateFMul(lhs_real, rhs_imag);
  llvm::Value* imag_result2 = b->CreateFMul(lhs_imag, rhs_real);
  llvm::Value* imag_result = b->CreateFAdd(imag_result1, imag_result2);
  return {real_result, imag_result};
}
}  // namespace

Status IrEmitter::HandleConvolution(HloInstruction* convolution) {
  if (ShapeUtil::IsZeroElementArray(convolution->shape())) {
    // Emit no code for an empty output.
    return Status::OK();
  }
  // TODO(b/31409998): Support convolution with dilation.
  return Unimplemented(
      "Hit a case for convolution that is not implemented on GPU.");
}

Status IrEmitter::HandleFft(HloInstruction* fft) {
  if (ShapeUtil::IsZeroElementArray(fft->shape())) {
    // Emit no code for an empty output.
    return Status::OK();
  }
  return Unimplemented("Hit a case for fft that is not implemented on GPU.");
}

Status IrEmitter::HandleAllReduce(HloInstruction* crs) {
  return Unimplemented(
      "AllReduce cannot be nested inside of fusion, map, etc.");
}

Status IrEmitter::HandleParameter(HloInstruction* parameter) {
  return Status::OK();
}

Status IrEmitter::HandleFusion(HloInstruction* fusion) {
  // kFusion for library calls should be handled by
  // IrEmitterUnnested::HandleFusion.
  CHECK_EQ(HloInstruction::FusionKind::kLoop, fusion->fusion_kind());
  GpuElementalIrEmitter elemental_emitter(hlo_module_config_, module_, &b_,
                                          GetNestedComputer());
  FusedIrEmitter fused_emitter(&elemental_emitter);
  BindFusionArguments(fusion, &fused_emitter);
  TF_ASSIGN_OR_RETURN(auto generator, fused_emitter.GetGenerator(
                                          fusion->fused_expression_root()));
  return EmitTargetElementLoop(*fusion, generator);
}

Status IrEmitter::HandleCall(HloInstruction* call) {
  std::vector<llvm::Value*> operand_addresses;
  for (HloInstruction* operand : call->operands()) {
    operand_addresses.push_back(GetBasePointer(*operand));
  }
  return EmitCallToNestedComputation(*call->to_apply(), operand_addresses,
                                     GetBasePointer(*call));
}

Status IrEmitter::HandleCustomCall(HloInstruction*) {
  return Unimplemented("custom-call");
}

Status IrEmitter::HandleInfeed(HloInstruction*) {
  // TODO(b/30467474): Implement infeed on GPU.
  return Unimplemented("Infeed is not supported on GPU.");
}

Status IrEmitter::HandleOutfeed(HloInstruction*) {
  // TODO(b/34359662): Implement outfeed on GPU.
  return Unimplemented("Outfeed is not supported on GPU.");
}

Status IrEmitter::HandleBatchNormInference(HloInstruction*) {
  return Unimplemented(
      "The GPU backend does not implement BatchNormInference directly.  It "
      "should be lowered before IR emission to HLO-soup using "
      "BatchNormRewriter or to a cudnn CustomCall using "
      "CudnnBatchNormRewriter.");
}

Status IrEmitter::HandleBatchNormTraining(HloInstruction*) {
  return Unimplemented(
      "The GPU backend does not implement BatchNormTraining directly.  It "
      "should be lowered before IR emission to HLO-soup using "
      "BatchNormRewriter or to a cudnn CustomCall using "
      "CudnnBatchNormRewriter.");
}

Status IrEmitter::HandleBatchNormGrad(HloInstruction*) {
  return Unimplemented(
      "The GPU backend does not implement BatchNormGrad directly.  It should "
      "be lowered before IR emission to HLO-soup (using BatchNormRewriter) or "
      "to a cudnn CustomCall using CudnnBatchNormRewriter.");
}

StatusOr<std::vector<llvm::Value*>> IrEmitter::ComputeNestedElement(
    const HloComputation& computation,
    absl::Span<llvm::Value* const> parameter_elements) {
  const Shape& return_shape = computation.root_instruction()->shape();
  llvm::Value* return_buffer = llvm_ir::EmitAllocaAtFunctionEntry(
      llvm_ir::ShapeToIrType(return_shape, module_), "return_buffer", &b_);
  std::vector<llvm::Value*> parameter_buffers;
  for (llvm::Value* parameter_element : parameter_elements) {
    parameter_buffers.push_back(llvm_ir::EmitAllocaAtFunctionEntry(
        parameter_element->getType(), "parameter_buffer", &b_));
    Store(parameter_element, parameter_buffers.back());
  }

  std::vector<llvm::Value*> allocas_for_returned_scalars;
  if (!return_shape.IsTuple()) {
    allocas_for_returned_scalars.push_back(return_buffer);
  } else {
    allocas_for_returned_scalars =
        llvm_ir::EmitTupleAllocasAtFunctionEntry(return_shape, &b_);
    llvm_ir::IrArray tuple_array(return_buffer, return_shape);

    EmitTuple(tuple_array, allocas_for_returned_scalars, &b_);
  }

  TF_RETURN_IF_ERROR(EmitCallToNestedComputation(computation, parameter_buffers,
                                                 return_buffer));

  std::vector<llvm::Value*> returned_scalars;
  returned_scalars.reserve(allocas_for_returned_scalars.size());
  for (llvm::Value* addr : allocas_for_returned_scalars) {
    returned_scalars.push_back(Load(addr));
  }
  return returned_scalars;
}

std::vector<llvm_ir::IrArray> IrEmitter::ConstructIrArrayForOutputs(
    const HloInstruction& hlo) {
  std::vector<llvm_ir::IrArray> output_arrays;
  if (hlo.shape().IsTuple()) {
    int64 num_outputs = ShapeUtil::TupleElementCount(hlo.shape());
    output_arrays.reserve(num_outputs);
    for (int64 i = 0; i < num_outputs; ++i) {
      output_arrays.push_back(GetIrArray(hlo, hlo, {i}));
    }
  } else {
    output_arrays.push_back(GetIrArray(hlo, hlo));
  }
  return output_arrays;
}

void IrEmitter::BindFusionArguments(const HloInstruction* fusion,
                                    FusedIrEmitter* fused_emitter) {
  for (int i = 0; i < fusion->operand_count(); i++) {
    const HloInstruction* operand = fusion->operand(i);
    fused_emitter->BindGenerator(
        fusion->fused_parameter(i),
        [this, operand, fusion](llvm_ir::IrArray::Index index) {
          return GetIrArray(*operand, *fusion)
              .EmitReadArrayElement(index, &b_, operand->name());
        });
  }
}

}  // namespace gpu
}  // namespace xla
