# Target Aware Conversion (TAC)

Different hardwares have different capabilities and restrictions.

TAC is designed to leverage hardwares' capabilities to:

*   Perform device-specific optimizations (such as unsupported ops lowering,
    layout transformations, etc.)
*   Graph partitioning based on the hardware costs modeling.
*   It supports general import/export where you can hook your own

	Types          Types
	lowerBlock     blockRewriter  // block lowering function, first round
	lowerValue     valueRewriter  // value lowering function, first round
	lateLowerBlock blockRewriter  // block lowering function that needs to be run after the first round; only used on some architectures
	lateLowerValue valueRewriter  // value lowering function that needs to be run after the first round; only used on some architectures

// Lowers some of the TensorFlow operations that can be represented using other
// TensorFlow operations.
struct LowerTF : public impl::TestTensorFlowLowerTFPassBase<LowerTF> {
  void runOnOperation() override {
    // Add lowering patterns to the list.
    RewritePatternSet patterns(&getContext());
    if (default_patterns_) {
      mlir::TF::PopulateLoweringTFPatterns(&getContext(), &patterns);
    }
    if (pre_hlo_patterns_) {

bool IsOpAllowedTf2xlaFallback(const TypeID& type_id);

// Whether this type is Preferred to use a TF2XLA fallback kernel when using
// the MLIR bridge. If this is true, then the TF2XLA fallback kernel will be
// used over the MLIR lowering.
bool IsOpAllowedTf2xlaPreferred(const TypeID& type_id);

}  // namespace mhlo
}  // namespace mlir

struct DecomposeResourceOpsPass
    : public impl::DecomposeResourceOpsPassBase<DecomposeResourceOpsPass> {
  void runOnOperation() override {
    // Add lowering patterns to the list.
    RewritePatternSet patterns(&getContext());
    TF::PopulateDecomposeResourceOpsPatterns(&getContext(), &patterns);

    if (failed(applyPatternsAndFoldGreedily(getOperation(),

/// Creates a pass that converts constants followed by a qbarrier to a
/// constant whose value is quantized. This is typically one of the last
/// passes done when lowering to express actual quantized arithmetic in a
/// low level representation. Because it modifies the constant, it is
/// destructive and cannot be undone.
std::unique_ptr<OperationPass<func::FuncOp>> createConvertConstPass();

    // IDE tries to acquire its bytecode via the index, however,
    // the index doesn't cover classfiles from compiler output,
    // so the lowering fails.
    //
    // To solve the problem, we need to find all delegated properties with inline accessors
    // reachable from files that will be compiled, and include files these inline accessors

  // CHECK: mlrt.await_handle [[handle_1]]
  mlrt.await_handle %handle_0
  mlrt.await_handle %handle_1

  // CHECK: return [[result]]
  return %result : tensor<i32>
}

// -----

// Test lowering tf.If

func.func @then(%x: tensor<i32>, %y: tensor<i32>) -> tensor<i32> {
  return %x: tensor<i32>
}

func.func @else(%x: tensor<i32>, %y: tensor<i32>) -> tensor<i32> {
  return %y: tensor<i32>
}

namespace mlir {
namespace tf_device {

// The TensorFlow Device dialect.
//
// This dialect contains operations to describe/launch computations on devices.
// These operations do not map 1-1 to TensorFlow ops and requires a lowering
// pass later to transform them into Compile/Run op pairs, like XlaCompile and
// XlaRun.
class TensorFlowDeviceDialect : public Dialect {
 public:
  static StringRef getDialectNamespace() { return "tf_device"; }

namespace mlir {
namespace tf_test {

// Returns test pass for variable freezing.
std::unique_ptr<OperationPass<ModuleOp>> CreateFreezeVariableTestPass();

// Test pass for applying TF->TF lowering patterns.
std::unique_ptr<OperationPass<func::FuncOp>> CreateTestTFLowerTFPass();

// Test passes for visitor util.
std::unique_ptr<OperationPass<func::FuncOp>> CreateTestVisitorUtilPass();