llvm-project/mlir/test/lib/Transforms/TestBufferPlacement.cpp

//===- TestBufferPlacement.cpp - Test for buffer placement ------*- C++ -*-===//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//
//===----------------------------------------------------------------------===//
//
// This file implements logic for testing buffer placement including its
// utility converters.
//
//===----------------------------------------------------------------------===//

#include "TestDialect.h"
#include "mlir/Conversion/StandardToLLVM/ConvertStandardToLLVM.h"
#include "mlir/Dialect/Linalg/IR/LinalgOps.h"
#include "mlir/IR/Function.h"
#include "mlir/IR/Operation.h"
#include "mlir/Pass/Pass.h"
#include "mlir/Pass/PassManager.h"
#include "mlir/Transforms/BufferPlacement.h"

using namespace mlir;

namespace {
/// This pass tests the computeAllocPosition helper method and buffer assignment
/// operation converters. Furthermore, this pass converts linalg operations on
/// tensors to linalg operations on buffers to prepare them for the
/// BufferPlacement pass that can be applied afterwards.
/// `allowMemrefFunctionResults` informs the buffer placement to allow functions
/// that have memref typed results. Buffer assignment operation converters will
/// be adapted respectively. It will also allow memref typed results to escape
/// from the deallocation.
template <bool allowMemrefFunctionResults>
struct TestBufferPlacementPreparationPass
    : mlir::PassWrapper<
          TestBufferPlacementPreparationPass<allowMemrefFunctionResults>,
          OperationPass<ModuleOp>> {

  /// Converts tensor-type generic linalg operations to memref ones using
  /// buffer assignment.
  /// TODO: Avoid the copy-pasta by exposing the pattern from BufferPlacement.h
  /// This is limited by not wanting BufferPlacement to depend on Linalg. Fixing
  /// this probably requires an OpConversionPattern over generic Operation*. For
  /// now only RewritePattern but not ConversionPattern allow this.

  class GenericOpConverter
      : public BufferAssignmentOpConversionPattern<linalg::GenericOp> {
  public:
    using BufferAssignmentOpConversionPattern<
        linalg::GenericOp>::BufferAssignmentOpConversionPattern;

    LogicalResult
    matchAndRewrite(linalg::GenericOp op, ArrayRef<Value> operands,
                    ConversionPatternRewriter &rewriter) const final {
      linalg::GenericOpAdaptor adaptor(operands,
                                       op.getOperation()->getAttrDictionary());

      // TODO: support ops with reduction.
      if (!op.init_tensors().empty())
        return failure();

      // All inputs need to be turned into buffers first. Until then, bail out.
      if (llvm::any_of(adaptor.inputs(), [](Value in) {
            return !in.getType().isa<MemRefType>();
          }))
        return failure();

      Location loc = op.getLoc();
      SmallVector<Value, 2> outputBuffers, newOutputBuffers;
      outputBuffers.assign(adaptor.output_buffers().begin(),
                           adaptor.output_buffers().end());
      newOutputBuffers.reserve(op.getNumOutputs());
      newOutputBuffers.append(adaptor.output_buffers().begin(),
                              adaptor.output_buffers().end());

      // Update all types to memref types.
      for (Type t : op.getResultTypes()) {
        auto type = t.cast<ShapedType>();
        if (!type.hasStaticShape())
          return rewriter.notifyMatchFailure(
              op, "dynamic shapes not currently supported");
        auto memrefType =
            MemRefType::get(type.getShape(), type.getElementType());
        auto alloc = rewriter.create<AllocOp>(loc, memrefType);
        newOutputBuffers.push_back(alloc);
      }

      // Generate a new linalg operation that works on buffers.
      auto linalgOp = rewriter.create<linalg::GenericOp>(
          loc,
          /*resultTensorTypes=*/ArrayRef<Type>{},
          /*inputs=*/adaptor.inputs(),
          /*outputBuffers=*/newOutputBuffers,
          /*initTensors=*/ValueRange{}, op.indexing_maps(), op.iterator_types(),
          op.docAttr(), op.library_callAttr(), op.symbol_sourceAttr());

      // Create a new block in the region of the new Generic Op.
      Block &oldBlock = op.getRegion().front();
      Region &newRegion = linalgOp.region();
      Block *newBlock = rewriter.createBlock(&newRegion, newRegion.begin(),
                                             oldBlock.getArgumentTypes());

      // Add the result arguments to the new block.
      for (Value v : newOutputBuffers)
        newBlock->addArgument(v.getType().cast<MemRefType>().getElementType());

      // Clone the body of the old block to the new block.
      BlockAndValueMapping mapping;
      for (unsigned i = 0; i < oldBlock.getNumArguments(); i++)
        mapping.map(oldBlock.getArgument(i), newBlock->getArgument(i));

      OpBuilder::InsertionGuard guard(rewriter);
      rewriter.setInsertionPointToEnd(newBlock);
      for (auto &op : oldBlock.getOperations()) {
        Operation *clonedOp = rewriter.clone(op, mapping);
        mapping.map(op.getResults(), clonedOp->getResults());
      }

      // Replace the results of the old op with the new output buffers.
      rewriter.replaceOp(op, newOutputBuffers);
      return success();
    }
  };

  void populateTensorLinalgToBufferLinalgConversionPattern(
      MLIRContext *context, BufferAssignmentTypeConverter *converter,
      OwningRewritePatternList *patterns) {
    populateWithBufferAssignmentOpConversionPatterns<
        mlir::ReturnOp, mlir::ReturnOp, linalg::CopyOp>(context, converter,
                                                        patterns);
    patterns->insert<GenericOpConverter>(context, converter);
  }

  void getDependentDialects(DialectRegistry &registry) const override {
    registry.insert<TestDialect>();
    registry.insert<linalg::LinalgDialect>();
  }

  void runOnOperation() override {
    MLIRContext &context = this->getContext();
    ConversionTarget target(context);
    BufferAssignmentTypeConverter converter;

    // Mark all Standard operations legal.
    target.addLegalDialect<StandardOpsDialect>();
    target.addLegalOp<MakeTupleOp>();
    target.addLegalOp<GetTupleElementOp>();
    target.addLegalOp<ModuleOp>();
    target.addLegalOp<ModuleTerminatorOp>();

    // Mark all Linalg operations illegal as long as they work on tensors.
    auto isLegalOperation = [&](Operation *op) {
      return converter.isLegal(op);
    };
    target.addDynamicallyLegalDialect<linalg::LinalgDialect>(isLegalOperation);

    // Mark Standard Return operations illegal as long as one operand is tensor.
    target.addDynamicallyLegalOp<mlir::ReturnOp>([&](mlir::ReturnOp returnOp) {
      return converter.isLegal(returnOp.getOperandTypes());
    });

    // Mark Standard Call Operation illegal as long as it operates on tensor.
    target.addDynamicallyLegalOp<mlir::CallOp>(
        [&](mlir::CallOp callOp) { return converter.isLegal(callOp); });

    // Mark the function whose arguments are in tensor-type illegal.
    target.addDynamicallyLegalOp<FuncOp>([&](FuncOp funcOp) {
      return converter.isSignatureLegal(funcOp.getType()) &&
             converter.isLegal(&funcOp.getBody());
    });

    auto kind = allowMemrefFunctionResults
                    ? BufferAssignmentTypeConverter::KeepAsFunctionResult
                    : BufferAssignmentTypeConverter::AppendToArgumentsList;
    converter.setResultConversionKind<RankedTensorType, MemRefType>(kind);
    converter.setResultConversionKind<UnrankedTensorType, UnrankedMemRefType>(
        kind);

    converter.addDecomposeTypeConversion(
        [](TupleType tupleType, SmallVectorImpl<Type> &types) {
          tupleType.getFlattenedTypes(types);
          return success();
        });

    converter.addArgumentMaterialization(
        [](OpBuilder &builder, TupleType resultType, ValueRange inputs,
           Location loc) -> Optional<Value> {
          if (inputs.size() == 1)
            return llvm::None;
          TypeRange TypeRange = inputs.getTypes();
          SmallVector<Type, 2> types(TypeRange.begin(), TypeRange.end());
          TupleType tuple = TupleType::get(types, builder.getContext());
          mlir::Value value = builder.create<MakeTupleOp>(loc, tuple, inputs);
          return value;
        });

    converter.addDecomposeValueConversion([](OpBuilder &builder, Location loc,
                                             TupleType resultType, Value value,
                                             SmallVectorImpl<Value> &values) {
      for (unsigned i = 0, e = resultType.size(); i < e; ++i) {
        Value res = builder.create<GetTupleElementOp>(
            loc, resultType.getType(i), value, builder.getI32IntegerAttr(i));
        values.push_back(res);
      }
      return success();
    });

    OwningRewritePatternList patterns;
    populateTensorLinalgToBufferLinalgConversionPattern(&context, &converter,
                                                        &patterns);
    if (failed(applyFullConversion(this->getOperation(), target, patterns)))
      this->signalPassFailure();
  };
};
} // end anonymous namespace

namespace mlir {
void registerTestBufferPlacementPreparationPass() {
  PassRegistration<
      TestBufferPlacementPreparationPass</*allowMemrefFunctionResults=*/false>>(
      "test-buffer-placement-preparation",
      "Tests buffer placement helper methods including its "
      "operation-conversion patterns");
}

void registerTestPreparationPassWithAllowedMemrefResults() {
  PassRegistration<
      TestBufferPlacementPreparationPass</*allowMemrefFunctionResults=*/true>>(
      "test-buffer-placement-preparation-with-allowed-memref-results",
      "Tests the helper operation converters of buffer placement for allowing "
      "functions to have memref typed results.");
}
} // end namespace mlir