clang-p2996/mlir/lib/Dialect/SCF/Transforms/BufferizableOpInterfaceImpl.cpp

//===- BufferizableOpInterfaceImpl.cpp - Impl. of BufferizableOpInterface -===//
//
// 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
//
//===----------------------------------------------------------------------===//

#include "mlir/Dialect/SCF/BufferizableOpInterfaceImpl.h"

#include "mlir/Dialect/Bufferization/IR/BufferizableOpInterface.h"
#include "mlir/Dialect/Bufferization/IR/Bufferization.h"
#include "mlir/Dialect/Bufferization/Transforms/OneShotAnalysis.h"
#include "mlir/Dialect/SCF/SCF.h"
#include "mlir/IR/Dialect.h"
#include "mlir/IR/Operation.h"
#include "mlir/IR/PatternMatch.h"

using namespace mlir;
using namespace mlir::bufferization;
using namespace mlir::scf;

namespace mlir {
namespace scf {
namespace {

// bufferization.to_memref is not allowed to change the rank.
static void ensureToMemrefOpIsValid(Value tensor, Type memrefType) {
#ifndef NDEBUG
  auto rankedTensorType = tensor.getType().dyn_cast<RankedTensorType>();
  assert((!rankedTensorType || (memrefType.cast<MemRefType>().getRank() ==
                                rankedTensorType.getRank())) &&
         "to_memref would be invalid: mismatching ranks");
#endif
}

/// Bufferization of scf.execute_region. Can be analyzed, but bufferization not
/// fully implemented at the moment.
struct ExecuteRegionOpInterface
    : public BufferizableOpInterface::ExternalModel<ExecuteRegionOpInterface,
                                                    scf::ExecuteRegionOp> {
  SmallVector<OpOperand *>
  getAliasingOpOperand(Operation *op, OpResult opResult,
                       const AnalysisState &state) const {
    // ExecuteRegionOps do not have tensor OpOperands. The yielded value can be
    // any SSA value that is in scope. To allow for use-def chain traversal
    // through ExecuteRegionOps in the analysis, the corresponding yield value
    // is considered to be aliasing with the result.
    auto executeRegionOp = cast<scf::ExecuteRegionOp>(op);
    size_t resultNum = std::distance(op->getOpResults().begin(),
                                     llvm::find(op->getOpResults(), opResult));
    // TODO: Support multiple blocks.
    assert(executeRegionOp.getRegion().getBlocks().size() == 1 &&
           "expected exactly 1 block");
    auto yieldOp = dyn_cast<scf::YieldOp>(
        executeRegionOp.getRegion().front().getTerminator());
    assert(yieldOp && "expected scf.yield terminator in scf.execute_region");
    return {&yieldOp->getOpOperand(resultNum)};
  }

  // TODO: For better bufferization results, this could return `true` only if
  // there is a memory write in the region.
  bool isMemoryWrite(Operation *op, OpResult opResult,
                     const AnalysisState &state) const {
    // Similar to scf.if, results of this op are always considered memory writes
    // in the analysis. This is a useful pattern for all ops that have tensor
    // OpResults but no tensor OpOperands. By default, `isMemoryWrite` is
    // implemented in terms of `bufferizesToMemoryWrite`, which does not work on
    // ops without OpOperands.
    return true;
  }

  LogicalResult bufferize(Operation *op, RewriterBase &rewriter,
                          BufferizationState &state) const {
    auto executeRegionOp = cast<scf::ExecuteRegionOp>(op);

    // Compute new result types.
    SmallVector<Type> newResultTypes;
    for (Type type : executeRegionOp->getResultTypes()) {
      if (auto tensorType = type.dyn_cast<TensorType>()) {
        newResultTypes.push_back(getMemRefType(tensorType, state.getOptions()));
      } else {
        newResultTypes.push_back(type);
      }
    }

    // Create new op and move over region.
    auto newOp =
        rewriter.create<scf::ExecuteRegionOp>(op->getLoc(), newResultTypes);
    newOp.getRegion().takeBody(executeRegionOp.getRegion());

    // Update terminator.
    assert(newOp.getRegion().getBlocks().size() == 1 &&
           "only 1 block supported");
    Block *newBlock = &newOp.getRegion().front();
    auto yieldOp = cast<scf::YieldOp>(newBlock->getTerminator());
    rewriter.setInsertionPoint(yieldOp);
    SmallVector<Value> newYieldValues;
    for (const auto &it : llvm::enumerate(yieldOp.getResults())) {
      Value val = it.value();
      if (val.getType().isa<TensorType>()) {
        newYieldValues.push_back(rewriter.create<bufferization::ToMemrefOp>(
            yieldOp.getLoc(), newResultTypes[it.index()], val));
      } else {
        newYieldValues.push_back(val);
      }
    }
    rewriter.replaceOpWithNewOp<scf::YieldOp>(yieldOp, newYieldValues);

    // Update all uses of the old op.
    rewriter.setInsertionPointAfter(newOp);
    SmallVector<Value> newResults;
    for (const auto &it : llvm::enumerate(executeRegionOp->getResultTypes())) {
      if (it.value().isa<TensorType>()) {
        newResults.push_back(rewriter.create<bufferization::ToTensorOp>(
            executeRegionOp.getLoc(), newOp->getResult(it.index())));
      } else {
        newResults.push_back(newOp->getResult(it.index()));
      }
    }

    // Replace old op.
    rewriter.replaceOp(executeRegionOp, newResults);

    return success();
  }

  BufferRelation bufferRelation(Operation *op, OpResult opResult,
                                const AnalysisState &state) const {
    return BufferRelation::Equivalent;
  }
};

/// Bufferization of scf.if. Replace with a new scf.if that yields memrefs.
struct IfOpInterface
    : public BufferizableOpInterface::ExternalModel<IfOpInterface, scf::IfOp> {
  SmallVector<OpOperand *>
  getAliasingOpOperand(Operation *op, OpResult opResult,
                       const AnalysisState &state) const {
    // IfOps do not have tensor OpOperands. The yielded value can be any SSA
    // value that is in scope. To allow for use-def chain traversal through
    // IfOps in the analysis, both corresponding yield values from the then/else
    // branches are considered to be aliasing with the result.
    auto ifOp = cast<scf::IfOp>(op);
    size_t resultNum = std::distance(op->getOpResults().begin(),
                                     llvm::find(op->getOpResults(), opResult));
    return {&ifOp.thenYield()->getOpOperand(resultNum),
            &ifOp.elseYield()->getOpOperand(resultNum)};
  }

  // TODO: For better bufferization results, this could return `true` only if
  // there is a memory write in one (or both) of the branches. Since this is not
  // allowed at the moment, we should never encounter scf.ifs that yield
  // unmodified tensors. Such scf.yield ops could just fold away.
  bool isMemoryWrite(Operation *op, OpResult opResult,
                     const AnalysisState &state) const {
    // IfOp results are always considered memory writes in the analysis. This
    // design decision simplifies the analysis considerably. E.g., consider the
    // following test case:
    //
    // %0 = "some_writing_op" : tensor<?xf32>
    // %r = scf.if %c -> (tensor<?xf32>) {
    //   scf.yield %0
    // } else {
    //   %1 = "another_writing_op"(%0) : tensor<?xf32>
    // }
    // "some_reading_op"(%r)
    //
    // "another_writing_op" in the above example should be able to bufferize
    // inplace in the absence of another read of %0. However, if the scf.if op
    // would not be considered a "write", the analysis would detect the
    // following conflict:
    //
    // * read = some_reading_op
    // * lastWrite = %0  (Note: The last write of %r would be a set: {%0, %1}.)
    // * conflictingWrite = %1
    //
    // For more details, check the "scf.IfOp" section of the design document.
    return true;
  }

  LogicalResult bufferize(Operation *op, RewriterBase &rewriter,
                          BufferizationState &state) const {
    auto ifOp = cast<scf::IfOp>(op);

    // Compute new types of the bufferized scf.if op.
    SmallVector<Type> newTypes;
    for (Type returnType : ifOp->getResultTypes()) {
      if (auto tensorType = returnType.dyn_cast<TensorType>()) {
        newTypes.push_back(getMemRefType(tensorType, state.getOptions()));
      } else {
        newTypes.push_back(returnType);
      }
    }

    // Create new op.
    auto newIfOp =
        rewriter.create<scf::IfOp>(ifOp.getLoc(), newTypes, ifOp.getCondition(),
                                   /*withElseRegion=*/true);

    // Remove terminators.
    if (!newIfOp.thenBlock()->empty()) {
      rewriter.eraseOp(newIfOp.thenBlock()->getTerminator());
      rewriter.eraseOp(newIfOp.elseBlock()->getTerminator());
    }

    // Move over then/else blocks.
    rewriter.mergeBlocks(ifOp.thenBlock(), newIfOp.thenBlock());
    rewriter.mergeBlocks(ifOp.elseBlock(), newIfOp.elseBlock());

    // Update scf.yield of new then-block.
    auto thenYieldOp = cast<scf::YieldOp>(newIfOp.thenBlock()->getTerminator());
    rewriter.setInsertionPoint(thenYieldOp);
    SmallVector<Value> thenYieldValues;
    for (OpOperand &operand : thenYieldOp->getOpOperands()) {
      if (operand.get().getType().isa<TensorType>()) {
        ensureToMemrefOpIsValid(operand.get(),
                                newTypes[operand.getOperandNumber()]);
        Value toMemrefOp = rewriter.create<bufferization::ToMemrefOp>(
            operand.get().getLoc(), newTypes[operand.getOperandNumber()],
            operand.get());
        operand.set(toMemrefOp);
      }
    }

    // Update scf.yield of new else-block.
    auto elseYieldOp = cast<scf::YieldOp>(newIfOp.elseBlock()->getTerminator());
    rewriter.setInsertionPoint(elseYieldOp);
    SmallVector<Value> elseYieldValues;
    for (OpOperand &operand : elseYieldOp->getOpOperands()) {
      if (operand.get().getType().isa<TensorType>()) {
        ensureToMemrefOpIsValid(operand.get(),
                                newTypes[operand.getOperandNumber()]);
        Value toMemrefOp = rewriter.create<bufferization::ToMemrefOp>(
            operand.get().getLoc(), newTypes[operand.getOperandNumber()],
            operand.get());
        operand.set(toMemrefOp);
      }
    }

    // Replace op results.
    replaceOpWithBufferizedValues(rewriter, op, newIfOp->getResults());

    return success();
  }

  BufferRelation bufferRelation(Operation *op, OpResult opResult,
                                const AnalysisState &state) const {
    // IfOp results are equivalent to their corresponding yield values if both
    // yield values are equivalent to each other.
    auto bufferizableOp = cast<BufferizableOpInterface>(op);
    SmallVector<OpOperand *> yieldValues =
        bufferizableOp.getAliasingOpOperand(opResult, state);
    assert(yieldValues.size() == 2 && "expected 2 yield values");
    bool equivalentYields = state.areEquivalentBufferizedValues(
        yieldValues[0]->get(), yieldValues[1]->get());
    return equivalentYields ? BufferRelation::Equivalent : BufferRelation::None;
  }
};

/// Bufferization of scf.for. Replace with a new scf.for that operates on
/// memrefs.
struct ForOpInterface
    : public BufferizableOpInterface::ExternalModel<ForOpInterface,
                                                    scf::ForOp> {
  bool bufferizesToMemoryRead(Operation *op, OpOperand &opOperand,
                              const AnalysisState &state) const {
    // scf::ForOp alone doesn't bufferize to a memory read, one of the uses of
    // its matching bbArg may.
    auto forOp = cast<scf::ForOp>(op);
    return state.isValueRead(forOp.getRegionIterArgForOpOperand(opOperand));
  }

  bool bufferizesToMemoryWrite(Operation *op, OpOperand &opOperand,
                               const AnalysisState &state) const {
    // Tensor iter_args of scf::ForOps are always considered as a write.
    return true;
  }

  SmallVector<OpResult> getAliasingOpResult(Operation *op, OpOperand &opOperand,
                                            const AnalysisState &state) const {
    auto forOp = cast<scf::ForOp>(op);
    return {forOp.getResultForOpOperand(opOperand)};
  }

  BufferRelation bufferRelation(Operation *op, OpResult opResult,
                                const AnalysisState &state) const {
    // ForOp results are equivalent to their corresponding init_args if the
    // corresponding iter_args and yield values are equivalent.
    auto forOp = cast<scf::ForOp>(op);
    OpOperand &forOperand = forOp.getOpOperandForResult(opResult);
    auto bbArg = forOp.getRegionIterArgForOpOperand(forOperand);
    auto yieldOp =
        cast<scf::YieldOp>(forOp.getLoopBody().front().getTerminator());
    bool equivalentYield = state.areEquivalentBufferizedValues(
        bbArg, yieldOp->getOperand(opResult.getResultNumber()));
    return equivalentYield ? BufferRelation::Equivalent : BufferRelation::None;
  }

  bool isWritable(Operation *op, Value value,
                  const AnalysisState &state) const {
    // Interestingly, scf::ForOp's bbArg can **always** be viewed
    // inplace from the perspective of ops nested under:
    //   1. Either the matching iter operand is not bufferized inplace and an
    //      alloc + optional copy makes the bbArg itself inplaceable.
    //   2. Or the matching iter operand is bufferized inplace and bbArg just
    //      bufferizes to that too.
    return true;
  }

  LogicalResult bufferize(Operation *op, RewriterBase &rewriter,
                          BufferizationState &state) const {
    auto forOp = cast<scf::ForOp>(op);
    auto bufferizableOp = cast<BufferizableOpInterface>(op);
    Block *oldLoopBody = &forOp.getLoopBody().front();

    // Indices of all iter_args that have tensor type. These are the ones that
    // are bufferized.
    DenseSet<int64_t> indices;
    // For every yielded value, is the value equivalent to its corresponding
    // bbArg?
    SmallVector<bool> equivalentYields;
    for (const auto &it : llvm::enumerate(forOp.getInitArgs())) {
      if (it.value().getType().isa<TensorType>()) {
        indices.insert(it.index());
        BufferRelation relation = bufferizableOp.bufferRelation(
            forOp->getResult(it.index()), state.getAnalysisState());
        equivalentYields.push_back(relation == BufferRelation::Equivalent);
      } else {
        equivalentYields.push_back(false);
      }
    }

    // Given a range of values, apply `func` to those marked in `indices`.
    // Otherwise, store the unmodified value in the result vector.
    auto convert = [&](ValueRange values,
                       llvm::function_ref<Value(Value, int64_t)> func) {
      SmallVector<Value> result;
      for (const auto &it : llvm::enumerate(values)) {
        size_t idx = it.index();
        Value val = it.value();
        result.push_back(indices.contains(idx) ? func(val, idx) : val);
      }
      return result;
    };

    // Construct a new scf.for op with memref instead of tensor values.
    SmallVector<Value> initArgs;
    for (OpOperand &opOperand : forOp.getIterOpOperands()) {
      if (opOperand.get().getType().isa<TensorType>()) {
        FailureOr<Value> resultBuffer = state.getBuffer(rewriter, opOperand);
        if (failed(resultBuffer))
          return failure();
        initArgs.push_back(*resultBuffer);
      } else {
        initArgs.push_back(opOperand.get());
      }
    }
    auto newForOp = rewriter.create<scf::ForOp>(
        forOp.getLoc(), forOp.getLowerBound(), forOp.getUpperBound(),
        forOp.getStep(), initArgs);
    Block *loopBody = &newForOp.getLoopBody().front();

    // Set up new iter_args. The loop body uses tensors, so wrap the (memref)
    // iter_args of the new loop in ToTensorOps.
    rewriter.setInsertionPointToStart(loopBody);
    SmallVector<Value> iterArgs =
        convert(newForOp.getRegionIterArgs(), [&](Value val, int64_t index) {
          return rewriter.create<bufferization::ToTensorOp>(val.getLoc(), val);
        });
    iterArgs.insert(iterArgs.begin(), newForOp.getInductionVar());

    // Erase terminator if present.
    if (iterArgs.size() == 1)
      rewriter.eraseOp(loopBody->getTerminator());

    // Move loop body to new loop.
    rewriter.mergeBlocks(oldLoopBody, loopBody, iterArgs);

    // Update scf.yield of new loop.
    auto yieldOp = cast<scf::YieldOp>(loopBody->getTerminator());
    rewriter.setInsertionPoint(yieldOp);
    SmallVector<Value> yieldValues =
        convert(yieldOp.getResults(), [&](Value val, int64_t index) {
          ensureToMemrefOpIsValid(val, initArgs[index].getType());
          Value yieldedVal = rewriter.create<bufferization::ToMemrefOp>(
              val.getLoc(), initArgs[index].getType(), val);

          if (equivalentYields[index])
            // Yielded value is equivalent to the corresponding iter_arg bbArg.
            // Yield the value directly. Most IR should be like that. Everything
            // else must be resolved with copies and is potentially inefficient.
            // By default, such problematic IR would already have been rejected
            // during `verifyAnalysis`, unless `allow-return-allocs`.
            return yieldedVal;

          // It is not certain that the yielded value and the iter_arg bbArg
          // have the same buffer. Allocate a new buffer and copy. The yielded
          // buffer will get deallocated by `deallocateBuffers`.

          // TODO: There are cases in which it is not neccessary to return a new
          // buffer allocation. E.g., when equivalent values are yielded in a
          // different order. This could be resolved with copies.
          Optional<Value> yieldedAlloc = state.createAlloc(
              rewriter, val.getLoc(), yieldedVal, /*deallocMemref=*/false);
          // TODO: We should rollback, but for now just assume that this always
          // succeeds.
          assert(yieldedAlloc.hasValue() && "could not create alloc");
          LogicalResult copyStatus =
              bufferization::createMemCpy(rewriter, val.getLoc(), yieldedVal,
                                          *yieldedAlloc, state.getOptions());
          (void)copyStatus;
          assert(succeeded(copyStatus) && "could not create memcpy");
          return *yieldedAlloc;
        });
    yieldOp.getResultsMutable().assign(yieldValues);

    // Replace loop results.
    replaceOpWithBufferizedValues(rewriter, op, newForOp->getResults());

    return success();
  }

  /// Assert that yielded values of an scf.for op are equivalent to their
  /// corresponding bbArgs. Otherwise, an alloc+copy are inserted and yielded
  /// from the loop. This could be a performance problem, so it must be
  /// explicitly activated with `alloc-return-allocs`.
  LogicalResult verifyAnalysis(Operation *op,
                               const AnalysisState &state) const {
    const auto &options =
        static_cast<const OneShotBufferizationOptions &>(state.getOptions());
    if (options.allowReturnAllocs)
      return success();

    auto forOp = cast<scf::ForOp>(op);
    auto yieldOp =
        cast<scf::YieldOp>(forOp.getLoopBody().front().getTerminator());
    for (OpOperand &operand : yieldOp->getOpOperands()) {
      auto tensorType = operand.get().getType().dyn_cast<TensorType>();
      if (!tensorType)
        continue;

      OpOperand &forOperand = forOp.getOpOperandForResult(
          forOp->getResult(operand.getOperandNumber()));
      auto bbArg = forOp.getRegionIterArgForOpOperand(forOperand);
      // Note: This is overly strict. We should check for aliasing bufferized
      // values. But we don't have a "must-alias" analysis yet.
      if (!state.areEquivalentBufferizedValues(operand.get(), bbArg))
        return yieldOp->emitError()
               << "Yield operand #" << operand.getOperandNumber()
               << " does not bufferize to a buffer that is aliasing the "
                  "matching enclosing scf::for operand";
    }
    return success();
  }
};

/// Bufferization of scf.yield. Bufferized as part of their enclosing ops, so
/// this is for analysis only.
struct YieldOpInterface
    : public BufferizableOpInterface::ExternalModel<YieldOpInterface,
                                                    scf::YieldOp> {
  bool bufferizesToMemoryRead(Operation *op, OpOperand &opOperand,
                              const AnalysisState &state) const {
    return true;
  }

  bool bufferizesToMemoryWrite(Operation *op, OpOperand &opOperand,
                               const AnalysisState &state) const {
    return false;
  }

  SmallVector<OpResult> getAliasingOpResult(Operation *op, OpOperand &opOperand,
                                            const AnalysisState &state) const {
    if (isa<scf::IfOp>(op->getParentOp()))
      return {op->getParentOp()->getResult(opOperand.getOperandNumber())};
    if (isa<scf::ExecuteRegionOp>(op->getParentOp()))
      return {op->getParentOp()->getResult(opOperand.getOperandNumber())};
    return {};
  }

  bool mustBufferizeInPlace(Operation *op, OpOperand &opOperand,
                            const AnalysisState &state) const {
    // Yield operands always bufferize inplace. Otherwise, an alloc + copy
    // may be generated inside the block. We should not return/yield allocations
    // when possible.
    return true;
  }

  LogicalResult bufferize(Operation *op, RewriterBase &rewriter,
                          BufferizationState &state) const {
    auto yieldOp = cast<scf::YieldOp>(op);
    if (!isa<scf::ExecuteRegionOp, scf::IfOp, scf::ForOp>(
            yieldOp->getParentOp()))
      return yieldOp->emitError("unsupported scf::YieldOp parent");
    return success();
  }
};

} // namespace
} // namespace scf
} // namespace mlir

void mlir::scf::registerBufferizableOpInterfaceExternalModels(
    DialectRegistry &registry) {
  registry.addExtension(+[](MLIRContext *ctx, scf::SCFDialect *dialect) {
    ExecuteRegionOp::attachInterface<ExecuteRegionOpInterface>(*ctx);
    ForOp::attachInterface<ForOpInterface>(*ctx);
    IfOp::attachInterface<IfOpInterface>(*ctx);
    YieldOp::attachInterface<YieldOpInterface>(*ctx);
  });
}