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Bufferize.cpp
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Bufferize.cpp
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//===- Bufferize.cpp - Bufferization of linalg ops ------------------===//
//
// 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/Transforms/Bufferize.h"
#include "PassDetail.h"
#include "mlir/Dialect/Linalg/IR/LinalgOps.h"
#include "mlir/Dialect/Linalg/Passes.h"
#include "mlir/Dialect/Linalg/Transforms/Transforms.h"
#include "mlir/Dialect/Linalg/Utils/Utils.h"
#include "mlir/Dialect/StandardOps/Transforms/Passes.h"
#include "mlir/Dialect/Vector/VectorOps.h"
#include "mlir/IR/BuiltinDialect.h"
#include "mlir/IR/Operation.h"
#include "mlir/Pass/Pass.h"
using namespace ::mlir;
using namespace ::mlir::linalg;
static SmallVector<Value, 4> getDynOperands(Location loc, Value val,
OpBuilder &b) {
SmallVector<Value, 4> dynOperands;
auto shapedType = val.getType().cast<ShapedType>();
for (auto dim : llvm::enumerate(shapedType.getShape())) {
if (dim.value() == TensorType::kDynamicSize) {
dynOperands.push_back(b.create<DimOp>(loc, val, dim.index()));
}
}
return dynOperands;
}
static Value cloneMemref(Location loc, Value memref, OpBuilder &b) {
auto memrefType = memref.getType().cast<MemRefType>();
auto alloc =
b.create<AllocOp>(loc, memrefType, getDynOperands(loc, memref, b));
b.create<linalg::CopyOp>(loc, memref, alloc);
return alloc;
}
static LogicalResult
allocateBuffersForResults(Location loc, LinalgOp linalgOp,
linalg::GenericOpAdaptor &adaptor,
SmallVectorImpl<Value> &resultBuffers, OpBuilder &b) {
// Lazily compute loopRanges.
SmallVector<Range, 4> loopRanges;
// Allocate a buffer for every tensor result.
assert(linalgOp.getNumOutputs() == linalgOp->getNumResults());
for (auto en : llvm::enumerate(linalgOp->getResultTypes())) {
size_t resultIndex = en.index();
Type resultType = en.value();
auto tensorType = resultType.dyn_cast<RankedTensorType>();
if (tensorType == nullptr) {
linalgOp.emitOpError()
<< "tensor to buffer conversion expects ranked tensor results";
return failure();
}
auto tensorShape = tensorType.getShape();
auto memrefType = MemRefType::get(tensorShape, tensorType.getElementType());
Value resultTensor = adaptor.outputs()[resultIndex];
// Clone output buffers whose value is actually used.
if (linalgOp.payloadUsesValueFromOutputOperandIndex(resultIndex)) {
resultBuffers.push_back(cloneMemref(loc, resultTensor, b));
continue;
}
if (auto alloc = resultTensor.getDefiningOp<AllocOp>()) {
resultBuffers.push_back(resultTensor);
continue;
}
// Allocate buffers for statically-shaped results.
if (memrefType.hasStaticShape()) {
resultBuffers.push_back(b.create<AllocOp>(loc, memrefType));
continue;
}
resultBuffers.push_back(b.create<AllocOp>(
loc, memrefType, getDynOperands(loc, resultTensor, b)));
}
return success();
}
/// Specialization for `linalg::GenericOp` and `linalg::IndexedGenericOp`.
/// A pattern to convert Generic Linalg operations which work on tensors to
/// use buffers. BufferPlacement pass should be later used to move
/// Alloc operations to the correct positions and insert the missing Dealloc
/// operations in the correct places.
template <typename GenericOpTy>
static void
finalizeBufferAllocationForGenericOp(ConversionPatternRewriter &rewriter,
GenericOpTy genericOp, ValueRange inputs,
ValueRange outputs) {
// Generate a new linalg operation that works on buffers.
auto newGenericOp = rewriter.create<GenericOpTy>(
genericOp.getLoc(),
/*resultTensorTypes=*/llvm::None,
/*inputs=*/inputs,
/*outputs=*/outputs, genericOp.indexing_maps(),
genericOp.iterator_types(), genericOp.docAttr(),
genericOp.library_callAttr(), genericOp.sparseAttr());
// Create a new block in the region of the new Generic Op.
Block *oldBlock = genericOp.getBody();
Region &newRegion = newGenericOp.region();
Block *newBlock = rewriter.createBlock(&newRegion, newRegion.begin(),
oldBlock->getArgumentTypes());
// Clone the body of the old block to the new block.
BlockAndValueMapping mapping;
mapping.map(oldBlock->getArguments(), newBlock->getArguments());
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(genericOp, outputs);
}
/// Specialization for all other `linalg::LinalgOp`.
static void finalizeBufferAllocation(ConversionPatternRewriter &rewriter,
linalg::LinalgOp linalgOp,
ValueRange inputs, ValueRange outputs) {
assert(!isa<linalg::GenericOp>(linalgOp.getOperation()));
assert(!isa<linalg::IndexedGenericOp>(linalgOp.getOperation()));
SmallVector<Value, 8> newOperands = inputs;
newOperands.append(outputs.begin(), outputs.end());
auto otherOperands = linalgOp.getAssumedNonShapedOperands();
newOperands.append(otherOperands.begin(), otherOperands.end());
linalgOp.clone(rewriter, linalgOp.getLoc(),
/*resultTypes=*/ArrayRef<Type>{}, newOperands);
// Replace the results of the old op with the new output buffers.
rewriter.replaceOp(linalgOp, outputs);
}
//===----------------------------------------------------------------------===//
// Bufferization patterns.
//===----------------------------------------------------------------------===//
namespace {
/// Generic conversion pattern that matches any LinalgOp. This avoids template
/// instantiating one pattern for each LinalgOp.
class BufferizeInitTensorOp : public OpConversionPattern<InitTensorOp> {
public:
using OpConversionPattern<InitTensorOp>::OpConversionPattern;
LogicalResult
matchAndRewrite(InitTensorOp op, ArrayRef<Value> operands,
ConversionPatternRewriter &rewriter) const final {
linalg::InitTensorOpAdaptor adaptor(operands, op->getAttrDictionary());
rewriter.replaceOpWithNewOp<AllocOp>(
op, getTypeConverter()->convertType(op.getType()).cast<MemRefType>(),
adaptor.sizes());
return success();
}
};
/// Generic conversion pattern that matches any LinalgOp. This avoids template
/// instantiating one pattern for each LinalgOp.
class BufferizeAnyLinalgOp : public ConversionPattern {
public:
BufferizeAnyLinalgOp(TypeConverter &typeConverter)
: ConversionPattern(/*benefit=*/1, typeConverter, MatchAnyOpTypeTag()) {}
LogicalResult
matchAndRewrite(Operation *op, ArrayRef<Value> operands,
ConversionPatternRewriter &rewriter) const final {
LinalgOp linalgOp = dyn_cast<linalg::LinalgOp>(op);
if (!linalgOp)
return failure();
// We abuse the GenericOpAdaptor here.
// TODO: Manually create an Adaptor that captures inputs and outputs for all
// linalg::LinalgOp interface ops.
linalg::GenericOpAdaptor adaptor(operands, op->getAttrDictionary());
Location loc = linalgOp.getLoc();
SmallVector<Value, 2> newOutputBuffers;
if (failed(allocateBuffersForResults(loc, linalgOp, adaptor,
newOutputBuffers, rewriter))) {
linalgOp.emitOpError()
<< "Failed to allocate buffers for tensor results.";
return failure();
}
// Delegate to the linalg generic pattern.
if (auto genericOp = dyn_cast<linalg::GenericOp>(op)) {
finalizeBufferAllocationForGenericOp<GenericOp>(
rewriter, genericOp, adaptor.inputs(), newOutputBuffers);
return success();
}
// Delegate to the linalg indexed generic pattern.
if (auto genericOp = dyn_cast<linalg::IndexedGenericOp>(op)) {
finalizeBufferAllocationForGenericOp<IndexedGenericOp>(
rewriter, genericOp, adaptor.inputs(), newOutputBuffers);
return success();
}
finalizeBufferAllocation(rewriter, linalgOp, adaptor.inputs(),
newOutputBuffers);
return success();
}
};
// Extract int64_t values from the assumed ArrayAttr of IntegerAttr.
static SmallVector<int64_t, 4> extractFromI64ArrayAttr(Attribute attr) {
return llvm::to_vector<4>(
llvm::map_range(attr.cast<ArrayAttr>(), [](Attribute a) -> int64_t {
return a.cast<IntegerAttr>().getInt();
}));
}
/// Convert `subtensor %t [offsets][sizes][strides] -> %st` to an alloc + copy
/// pattern.
/// ```
/// %a = alloc(sizes)
/// %sv = subview %source [offsets][sizes][strides]
/// linalg_copy(%sv, %a)
/// ```
///
/// This pattern is arguable a std pattern once linalg::CopyOp becomes
/// std::CopyOp.
class SubTensorOpConverter : public OpConversionPattern<SubTensorOp> {
public:
using OpConversionPattern<SubTensorOp>::OpConversionPattern;
LogicalResult
matchAndRewrite(SubTensorOp op, ArrayRef<Value> operands,
ConversionPatternRewriter &rewriter) const final {
SubTensorOpAdaptor adaptor(operands, op->getAttrDictionary());
Value sourceMemref = adaptor.source();
assert(sourceMemref.getType().isa<MemRefType>());
MemRefType subviewMemRefType =
getTypeConverter()->convertType(op.getType()).cast<MemRefType>();
// op.sizes() capture exactly the dynamic alloc operands matching the
// subviewMemRefType thanks to subview/subtensor canonicalization and
// verification.
Value alloc =
rewriter.create<AllocOp>(op.getLoc(), subviewMemRefType, op.sizes());
Value subView = rewriter.create<SubViewOp>(
op.getLoc(), sourceMemref, extractFromI64ArrayAttr(op.static_offsets()),
extractFromI64ArrayAttr(op.static_sizes()),
extractFromI64ArrayAttr(op.static_strides()), op.offsets(), op.sizes(),
op.strides());
rewriter.create<linalg::CopyOp>(op.getLoc(), subView, alloc);
rewriter.replaceOp(op, alloc);
return success();
}
};
/// Convert `subtensor_insert %source into %dest [offsets][sizes][strides] ->
/// %t` to an tensor_to_memref + subview + copy + tensor_load pattern.
/// tensor_to_memref and tensor_load are inserted automatically by the
/// conversion infra:
/// ```
/// %sv = subview %dest [offsets][sizes][strides]
/// linalg_copy(%source, %sv)
/// // replace with %dest
/// ```
///
/// This pattern is arguable a std pattern once linalg::CopyOp becomes
/// std::CopyOp.
class SubTensorInsertOpConverter
: public OpConversionPattern<SubTensorInsertOp> {
public:
using OpConversionPattern<SubTensorInsertOp>::OpConversionPattern;
LogicalResult
matchAndRewrite(SubTensorInsertOp op, ArrayRef<Value> operands,
ConversionPatternRewriter &rewriter) const final {
SubTensorInsertOpAdaptor adaptor(operands, op->getAttrDictionary());
Value sourceMemRef = adaptor.source();
assert(sourceMemRef.getType().isa<MemRefType>());
// For now, be conservative and copy the converted input memref.
// In general, the converted input memref here could be aliased or could
// point into constant memory, so mutating it would lead to miscompilations.
Value destMemRef = cloneMemref(op.getLoc(), adaptor.dest(), rewriter);
assert(destMemRef.getType().isa<MemRefType>());
// Take a subview to copy the small memref.
Value subview = rewriter.create<SubViewOp>(
op.getLoc(), destMemRef, extractFromI64ArrayAttr(op.static_offsets()),
extractFromI64ArrayAttr(op.static_sizes()),
extractFromI64ArrayAttr(op.static_strides()), adaptor.offsets(),
adaptor.sizes(), adaptor.strides());
// Copy the small memref.
rewriter.create<linalg::CopyOp>(op.getLoc(), sourceMemRef, subview);
rewriter.replaceOp(op, destMemRef);
return success();
}
};
} // namespace
namespace {
/// Converts Linalg operations that work on tensor-type operands or results to
/// work on buffers.
struct LinalgBufferizePass : public LinalgBufferizeBase<LinalgBufferizePass> {
void runOnOperation() override {
MLIRContext &context = getContext();
ConversionTarget target(context);
BufferizeTypeConverter typeConverter;
// Mark all Standard operations legal.
target.addLegalDialect<AffineDialect, StandardOpsDialect>();
target.addIllegalOp<InitTensorOp, SubTensorOp, SubTensorInsertOp>();
// Mark all Linalg operations illegal as long as they work on tensors.
auto isLegalOperation = [&](Operation *op) {
return typeConverter.isLegal(op);
};
target.addDynamicallyLegalDialect<linalg::LinalgDialect>(isLegalOperation);
target.addDynamicallyLegalOp<ConstantOp>(isLegalOperation);
OwningRewritePatternList patterns;
populateLinalgBufferizePatterns(&context, typeConverter, patterns);
if (failed(applyPartialConversion(getOperation(), target,
std::move(patterns))))
signalPassFailure();
}
};
} // end anonymous namespace
std::unique_ptr<OperationPass<FuncOp>> mlir::createLinalgBufferizePass() {
return std::make_unique<LinalgBufferizePass>();
}
void mlir::linalg::populateLinalgBufferizePatterns(
MLIRContext *context, BufferizeTypeConverter &typeConverter,
OwningRewritePatternList &patterns) {
patterns.insert<BufferizeAnyLinalgOp>(typeConverter);
// TODO: Drop this once tensor constants work in standard.
// clang-format off
patterns.insert<
BufferizeInitTensorOp,
SubTensorOpConverter,
SubTensorInsertOpConverter
>(typeConverter, context);
// clang-format on
}