Files
clang-p2996/mlir/lib/Conversion/SCFToSPIRV/SCFToSPIRV.cpp
River Riddle 015192c634 [mlir:DialectConversion] Restructure how argument/target materializations get invoked
The current implementation invokes materializations
whenever an input operand does not have a mapping for the
desired type, i.e. it requires materialization at the earliest possible
point. This conflicts with goal of dialect conversion (and also the
current documentation) which states that a materialization is only
required if the materialization is supposed to persist after the
conversion process has finished.

This revision refactors this such that whenever a target
materialization "might" be necessary, we insert an
unrealized_conversion_cast to act as a temporary materialization.
This allows for deferring the invocation of the user
materialization hooks until the end of the conversion process,
where we actually have a better sense if it's actually
necessary. This has several benefits:

* In some cases a target materialization hook is no longer
   necessary
When performing a full conversion, there are some situations
where a temporary materialization is necessary. Moving forward,
these users won't need to provide any target materializations,
as the temporary materializations do not require the user to
provide materialization hooks.

* getRemappedValue can now handle values that haven't been
   converted yet
Before this commit, it wasn't well supported to get the remapped
value of a value that hadn't been converted yet (making it
difficult/impossible to convert multiple operations in many
situations). This commit updates getRemappedValue to properly
handle this case by inserting temporary materializations when
necessary.

Another code-health related benefit is that with this change we
can move a majority of the complexity related to materializations
to the end of the conversion process, instead of handling adhoc
while conversion is happening.

Differential Revision: https://reviews.llvm.org/D111620
2021-10-27 02:09:04 +00:00

327 lines
14 KiB
C++

//===- SCFToSPIRV.cpp - SCF to SPIR-V Patterns ----------------------------===//
//
// 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 patterns to convert SCF dialect to SPIR-V dialect.
//
//===----------------------------------------------------------------------===//
#include "mlir/Conversion/SCFToSPIRV/SCFToSPIRV.h"
#include "mlir/Dialect/SCF/SCF.h"
#include "mlir/Dialect/SPIRV/IR/SPIRVDialect.h"
#include "mlir/Dialect/SPIRV/IR/SPIRVOps.h"
#include "mlir/Dialect/SPIRV/Transforms/SPIRVConversion.h"
#include "mlir/IR/BuiltinOps.h"
#include "mlir/Transforms/DialectConversion.h"
using namespace mlir;
//===----------------------------------------------------------------------===//
// Context
//===----------------------------------------------------------------------===//
namespace mlir {
struct ScfToSPIRVContextImpl {
// Map between the spirv region control flow operation (spv.mlir.loop or
// spv.mlir.selection) to the VariableOp created to store the region results.
// The order of the VariableOp matches the order of the results.
DenseMap<Operation *, SmallVector<spirv::VariableOp, 8>> outputVars;
};
} // namespace mlir
/// We use ScfToSPIRVContext to store information about the lowering of the scf
/// region that need to be used later on. When we lower scf.for/scf.if we create
/// VariableOp to store the results. We need to keep track of the VariableOp
/// created as we need to insert stores into them when lowering Yield. Those
/// StoreOp cannot be created earlier as they may use a different type than
/// yield operands.
ScfToSPIRVContext::ScfToSPIRVContext() {
impl = std::make_unique<ScfToSPIRVContextImpl>();
}
ScfToSPIRVContext::~ScfToSPIRVContext() = default;
//===----------------------------------------------------------------------===//
// Pattern Declarations
//===----------------------------------------------------------------------===//
namespace {
/// Common class for all vector to GPU patterns.
template <typename OpTy>
class SCFToSPIRVPattern : public OpConversionPattern<OpTy> {
public:
SCFToSPIRVPattern<OpTy>(MLIRContext *context, SPIRVTypeConverter &converter,
ScfToSPIRVContextImpl *scfToSPIRVContext)
: OpConversionPattern<OpTy>::OpConversionPattern(converter, context),
scfToSPIRVContext(scfToSPIRVContext), typeConverter(converter) {}
protected:
ScfToSPIRVContextImpl *scfToSPIRVContext;
// FIXME: We explicitly keep a reference of the type converter here instead of
// passing it to OpConversionPattern during construction. This effectively
// bypasses the conversion framework's automation on type conversion. This is
// needed right now because the conversion framework will unconditionally
// legalize all types used by SCF ops upon discovering them, for example, the
// types of loop carried values. We use SPIR-V variables for those loop
// carried values. Depending on the available capabilities, the SPIR-V
// variable can be different, for example, cooperative matrix or normal
// variable. We'd like to detach the conversion of the loop carried values
// from the SCF ops (which is mainly a region). So we need to "mark" types
// used by SCF ops as legal, if to use the conversion framework for type
// conversion. There isn't a straightforward way to do that yet, as when
// converting types, ops aren't taken into consideration. Therefore, we just
// bypass the framework's type conversion for now.
SPIRVTypeConverter &typeConverter;
};
/// Pattern to convert a scf::ForOp within kernel functions into spirv::LoopOp.
class ForOpConversion final : public SCFToSPIRVPattern<scf::ForOp> {
public:
using SCFToSPIRVPattern<scf::ForOp>::SCFToSPIRVPattern;
LogicalResult
matchAndRewrite(scf::ForOp forOp, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override;
};
/// Pattern to convert a scf::IfOp within kernel functions into
/// spirv::SelectionOp.
class IfOpConversion final : public SCFToSPIRVPattern<scf::IfOp> {
public:
using SCFToSPIRVPattern<scf::IfOp>::SCFToSPIRVPattern;
LogicalResult
matchAndRewrite(scf::IfOp ifOp, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override;
};
class TerminatorOpConversion final : public SCFToSPIRVPattern<scf::YieldOp> {
public:
using SCFToSPIRVPattern<scf::YieldOp>::SCFToSPIRVPattern;
LogicalResult
matchAndRewrite(scf::YieldOp terminatorOp, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override;
};
} // namespace
/// Helper function to replaces SCF op outputs with SPIR-V variable loads.
/// We create VariableOp to handle the results value of the control flow region.
/// spv.mlir.loop/spv.mlir.selection currently don't yield value. Right after
/// the loop we load the value from the allocation and use it as the SCF op
/// result.
template <typename ScfOp, typename OpTy>
static void replaceSCFOutputValue(ScfOp scfOp, OpTy newOp,
ConversionPatternRewriter &rewriter,
ScfToSPIRVContextImpl *scfToSPIRVContext,
ArrayRef<Type> returnTypes) {
Location loc = scfOp.getLoc();
auto &allocas = scfToSPIRVContext->outputVars[newOp];
// Clearing the allocas is necessary in case a dialect conversion path failed
// previously, and this is the second attempt of this conversion.
allocas.clear();
SmallVector<Value, 8> resultValue;
for (Type convertedType : returnTypes) {
auto pointerType =
spirv::PointerType::get(convertedType, spirv::StorageClass::Function);
rewriter.setInsertionPoint(newOp);
auto alloc = rewriter.create<spirv::VariableOp>(
loc, pointerType, spirv::StorageClass::Function,
/*initializer=*/nullptr);
allocas.push_back(alloc);
rewriter.setInsertionPointAfter(newOp);
Value loadResult = rewriter.create<spirv::LoadOp>(loc, alloc);
resultValue.push_back(loadResult);
}
rewriter.replaceOp(scfOp, resultValue);
}
//===----------------------------------------------------------------------===//
// scf::ForOp
//===----------------------------------------------------------------------===//
LogicalResult
ForOpConversion::matchAndRewrite(scf::ForOp forOp, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const {
// scf::ForOp can be lowered to the structured control flow represented by
// spirv::LoopOp by making the continue block of the spirv::LoopOp the loop
// latch and the merge block the exit block. The resulting spirv::LoopOp has a
// single back edge from the continue to header block, and a single exit from
// header to merge.
auto loc = forOp.getLoc();
auto loopOp = rewriter.create<spirv::LoopOp>(loc, spirv::LoopControl::None);
loopOp.addEntryAndMergeBlock();
OpBuilder::InsertionGuard guard(rewriter);
// Create the block for the header.
auto *header = new Block();
// Insert the header.
loopOp.body().getBlocks().insert(std::next(loopOp.body().begin(), 1), header);
// Create the new induction variable to use.
BlockArgument newIndVar = header->addArgument(adaptor.lowerBound().getType());
for (Value arg : adaptor.initArgs())
header->addArgument(arg.getType());
Block *body = forOp.getBody();
// Apply signature conversion to the body of the forOp. It has a single block,
// with argument which is the induction variable. That has to be replaced with
// the new induction variable.
TypeConverter::SignatureConversion signatureConverter(
body->getNumArguments());
signatureConverter.remapInput(0, newIndVar);
for (unsigned i = 1, e = body->getNumArguments(); i < e; i++)
signatureConverter.remapInput(i, header->getArgument(i));
body = rewriter.applySignatureConversion(&forOp.getLoopBody(),
signatureConverter);
// Move the blocks from the forOp into the loopOp. This is the body of the
// loopOp.
rewriter.inlineRegionBefore(forOp->getRegion(0), loopOp.body(),
std::next(loopOp.body().begin(), 2));
SmallVector<Value, 8> args(1, adaptor.lowerBound());
args.append(adaptor.initArgs().begin(), adaptor.initArgs().end());
// Branch into it from the entry.
rewriter.setInsertionPointToEnd(&(loopOp.body().front()));
rewriter.create<spirv::BranchOp>(loc, header, args);
// Generate the rest of the loop header.
rewriter.setInsertionPointToEnd(header);
auto *mergeBlock = loopOp.getMergeBlock();
auto cmpOp = rewriter.create<spirv::SLessThanOp>(
loc, rewriter.getI1Type(), newIndVar, adaptor.upperBound());
rewriter.create<spirv::BranchConditionalOp>(
loc, cmpOp, body, ArrayRef<Value>(), mergeBlock, ArrayRef<Value>());
// Generate instructions to increment the step of the induction variable and
// branch to the header.
Block *continueBlock = loopOp.getContinueBlock();
rewriter.setInsertionPointToEnd(continueBlock);
// Add the step to the induction variable and branch to the header.
Value updatedIndVar = rewriter.create<spirv::IAddOp>(
loc, newIndVar.getType(), newIndVar, adaptor.step());
rewriter.create<spirv::BranchOp>(loc, header, updatedIndVar);
// Infer the return types from the init operands. Vector type may get
// converted to CooperativeMatrix or to Vector type, to avoid having complex
// extra logic to figure out the right type we just infer it from the Init
// operands.
SmallVector<Type, 8> initTypes;
for (auto arg : adaptor.initArgs())
initTypes.push_back(arg.getType());
replaceSCFOutputValue(forOp, loopOp, rewriter, scfToSPIRVContext, initTypes);
return success();
}
//===----------------------------------------------------------------------===//
// scf::IfOp
//===----------------------------------------------------------------------===//
LogicalResult
IfOpConversion::matchAndRewrite(scf::IfOp ifOp, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const {
// When lowering `scf::IfOp` we explicitly create a selection header block
// before the control flow diverges and a merge block where control flow
// subsequently converges.
auto loc = ifOp.getLoc();
// Create `spv.selection` operation, selection header block and merge block.
auto selectionOp =
rewriter.create<spirv::SelectionOp>(loc, spirv::SelectionControl::None);
auto *mergeBlock =
rewriter.createBlock(&selectionOp.body(), selectionOp.body().end());
rewriter.create<spirv::MergeOp>(loc);
OpBuilder::InsertionGuard guard(rewriter);
auto *selectionHeaderBlock =
rewriter.createBlock(&selectionOp.body().front());
// Inline `then` region before the merge block and branch to it.
auto &thenRegion = ifOp.thenRegion();
auto *thenBlock = &thenRegion.front();
rewriter.setInsertionPointToEnd(&thenRegion.back());
rewriter.create<spirv::BranchOp>(loc, mergeBlock);
rewriter.inlineRegionBefore(thenRegion, mergeBlock);
auto *elseBlock = mergeBlock;
// If `else` region is not empty, inline that region before the merge block
// and branch to it.
if (!ifOp.elseRegion().empty()) {
auto &elseRegion = ifOp.elseRegion();
elseBlock = &elseRegion.front();
rewriter.setInsertionPointToEnd(&elseRegion.back());
rewriter.create<spirv::BranchOp>(loc, mergeBlock);
rewriter.inlineRegionBefore(elseRegion, mergeBlock);
}
// Create a `spv.BranchConditional` operation for selection header block.
rewriter.setInsertionPointToEnd(selectionHeaderBlock);
rewriter.create<spirv::BranchConditionalOp>(loc, adaptor.condition(),
thenBlock, ArrayRef<Value>(),
elseBlock, ArrayRef<Value>());
SmallVector<Type, 8> returnTypes;
for (auto result : ifOp.results()) {
auto convertedType = typeConverter.convertType(result.getType());
returnTypes.push_back(convertedType);
}
replaceSCFOutputValue(ifOp, selectionOp, rewriter, scfToSPIRVContext,
returnTypes);
return success();
}
//===----------------------------------------------------------------------===//
// scf::YieldOp
//===----------------------------------------------------------------------===//
/// Yield is lowered to stores to the VariableOp created during lowering of the
/// parent region. For loops we also need to update the branch looping back to
/// the header with the loop carried values.
LogicalResult TerminatorOpConversion::matchAndRewrite(
scf::YieldOp terminatorOp, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const {
ValueRange operands = adaptor.getOperands();
// If the region is return values, store each value into the associated
// VariableOp created during lowering of the parent region.
if (!operands.empty()) {
auto loc = terminatorOp.getLoc();
auto &allocas = scfToSPIRVContext->outputVars[terminatorOp->getParentOp()];
assert(allocas.size() == operands.size());
for (unsigned i = 0, e = operands.size(); i < e; i++)
rewriter.create<spirv::StoreOp>(loc, allocas[i], operands[i]);
if (isa<spirv::LoopOp>(terminatorOp->getParentOp())) {
// For loops we also need to update the branch jumping back to the header.
auto br =
cast<spirv::BranchOp>(rewriter.getInsertionBlock()->getTerminator());
SmallVector<Value, 8> args(br.getBlockArguments());
args.append(operands.begin(), operands.end());
rewriter.setInsertionPoint(br);
rewriter.create<spirv::BranchOp>(terminatorOp.getLoc(), br.getTarget(),
args);
rewriter.eraseOp(br);
}
}
rewriter.eraseOp(terminatorOp);
return success();
}
//===----------------------------------------------------------------------===//
// Hooks
//===----------------------------------------------------------------------===//
void mlir::populateSCFToSPIRVPatterns(SPIRVTypeConverter &typeConverter,
ScfToSPIRVContext &scfToSPIRVContext,
RewritePatternSet &patterns) {
patterns.add<ForOpConversion, IfOpConversion, TerminatorOpConversion>(
patterns.getContext(), typeConverter, scfToSPIRVContext.getImpl());
}