Files
clang-p2996/mlir/lib/Transforms/Vectorization/VectorizerTestPass.cpp
Nicolas Vasilache cfa5831960 Uniformize composition of AffineApplyOp by construction
This CL is the 5th on the path to simplifying AffineMap composition.
This removes the distinction between normalized single-result AffineMap and
more general composed multi-result map.

One nice byproduct of making the implementation driven by single-result is
that the multi-result extension is a trivial change: the implementation is
still single-result and we just use:

```
unsigned idx = getIndexOf(...);
map.getResult(idx);
```

This CL also fixes an AffineNormalizer implementation issue related to symbols.
Namely it stops performing substitutions on symbols in AffineNormalizer and
instead concatenates them all to be consistent with the call to
`AffineMap::compose(AffineMap)`. This latter call to `compose` cannot perform
simplifications of symbols coming from different maps based on positions only:
i.e. dims are applied and renumbered but symbols must be concatenated.

The only way to determine whether symbols from different AffineApply are the
same is to look at the concrete values. The canonicalizeMapAndOperands is thus
extended with behavior to support replacing operands that appear multiple
times.

Lastly, this CL demonstrates that the implementation is correct by rewriting
ComposeAffineMaps using only `makeComposedAffineApply`. The implementation
uses a matcher because AffineApplyOp are introduced as composed operations on
the fly instead of iteratively forwardSubstituting. For this purpose, a walker
would revisit freshly introduced AffineApplyOp. Regardless, ComposeAffineMaps
is scheduled to disappear, this CL replaces the implementation based on
iterative `forwardSubstitute` by a composed-by-construction
`makeComposedAffineApply`.
Remaining calls to `forwardSubstitute` will be removed in the next CL.

PiperOrigin-RevId: 228830443
2019-03-29 15:08:40 -07:00

304 lines
10 KiB
C++

//===- VectorizerTestPass.cpp - VectorizerTestPass Pass Impl --------------===//
//
// Copyright 2019 The MLIR Authors.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
// =============================================================================
//
// This file implements a simple testing pass for vectorization functionality.
//
//===----------------------------------------------------------------------===//
#include "mlir/Analysis/AffineAnalysis.h"
#include "mlir/Analysis/MLFunctionMatcher.h"
#include "mlir/Analysis/SliceAnalysis.h"
#include "mlir/Analysis/VectorAnalysis.h"
#include "mlir/IR/Builders.h"
#include "mlir/IR/BuiltinOps.h"
#include "mlir/IR/StandardTypes.h"
#include "mlir/Pass.h"
#include "mlir/Support/Functional.h"
#include "mlir/Support/STLExtras.h"
#include "mlir/Transforms/Passes.h"
#include "llvm/Support/CommandLine.h"
#include "llvm/Support/Debug.h"
#define DEBUG_TYPE "vectorizer-test"
using namespace mlir;
using llvm::outs;
using llvm::SetVector;
using functional::map;
static llvm::cl::list<int> clTestVectorShapeRatio(
"vector-shape-ratio",
llvm::cl::desc("Specify the HW vector size for vectorization"),
llvm::cl::ZeroOrMore);
static llvm::cl::opt<bool> clTestForwardSlicingAnalysis(
"forward-slicing",
llvm::cl::desc(
"Specify to enable testing forward static slicing and topological sort "
"functionalities"));
static llvm::cl::opt<bool> clTestBackwardSlicingAnalysis(
"backward-slicing",
llvm::cl::desc("Specify to enable testing backward static slicing and "
"topological sort functionalities"));
static llvm::cl::opt<bool> clTestSlicingAnalysis(
"slicing",
llvm::cl::desc(
"Specify to enable testing static slicing and topological sort "
"functionalities"));
static llvm::cl::opt<bool> clTestComposeMaps(
"compose-maps",
llvm::cl::desc(
"Specify to enable testing the composition of AffineMap where each "
"AffineMap in the composition is specified as the affine_map attribute "
"in a constant op."));
static llvm::cl::opt<bool> clTestNormalizeMaps(
"normalize-maps",
llvm::cl::desc(
"Specify to enable testing the normalization of AffineAffineApplyOp "
"where each AffineAffineApplyOp in the composition is a single output "
"instruction."));
namespace {
struct VectorizerTestPass : public FunctionPass {
static constexpr auto kTestAffineMapOpName = "test_affine_map";
static constexpr auto kTestAffineMapAttrName = "affine_map";
VectorizerTestPass() : FunctionPass(&VectorizerTestPass::passID) {}
PassResult runOnFunction(Function *f) override;
void testVectorShapeRatio(Function *f);
void testForwardSlicing(Function *f);
void testBackwardSlicing(Function *f);
void testSlicing(Function *f);
void testComposeMaps(Function *f);
void testNormalizeMaps(Function *f);
// Thread-safe RAII contexts local to pass, BumpPtrAllocator freed on exit.
MLFunctionMatcherContext MLContext;
static char passID;
};
} // end anonymous namespace
char VectorizerTestPass::passID = 0;
void VectorizerTestPass::testVectorShapeRatio(Function *f) {
using matcher::Op;
SmallVector<int, 8> shape(clTestVectorShapeRatio.begin(),
clTestVectorShapeRatio.end());
auto subVectorType = VectorType::get(shape, Type::getF32(f->getContext()));
// Only filter instructions that operate on a strict super-vector and have one
// return. This makes testing easier.
auto filter = [subVectorType](const Instruction &inst) {
auto *opInst = dyn_cast<OperationInst>(&inst);
if (!opInst) {
return false;
}
assert(subVectorType.getElementType() ==
Type::getF32(subVectorType.getContext()) &&
"Only f32 supported for now");
if (!matcher::operatesOnSuperVectors(*opInst, subVectorType)) {
return false;
}
if (opInst->getNumResults() != 1) {
return false;
}
return true;
};
auto pat = Op(filter);
auto matches = pat.match(f);
for (auto m : matches) {
auto *opInst = cast<OperationInst>(m.first);
// This is a unit test that only checks and prints shape ratio.
// As a consequence we write only Ops with a single return type for the
// purpose of this test. If we need to test more intricate behavior in the
// future we can always extend.
auto superVectorType = opInst->getResult(0)->getType().cast<VectorType>();
auto ratio = shapeRatio(superVectorType, subVectorType);
if (!ratio.hasValue()) {
opInst->emitNote("NOT MATCHED");
} else {
outs() << "\nmatched: " << *opInst << " with shape ratio: ";
interleaveComma(MutableArrayRef<unsigned>(*ratio), outs());
}
}
}
static std::string toString(Instruction *inst) {
std::string res;
auto os = llvm::raw_string_ostream(res);
inst->print(os);
return res;
}
static MLFunctionMatches matchTestSlicingOps(Function *f) {
// Just use a custom op name for this test, it makes life easier.
constexpr auto kTestSlicingOpName = "slicing-test-op";
using functional::map;
using matcher::Op;
// Match all OpInstructions with the kTestSlicingOpName name.
auto filter = [](const Instruction &inst) {
const auto &opInst = cast<OperationInst>(inst);
return opInst.getName().getStringRef() == kTestSlicingOpName;
};
auto pat = Op(filter);
return pat.match(f);
}
void VectorizerTestPass::testBackwardSlicing(Function *f) {
auto matches = matchTestSlicingOps(f);
for (auto m : matches) {
SetVector<Instruction *> backwardSlice;
getBackwardSlice(m.first, &backwardSlice);
auto strs = map(toString, backwardSlice);
outs() << "\nmatched: " << *m.first << " backward static slice: ";
for (const auto &s : strs) {
outs() << "\n" << s;
}
}
}
void VectorizerTestPass::testForwardSlicing(Function *f) {
auto matches = matchTestSlicingOps(f);
for (auto m : matches) {
SetVector<Instruction *> forwardSlice;
getForwardSlice(m.first, &forwardSlice);
auto strs = map(toString, forwardSlice);
outs() << "\nmatched: " << *m.first << " forward static slice: ";
for (const auto &s : strs) {
outs() << "\n" << s;
}
}
}
void VectorizerTestPass::testSlicing(Function *f) {
auto matches = matchTestSlicingOps(f);
for (auto m : matches) {
SetVector<Instruction *> staticSlice = getSlice(m.first);
auto strs = map(toString, staticSlice);
outs() << "\nmatched: " << *m.first << " static slice: ";
for (const auto &s : strs) {
outs() << "\n" << s;
}
}
}
bool customOpWithAffineMapAttribute(const Instruction &inst) {
const auto &opInst = cast<OperationInst>(inst);
return opInst.getName().getStringRef() ==
VectorizerTestPass::kTestAffineMapOpName;
}
void VectorizerTestPass::testComposeMaps(Function *f) {
using matcher::Op;
auto pattern = Op(customOpWithAffineMapAttribute);
auto matches = pattern.match(f);
SmallVector<AffineMap, 4> maps;
maps.reserve(matches.size());
std::reverse(matches.begin(), matches.end());
for (auto m : matches) {
auto *opInst = cast<OperationInst>(m.first);
auto map = opInst->getAttr(VectorizerTestPass::kTestAffineMapAttrName)
.cast<AffineMapAttr>()
.getValue();
maps.push_back(map);
}
AffineMap res;
for (auto m : maps) {
res = res ? res.compose(m) : m;
}
simplifyAffineMap(res).print(outs() << "\nComposed map: ");
}
bool affineApplyOp(const Instruction &inst) {
const auto &opInst = cast<OperationInst>(inst);
return opInst.isa<AffineApplyOp>();
}
bool singleResultAffineApplyOpWithoutUses(const Instruction &inst) {
const auto &opInst = cast<OperationInst>(inst);
auto app = opInst.dyn_cast<AffineApplyOp>();
return app && (app->getNumResults() == 1) &&
app->getResult(0)->getUses().end() ==
app->getResult(0)->getUses().begin();
}
void VectorizerTestPass::testNormalizeMaps(Function *f) {
using matcher::Op;
// Save matched AffineApplyOp that all need to be erased in the end.
auto pattern = Op(affineApplyOp);
auto toErase = pattern.match(f);
std::reverse(toErase.begin(), toErase.end());
{
// Compose maps.
auto pattern = Op(singleResultAffineApplyOpWithoutUses);
for (auto m : pattern.match(f)) {
auto app = cast<OperationInst>(m.first)->cast<AffineApplyOp>();
FuncBuilder b(m.first);
using ValueTy = decltype(*(app->getOperands().begin()));
SmallVector<Value *, 8> operands =
functional::map([](ValueTy v) { return static_cast<Value *>(v); },
app->getOperands().begin(), app->getOperands().end());
makeComposedAffineApply(&b, app->getLoc(), app->getAffineMap(), operands);
}
}
// We should now be able to erase everything in reverse order in this test.
for (auto m : toErase) {
m.first->erase();
}
}
PassResult VectorizerTestPass::runOnFunction(Function *f) {
// Only support single block functions at this point.
if (f->getBlocks().size() != 1)
return success();
if (!clTestVectorShapeRatio.empty()) {
testVectorShapeRatio(f);
}
if (clTestForwardSlicingAnalysis) {
testForwardSlicing(f);
}
if (clTestBackwardSlicingAnalysis) {
testBackwardSlicing(f);
}
if (clTestSlicingAnalysis) {
testSlicing(f);
}
if (clTestComposeMaps) {
testComposeMaps(f);
}
if (clTestNormalizeMaps) {
testNormalizeMaps(f);
}
return PassResult::Success;
}
FunctionPass *mlir::createVectorizerTestPass() {
return new VectorizerTestPass();
}
static PassRegistration<VectorizerTestPass>
pass("vectorizer-test", "Tests vectorizer standalone functionality.");
#undef DEBUG_TYPE