llvm-project/mlir/lib/Analysis/MLFunctionMatcher.cpp
Nicolas Vasilache af7f56fdf8 [MLIR] Implement 1-D vectorization for fastest varying load/stores
This CL is a first in a series that implements early vectorization of
increasingly complex patterns. In particular, early vectorization will support
arbitrary loop nesting patterns (both perfectly and imperfectly nested), at
arbitrary depths in the loop tree.

This first CL builds the minimal support for applying 1-D patterns.
It relies on an unaligned load/store op abstraction that can be inplemented
differently on different HW.
Future CLs will support higher dimensional patterns, but 1-D patterns already
exhibit interesting properties.
In particular, we want to separate pattern matching (i.e. legality both
structural and dependency analysis based), from profitability analysis, from
application of the transformation.
As a consequence patterns may intersect and we need to verify that a pattern
can still apply by the time we get to applying it.

A non-greedy analysis on profitability that takes into account pattern
intersection is left for future work.

Additionally the CL makes the following cleanups:
1. the matches method now returns a value, not a reference;
2. added comments about the MLFunctionMatcher and MLFunctionMatches usage by
value;
3. added size and empty methods to matches;
4. added a negative vectorization test with a conditional, this exhibited a
but in the iterators. Iterators now return nullptr if the underlying storage
is nullpt.

PiperOrigin-RevId: 219299489
2019-03-29 13:44:26 -07:00

256 lines
8.9 KiB
C++

//===- MLFunctionMatcher.cpp - MLFunctionMatcher Impl ----------*- C++ -*-===//
//
// 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.
// =============================================================================
#include "mlir/Analysis/MLFunctionMatcher.h"
#include "mlir/StandardOps/StandardOps.h"
#include "llvm/Support/Allocator.h"
namespace mlir {
/// Underlying storage for MLFunctionMatches.
struct MLFunctionMatchesStorage {
MLFunctionMatchesStorage(MLFunctionMatches::EntryType e) : matches({e}) {}
SmallVector<MLFunctionMatches::EntryType, 8> matches;
};
/// Underlying storage for MLFunctionMatcher.
struct MLFunctionMatcherStorage {
MLFunctionMatcherStorage(Statement::Kind k,
MutableArrayRef<MLFunctionMatcher> c,
FilterFunctionType filter, Statement *skip)
: kind(k), childrenMLFunctionMatchers(c.begin(), c.end()), filter(filter),
skip(skip) {}
Statement::Kind kind;
SmallVector<MLFunctionMatcher, 4> childrenMLFunctionMatchers;
FilterFunctionType filter;
/// skip is needed so that we can implement match without switching on the
/// type of the Statement.
/// The idea is that a MLFunctionMatcher first checks if it matches locally
/// and then recursively applies its children matchers to its elem->children.
/// Since we want to rely on the StmtWalker impl rather than duplicate its
/// the logic, we allow an off-by-one traversal to account for the fact that
/// we write:
///
/// void match(Statement *elem) {
/// for (auto &c : getChildrenMLFunctionMatchers()) {
/// MLFunctionMatcher childMLFunctionMatcher(...);
/// ^~~~ Needs off-by-one
/// traversal.
///
Statement *skip;
};
} // end namespace mlir
using namespace mlir;
llvm::BumpPtrAllocator *&MLFunctionMatches::allocator() {
static thread_local llvm::BumpPtrAllocator *allocator = nullptr;
return allocator;
}
void MLFunctionMatches::append(Statement *stmt, MLFunctionMatches children) {
if (!storage) {
storage = allocator()->Allocate<MLFunctionMatchesStorage>();
new (storage) MLFunctionMatchesStorage(std::make_pair(stmt, children));
} else {
storage->matches.push_back(std::make_pair(stmt, children));
}
}
MLFunctionMatches::iterator MLFunctionMatches::begin() {
return storage ? storage->matches.begin() : nullptr;
}
MLFunctionMatches::iterator MLFunctionMatches::end() {
return storage ? storage->matches.end() : nullptr;
}
/// Return the combination of multiple MLFunctionMatches as a new object.
static MLFunctionMatches combine(ArrayRef<MLFunctionMatches> matches) {
MLFunctionMatches res;
for (auto s : matches) {
for (auto ss : s) {
res.append(ss.first, ss.second);
}
}
return res;
}
/// Calls walk on `function`.
MLFunctionMatches MLFunctionMatcher::match(MLFunction *function) {
assert(!matches && "MLFunctionMatcher already matched!");
this->walkPostOrder(function);
return matches;
}
/// Calls walk on `statement`.
MLFunctionMatches MLFunctionMatcher::match(Statement *statement) {
assert(!matches && "MLFunctionMatcher already matched!");
this->walkPostOrder(statement);
return matches;
}
unsigned MLFunctionMatcher::getDepth() {
auto children = getChildrenMLFunctionMatchers();
if (children.empty()) {
return 1;
}
unsigned depth = 0;
for (auto &c : children) {
depth = std::max(depth, c.getDepth());
}
return depth + 1;
}
/// Matches a single statement in the following way:
/// 1. checks the kind of statement against the matcher, if different then
/// there is no match;
/// 2. calls the customizable filter function to refine the single statement
/// match with extra semantic constraints;
/// 3. if all is good, recursivey matches the children patterns;
/// 4. if all children match then the single statement matches too and is
/// appended to the list of matches;
/// 5. TODO(ntv) Optionally applies actions (lambda), in which case we will
/// want to traverse in post-order DFS to avoid invalidating iterators.
void MLFunctionMatcher::matchOne(Statement *elem) {
if (storage->skip == elem) {
return;
}
// Structural filter
if (elem->getKind() != getKind()) {
return;
}
// Local custom filter function
if (!getFilterFunction()(*elem)) {
return;
}
SmallVector<MLFunctionMatches, 8> childrenMLFunctionMatches;
for (auto &c : getChildrenMLFunctionMatchers()) {
/// We create a new childMLFunctionMatcher here because a matcher holds its
/// results. So we concretely need multiple copies of a given matcher, one
/// for each matching result.
MLFunctionMatcher childMLFunctionMatcher = forkMLFunctionMatcherAt(c, elem);
childMLFunctionMatcher.walkPostOrder(elem);
if (!childMLFunctionMatcher.matches) {
return;
}
childrenMLFunctionMatches.push_back(childMLFunctionMatcher.matches);
}
matches.append(elem, combine(childrenMLFunctionMatches));
}
llvm::BumpPtrAllocator *&MLFunctionMatcher::allocator() {
static thread_local llvm::BumpPtrAllocator *allocator = nullptr;
return allocator;
}
MLFunctionMatcher::MLFunctionMatcher(Statement::Kind k, MLFunctionMatcher child,
FilterFunctionType filter)
: storage(allocator()->Allocate<MLFunctionMatcherStorage>()) {
// Initialize with placement new.
new (storage)
MLFunctionMatcherStorage(k, {child}, filter, nullptr /* skip */);
}
MLFunctionMatcher::MLFunctionMatcher(
Statement::Kind k, MutableArrayRef<MLFunctionMatcher> children,
FilterFunctionType filter)
: storage(allocator()->Allocate<MLFunctionMatcherStorage>()) {
// Initialize with placement new.
new (storage)
MLFunctionMatcherStorage(k, children, filter, nullptr /* skip */);
}
MLFunctionMatcher
MLFunctionMatcher::forkMLFunctionMatcherAt(MLFunctionMatcher tmpl,
Statement *stmt) {
MLFunctionMatcher res(tmpl.getKind(), tmpl.getChildrenMLFunctionMatchers(),
tmpl.getFilterFunction());
res.storage->skip = stmt;
return res;
}
Statement::Kind MLFunctionMatcher::getKind() { return storage->kind; }
MutableArrayRef<MLFunctionMatcher>
MLFunctionMatcher::getChildrenMLFunctionMatchers() {
return storage->childrenMLFunctionMatchers;
}
FilterFunctionType MLFunctionMatcher::getFilterFunction() {
return storage->filter;
}
namespace mlir {
namespace matcher {
MLFunctionMatcher Op(FilterFunctionType filter) {
return MLFunctionMatcher(Statement::Kind::Operation, {}, filter);
}
MLFunctionMatcher If(MLFunctionMatcher child) {
return MLFunctionMatcher(Statement::Kind::If, child, defaultFilterFunction);
}
MLFunctionMatcher If(FilterFunctionType filter, MLFunctionMatcher child) {
return MLFunctionMatcher(Statement::Kind::If, child, filter);
}
MLFunctionMatcher If(MutableArrayRef<MLFunctionMatcher> children) {
return MLFunctionMatcher(Statement::Kind::If, children,
defaultFilterFunction);
}
MLFunctionMatcher If(FilterFunctionType filter,
MutableArrayRef<MLFunctionMatcher> children) {
return MLFunctionMatcher(Statement::Kind::If, children, filter);
}
MLFunctionMatcher For(MLFunctionMatcher child) {
return MLFunctionMatcher(Statement::Kind::For, child, defaultFilterFunction);
}
MLFunctionMatcher For(FilterFunctionType filter, MLFunctionMatcher child) {
return MLFunctionMatcher(Statement::Kind::For, child, filter);
}
MLFunctionMatcher For(MutableArrayRef<MLFunctionMatcher> children) {
return MLFunctionMatcher(Statement::Kind::For, children,
defaultFilterFunction);
}
MLFunctionMatcher For(FilterFunctionType filter,
MutableArrayRef<MLFunctionMatcher> children) {
return MLFunctionMatcher(Statement::Kind::For, children, filter);
}
// TODO(ntv): parallel annotation on loops.
bool isParallelLoop(const Statement &stmt) {
const auto *loop = cast<ForStmt>(&stmt);
return (void *)loop || true; // loop->isParallel();
};
// TODO(ntv): reduction annotation on loops.
bool isReductionLoop(const Statement &stmt) {
const auto *loop = cast<ForStmt>(&stmt);
return (void *)loop || true; // loop->isReduction();
};
bool isLoadOrStore(const Statement &stmt) {
const auto *opStmt = dyn_cast<OperationStmt>(&stmt);
return opStmt && (opStmt->isa<LoadOp>() || opStmt->isa<StoreOp>());
};
} // end namespace matcher
} // end namespace mlir