llvm-project/mlir/lib/Transforms/Canonicalizer.cpp

65 lines
2.4 KiB
C++
Raw Normal View History

//===- Canonicalizer.cpp - Canonicalize MLIR operations -------------------===//
//
// 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 transformation pass converts operations into their canonical forms by
// folding constants, applying operation identity transformations etc.
//
//===----------------------------------------------------------------------===//
#include "mlir/IR/MLIRContext.h"
#include "mlir/IR/PatternMatch.h"
Introduce memref bound checking. Introduce analysis to check memref accesses (in MLFunctions) for out of bound ones. It works as follows: $ mlir-opt -memref-bound-check test/Transforms/memref-bound-check.mlir /tmp/single.mlir:10:12: error: 'load' op memref out of upper bound access along dimension tensorflow/mlir#1 %x = load %A[%idxtensorflow/mlir#0, %idxtensorflow/mlir#1] : memref<9 x 9 x i32> ^ /tmp/single.mlir:10:12: error: 'load' op memref out of lower bound access along dimension tensorflow/mlir#1 %x = load %A[%idxtensorflow/mlir#0, %idxtensorflow/mlir#1] : memref<9 x 9 x i32> ^ /tmp/single.mlir:10:12: error: 'load' op memref out of upper bound access along dimension tensorflow/mlir#2 %x = load %A[%idxtensorflow/mlir#0, %idxtensorflow/mlir#1] : memref<9 x 9 x i32> ^ /tmp/single.mlir:10:12: error: 'load' op memref out of lower bound access along dimension tensorflow/mlir#2 %x = load %A[%idxtensorflow/mlir#0, %idxtensorflow/mlir#1] : memref<9 x 9 x i32> ^ /tmp/single.mlir:12:12: error: 'load' op memref out of upper bound access along dimension tensorflow/mlir#1 %y = load %B[%idy] : memref<128 x i32> ^ /tmp/single.mlir:12:12: error: 'load' op memref out of lower bound access along dimension tensorflow/mlir#1 %y = load %B[%idy] : memref<128 x i32> ^ #map0 = (d0, d1) -> (d0, d1) #map1 = (d0, d1) -> (d0 * 128 - d1) mlfunc @test() { %0 = alloc() : memref<9x9xi32> %1 = alloc() : memref<128xi32> for %i0 = -1 to 9 { for %i1 = -1 to 9 { %2 = affine_apply #map0(%i0, %i1) %3 = load %0[%2tensorflow/mlir#0, %2tensorflow/mlir#1] : memref<9x9xi32> %4 = affine_apply #map1(%i0, %i1) %5 = load %1[%4] : memref<128xi32> } } return } - Improves productivity while manually / semi-automatically developing MLIR for testing / prototyping; also provides an indirect way to catch errors in transformations. - This pass is an easy way to test the underlying affine analysis machinery including low level routines. Some code (in getMemoryRegion()) borrowed from @andydavis cl/218263256. While on this: - create mlir/Analysis/Passes.h; move Pass.h up from mlir/Transforms/ to mlir/ - fix a bug in AffineAnalysis.cpp::toAffineExpr TODO: extend to non-constant loop bounds (straightforward). Will transparently work for all accesses once floordiv, mod, ceildiv are supported in the AffineMap -> FlatAffineConstraints conversion. PiperOrigin-RevId: 219397961
2018-10-31 08:43:06 +08:00
#include "mlir/Pass.h"
#include "mlir/Transforms/Passes.h"
using namespace mlir;
//===----------------------------------------------------------------------===//
// The actual Canonicalizer Pass.
//===----------------------------------------------------------------------===//
namespace {
/// Canonicalize operations in functions.
struct Canonicalizer : public FunctionPass {
Canonicalizer() : FunctionPass(&Canonicalizer::passID) {}
PassResult runOnFunction(Function *fn) override;
static char passID;
};
} // end anonymous namespace
char Canonicalizer::passID = 0;
PassResult Canonicalizer::runOnFunction(Function *fn) {
auto *context = fn->getContext();
OwningRewritePatternList patterns;
// TODO: Instead of adding all known patterns from the whole system lazily add
// and cache the canonicalization patterns for ops we see in practice when
// building the worklist. For now, we just grab everything.
for (auto *op : fn->getContext()->getRegisteredOperations())
op->getCanonicalizationPatterns(patterns, context);
applyPatternsGreedily(fn, std::move(patterns));
return success();
}
/// Create a Canonicalizer pass.
FunctionPass *mlir::createCanonicalizerPass() { return new Canonicalizer(); }
static PassRegistration<Canonicalizer> pass("canonicalize",
"Canonicalize operations");