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llvm/mlir/lib/Transforms/SimplifyAffineStructures.cpp

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//===- SimplifyAffineStructures.cpp - ---------------------------*- 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.
// =============================================================================
//
// This file implements a pass to simplify affine structures.
//
//===----------------------------------------------------------------------===//
#include "mlir/Analysis/AffineStructures.h"
#include "mlir/IR/Function.h"
#include "mlir/IR/Instruction.h"
#include "mlir/IR/IntegerSet.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
2018-10-30 17:43:06 -07:00
#include "mlir/Pass.h"
#include "mlir/Transforms/Passes.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
2018-10-30 17:43:06 -07:00
#define DEBUG_TYPE "simplify-affine-structure"
using namespace mlir;
namespace {
/// Simplifies all affine expressions appearing in the operation instructions of
/// the Function. This is mainly to test the simplifyAffineExpr method.
/// TODO(someone): This should just be defined as a canonicalization pattern
/// on AffineMap and driven from the existing canonicalization pass.
struct SimplifyAffineStructures : public FunctionPass {
explicit SimplifyAffineStructures()
: FunctionPass(&SimplifyAffineStructures::passID) {}
PassResult runOnFunction(Function *f) override;
static char passID;
};
} // end anonymous namespace
char SimplifyAffineStructures::passID = 0;
FunctionPass *mlir::createSimplifyAffineStructuresPass() {
return new SimplifyAffineStructures();
}
/// Performs basic integer set simplifications. Checks if it's empty, and
/// replaces it with the canonical empty set if it is.
static IntegerSet simplifyIntegerSet(IntegerSet set) {
FlatAffineConstraints fac(set);
if (fac.isEmpty())
return IntegerSet::getEmptySet(set.getNumDims(), set.getNumSymbols(),
set.getContext());
return set;
}
PassResult SimplifyAffineStructures::runOnFunction(Function *f) {
f->walk([&](OperationInst *opInst) {
for (auto attr : opInst->getAttrs()) {
if (auto mapAttr = attr.second.dyn_cast<AffineMapAttr>()) {
MutableAffineMap mMap(mapAttr.getValue());
mMap.simplify();
auto map = mMap.getAffineMap();
opInst->setAttr(attr.first, AffineMapAttr::get(map));
} else if (auto setAttr = attr.second.dyn_cast<IntegerSetAttr>()) {
auto simplified = simplifyIntegerSet(setAttr.getValue());
opInst->setAttr(attr.first, IntegerSetAttr::get(simplified));
}
}
});
return success();
}
static PassRegistration<SimplifyAffineStructures>
pass("simplify-affine-structures", "Simplify affine expressions");