llvm-project/mlir/test/lib/Transforms/TestMemRefBoundCheck.cpp

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//===- TestMemRefBoundCheck.cpp - Test out of bound access checks ---------===//
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
//
// 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
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
//
//===----------------------------------------------------------------------===//
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
//
// This file implements a pass to check memref accesses for out of bound
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
// accesses.
//
//===----------------------------------------------------------------------===//
#include "mlir/ADT/TypeSwitch.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
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#include "mlir/Analysis/AffineAnalysis.h"
#include "mlir/Analysis/AffineStructures.h"
#include "mlir/Analysis/Utils.h"
#include "mlir/Dialect/Affine/IR/AffineOps.h"
#include "mlir/Dialect/StandardOps/IR/Ops.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/IR/Builders.h"
#include "mlir/Pass/Pass.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
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#include "llvm/Support/Debug.h"
#define DEBUG_TYPE "memref-bound-check"
using namespace mlir;
namespace {
/// Checks for out of bound memef access subscripts..
struct TestMemRefBoundCheck
: public PassWrapper<TestMemRefBoundCheck, FunctionPass> {
void runOnFunction() override;
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
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};
} // end anonymous namespace
void TestMemRefBoundCheck::runOnFunction() {
getFunction().walk([](Operation *opInst) {
TypeSwitch<Operation *>(opInst).Case<AffineLoadOp, AffineStoreOp>(
[](auto op) { boundCheckLoadOrStoreOp(op); });
// TODO(bondhugula): do this for DMA ops as well.
});
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
}
namespace mlir {
void registerMemRefBoundCheck() {
PassRegistration<TestMemRefBoundCheck>(
"test-memref-bound-check", "Check memref access bounds in a Function");
}
} // namespace mlir