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[mlir][linalg] Run CSE after every CodegenStrategy transformation. 

Add CSE after every transformation. Transformations such as tiling introduce redundant computation, for example, one AffineMinOp for every operand dimension pair. Follow up transformations such as Padding and Hoisting benefit from CSE since comparing slice sizes simplifies to comparing SSA values instead of analyzing affine expressions.

Reviewed By: nicolasvasilache

Differential Revision: https://reviews.llvm.org/D114585
This commit is contained in:
gysit 2021-11-30 14:48:25 +00:00
parent 74cbd71072
commit 914e72d400
2 changed files with 15 additions and 2 deletions
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8
mlir/lib/Dialect/Linalg/Transforms/LinalgStrategyPasses.cpp
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@ -25,9 +25,11 @@
#include "mlir/Dialect/Vector/VectorTransforms.h"
#include "mlir/IR/AffineExpr.h"
#include "mlir/IR/AffineMap.h"
#include "mlir/Pass/PassManager.h"
#include "mlir/Support/LLVM.h"
#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
#include "mlir/Transforms/LoopUtils.h"
#include "mlir/Transforms/Passes.h"
#include "mlir/Transforms/Utils.h"
using namespace mlir;
@ -335,6 +337,12 @@ struct LinalgStrategyEnablePass
if (options.hoistRedundantVectorTransfersOnTensor)
hoistRedundantVectorTransfersOnTensor(funcOp);
// Run CSE to cleanup after canonicalization.
OpPassManager dynamicPM("builtin.func");
dynamicPM.addPass(createCSEPass());
if (failed(runPipeline(dynamicPM, funcOp)))
return signalPassFailure();
}
LinalgEnablingOptions options;

9
mlir/test/Dialect/Linalg/codegen-strategy.mlir
View File

@ -40,14 +40,19 @@ func @matmul(%arg0: tensor<72x72xf32>, %arg1: tensor<72x72xf32>, %arg2: tensor<7
// -----
// CHECK-PAD-DAG: #[[MAP0:[0-9a-z]+]] = affine_map<(d0) -> (16, -d0 + 72)>
// CHECK-PAD: func @matmul(
func @matmul(%arg0: tensor<72x72xf32>, %arg1: tensor<72x72xf32>, %arg2: tensor<72x72xf32>) -> tensor<72x72xf32> {
// Check the padding of the input operands has been hoisted out of the tile loop nest.
// CHECK-PAD-COUNT=2: linalg.pad_tensor %{{.*}} nofold
// CHECK-PAD-COUNT=3: scf.for
// CHECK-PAD: scf.for
// Check CSE eliminates the duplicate min operations introduced by tiling.
// CHECK-PAD: affine.min #[[MAP0]]
// CHECK-PAD-NOT: affine.min #[[MAP0]]
// CHECK-PAD-COUNT=2: scf.for
// CHECK-PAD: linalg.matmul
%0 = linalg.matmul ins(%arg0, %arg1: tensor<72x72xf32>, tensor<72x72xf32>) outs(%arg2: tensor<72x72xf32>) -> tensor<72x72xf32>
return %0 : tensor<72x72xf32>
}