229 lines
8.8 KiB
C++
229 lines
8.8 KiB
C++
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//===- StructuredOpsUtils.cpp - Utilities used by structured ops ----------===//
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//
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// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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//
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//===----------------------------------------------------------------------===//
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#include "mlir/Dialect/Utils/StructuredOpsUtils.h"
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#include "mlir/IR/AffineMap.h"
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#include "mlir/IR/Builders.h"
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#include "mlir/IR/BuiltinAttributes.h"
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#include "mlir/IR/IRMapping.h"
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#include "llvm/ADT/StringSet.h"
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#include "mlir/Dialect/Utils/DialectUtilsEnums.cpp.inc"
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using namespace mlir;
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bool mlir::isRowMajorMatmul(ArrayAttr indexingMaps) {
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if (indexingMaps.size() != 3)
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return false;
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AffineMap map0 = cast<AffineMapAttr>(indexingMaps[0]).getValue();
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AffineMap map1 = cast<AffineMapAttr>(indexingMaps[1]).getValue();
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AffineMap map2 = cast<AffineMapAttr>(indexingMaps[2]).getValue();
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if (map0.getNumResults() != 2 || map1.getNumResults() != 2 ||
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map2.getNumResults() != 2 || map0.getNumInputs() != 3 ||
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map1.getNumInputs() != 3 || map2.getNumInputs() != 3) {
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return false;
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}
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// Extract dimensions for MxK * KxN -> MxN
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AffineExpr m = map2.getResult(0);
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AffineExpr n = map2.getResult(1);
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AffineExpr k = map0.getResult(1);
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auto *context = indexingMaps.getContext();
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auto mapA = AffineMapAttr::get(AffineMap::get(3, 0, {m, k}, context));
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auto mapB = AffineMapAttr::get(AffineMap::get(3, 0, {k, n}, context));
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auto mapC = AffineMapAttr::get(AffineMap::get(3, 0, {m, n}, context));
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auto maps = ArrayAttr::get(context, {mapA, mapB, mapC});
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return indexingMaps == maps;
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}
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bool mlir::isColumnMajorMatmul(ArrayAttr indexingMaps) {
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if (indexingMaps.size() != 3)
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return false;
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AffineMap map0 = cast<AffineMapAttr>(indexingMaps[0]).getValue();
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AffineMap map1 = cast<AffineMapAttr>(indexingMaps[1]).getValue();
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AffineMap map2 = cast<AffineMapAttr>(indexingMaps[2]).getValue();
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if (map0.getNumResults() != 2 || map1.getNumResults() != 2 ||
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map2.getNumResults() != 2 || map0.getNumInputs() != 3 ||
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map1.getNumInputs() != 3 || map2.getNumInputs() != 3) {
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return false;
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}
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// Extract dimensions for KxM * NxK -> NxM
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AffineExpr n = map2.getResult(0);
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AffineExpr m = map2.getResult(1);
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AffineExpr k = map0.getResult(0);
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auto *context = indexingMaps.getContext();
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auto mapA = AffineMapAttr::get(AffineMap::get(3, 0, {k, m}, context));
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auto mapB = AffineMapAttr::get(AffineMap::get(3, 0, {n, k}, context));
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auto mapC = AffineMapAttr::get(AffineMap::get(3, 0, {n, m}, context));
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auto maps = ArrayAttr::get(context, {mapA, mapB, mapC});
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return indexingMaps == maps;
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}
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bool mlir::isRowMajorBatchMatmul(ArrayAttr indexingMaps) {
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if (indexingMaps.size() != 3)
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return false;
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AffineMap map0 = cast<AffineMapAttr>(indexingMaps[0]).getValue();
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AffineMap map1 = cast<AffineMapAttr>(indexingMaps[1]).getValue();
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AffineMap map2 = cast<AffineMapAttr>(indexingMaps[2]).getValue();
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if (map0.getNumResults() != 3 || map1.getNumResults() != 3 ||
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map2.getNumResults() != 3 || map0.getNumInputs() != 4 ||
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map1.getNumInputs() != 4 || map2.getNumInputs() != 4) {
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return false;
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}
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// Extract dimensions for BxMxK * BxKxN -> BxMxN
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AffineExpr b = map2.getResult(0);
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AffineExpr m = map2.getResult(1);
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AffineExpr n = map2.getResult(2);
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AffineExpr k = map0.getResult(2);
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auto *context = indexingMaps.getContext();
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auto mapA = AffineMapAttr::get(AffineMap::get(4, 0, {b, m, k}, context));
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auto mapB = AffineMapAttr::get(AffineMap::get(4, 0, {b, k, n}, context));
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auto mapC = AffineMapAttr::get(AffineMap::get(4, 0, {b, m, n}, context));
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auto maps = ArrayAttr::get(context, {mapA, mapB, mapC});
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return indexingMaps == maps;
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}
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bool mlir::isVecmat(ArrayAttr indexingMaps) {
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if (indexingMaps.size() != 3)
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return false;
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AffineMap map0 = cast<AffineMapAttr>(indexingMaps[0]).getValue();
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AffineMap map1 = cast<AffineMapAttr>(indexingMaps[1]).getValue();
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AffineMap map2 = cast<AffineMapAttr>(indexingMaps[2]).getValue();
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if (map0.getNumResults() != 1 || map1.getNumResults() != 2 ||
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map2.getNumResults() != 1 || map0.getNumInputs() != 2 ||
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map1.getNumInputs() != 2 || map2.getNumInputs() != 2) {
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return false;
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}
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// Extract dimensions for K * KxN -> N
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AffineExpr k = map0.getResult(0);
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AffineExpr n = map2.getResult(0);
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auto *context = indexingMaps.getContext();
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auto mapA = AffineMapAttr::get(AffineMap::get(2, 0, {k}, context));
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auto mapB = AffineMapAttr::get(AffineMap::get(2, 0, {k, n}, context));
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auto mapC = AffineMapAttr::get(AffineMap::get(2, 0, {n}, context));
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auto maps = ArrayAttr::get(context, {mapA, mapB, mapC});
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return indexingMaps == maps;
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}
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bool mlir::isBatchVecmat(ArrayAttr indexingMaps) {
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if (indexingMaps.size() != 3)
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return false;
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AffineMap map0 = cast<AffineMapAttr>(indexingMaps[0]).getValue();
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AffineMap map1 = cast<AffineMapAttr>(indexingMaps[1]).getValue();
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AffineMap map2 = cast<AffineMapAttr>(indexingMaps[2]).getValue();
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if (map0.getNumResults() != 2 || map1.getNumResults() != 3 ||
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map2.getNumResults() != 2 || map0.getNumInputs() != 3 ||
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map1.getNumInputs() != 3 || map2.getNumInputs() != 3) {
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return false;
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}
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// Extract dimensions for B*K * B*K*N -> B*N
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AffineExpr b = map0.getResult(0);
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AffineExpr k = map0.getResult(1);
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AffineExpr n = map2.getResult(1);
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auto *context = indexingMaps.getContext();
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auto mapA = AffineMapAttr::get(AffineMap::get(3, 0, {b, k}, context));
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auto mapB = AffineMapAttr::get(AffineMap::get(3, 0, {b, k, n}, context));
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auto mapC = AffineMapAttr::get(AffineMap::get(3, 0, {b, n}, context));
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auto maps = ArrayAttr::get(context, {mapA, mapB, mapC});
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return indexingMaps == maps;
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}
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bool mlir::isMatvec(ArrayAttr indexingMaps) {
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if (indexingMaps.size() != 3)
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return false;
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AffineMap map0 = cast<AffineMapAttr>(indexingMaps[0]).getValue();
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AffineMap map1 = cast<AffineMapAttr>(indexingMaps[1]).getValue();
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AffineMap map2 = cast<AffineMapAttr>(indexingMaps[2]).getValue();
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if (map0.getNumResults() != 2 || map1.getNumResults() != 1 ||
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map2.getNumResults() != 1 || map0.getNumInputs() != 2 ||
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map1.getNumInputs() != 2 || map2.getNumInputs() != 2) {
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return false;
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}
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// Extract dimensions for N*K * K -> N
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AffineExpr k = map1.getResult(0);
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AffineExpr n = map2.getResult(0);
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auto *context = indexingMaps.getContext();
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auto mapA = AffineMapAttr::get(AffineMap::get(2, 0, {n, k}, context));
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auto mapB = AffineMapAttr::get(AffineMap::get(2, 0, {k}, context));
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auto mapC = AffineMapAttr::get(AffineMap::get(2, 0, {n}, context));
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auto maps = ArrayAttr::get(context, {mapA, mapB, mapC});
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return indexingMaps == maps;
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}
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bool mlir::isBatchMatvec(ArrayAttr indexingMaps) {
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if (indexingMaps.size() != 3)
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return false;
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AffineMap map0 = cast<AffineMapAttr>(indexingMaps[0]).getValue();
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AffineMap map1 = cast<AffineMapAttr>(indexingMaps[1]).getValue();
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AffineMap map2 = cast<AffineMapAttr>(indexingMaps[2]).getValue();
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if (map0.getNumResults() != 3 || map1.getNumResults() != 2 ||
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map2.getNumResults() != 2 || map0.getNumInputs() != 3 ||
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map1.getNumInputs() != 3 || map2.getNumInputs() != 3) {
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return false;
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}
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// Extract dimensions for B*N*K * B*K -> B*N
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AffineExpr b = map0.getResult(0);
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AffineExpr k = map1.getResult(1);
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AffineExpr n = map2.getResult(1);
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auto *context = indexingMaps.getContext();
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auto mapA = AffineMapAttr::get(AffineMap::get(3, 0, {b, n, k}, context));
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auto mapB = AffineMapAttr::get(AffineMap::get(3, 0, {b, k}, context));
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auto mapC = AffineMapAttr::get(AffineMap::get(3, 0, {b, n}, context));
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auto maps = ArrayAttr::get(context, {mapA, mapB, mapC});
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return indexingMaps == maps;
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}
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Operation *mlir::clone(OpBuilder &b, Operation *op, TypeRange newResultTypes,
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ValueRange newOperands) {
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IRMapping bvm;
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OperationState state(op->getLoc(), op->getName(), newOperands, newResultTypes,
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op->getAttrs());
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for (Region &r : op->getRegions())
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r.cloneInto(state.addRegion(), bvm);
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return b.create(state);
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}
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Operation *mlir::cloneWithoutRegions(OpBuilder &b, Operation *op,
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TypeRange newResultTypes,
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ValueRange newOperands) {
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OperationState state(op->getLoc(), op->getName(), newOperands, newResultTypes,
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op->getAttrs());
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for (size_t cnt = 0, e = op->getNumRegions(); cnt < e; ++cnt)
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state.addRegion();
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return b.create(state);
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}
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SmallVector<NamedAttribute>
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mlir::getPrunedAttributeList(Operation *op, ArrayRef<StringRef> elidedAttrs) {
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llvm::StringSet<> elidedAttrsSet;
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elidedAttrsSet.insert(elidedAttrs.begin(), elidedAttrs.end());
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SmallVector<NamedAttribute> attrs;
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for (auto attr : op->getAttrs()) {
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if (elidedAttrsSet.count(attr.getName()))
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continue;
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attrs.push_back(attr);
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}
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return attrs;
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}
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