[MLIR][HLO] Use canonicalization patterns in broadcast propagation pass

Replace local canonicalization patterns with those from upstream.

PiperOrigin-RevId: 376794178

lib/Dialect/mhlo/transforms/move_up_dynamic_broadcasts_for_fusion.cc

@@ -276,7 +276,8 @@ struct MergeAssumingOpsPattern : public OpRewritePattern<shape::AssumingOp> {
               llvm::dyn_cast<shape::AssumingYieldOp>(body_a->getTerminator());
           for (auto pair :
                llvm::zip(preceding_op->getResults(), yield_op_a.operands())) {
-            mapping.map(std::get<0>(pair), mapping.lookup(std::get<1>(pair)));
+            mapping.map(std::get<0>(pair),
+                        mapping.lookupOrDefault(std::get<1>(pair)));
           }
 
           // Copy op's body.
@@ -306,61 +307,6 @@ struct MergeAssumingOpsPattern : public OpRewritePattern<shape::AssumingOp> {
   }
 };
 
-// Eliminate casted extent tensors. Instead, produce the concrete extent tensor
-// type where possible.
-struct CanonicalizeCastedShapeOfOpPattern
-    : public OpRewritePattern<tensor::CastOp> {
-  using OpRewritePattern<tensor::CastOp>::OpRewritePattern;
-
-  LogicalResult matchAndRewrite(tensor::CastOp op,
-                                PatternRewriter &rewriter) const override {
-    // Only merge tensor cast into `shape_of` ops.
-    auto shape_of_op = op.source().getDefiningOp<shape::ShapeOfOp>();
-    if (!shape_of_op) return failure();
-
-    // Desired type must be an extent tensor type.
-    auto result_ty = op.getType().dyn_cast<RankedTensorType>();
-    if (!result_ty || result_ty.getRank() != 1 ||
-        !result_ty.getElementType().isIndex())
-      return failure();
-
-    rewriter.replaceOpWithNewOp<shape::ShapeOfOp>(op, result_ty,
-                                                  shape_of_op.arg());
-    if (shape_of_op->getUses().empty()) rewriter.eraseOp(shape_of_op);
-    return success();
-  }
-};
-
-// TODO(frgossen): Remove this once it has landed upstream.
-struct CanonicalizeBroadcastPattern
-    : public OpRewritePattern<shape::BroadcastOp> {
-  using OpRewritePattern<shape::BroadcastOp>::OpRewritePattern;
-
-  LogicalResult matchAndRewrite(shape::BroadcastOp op,
-                                PatternRewriter &rewriter) const override {
-    // Only concretize dynamic extent tensor result types.
-    auto resultTy = op.getType().dyn_cast<RankedTensorType>();
-    if (!resultTy || !resultTy.isDynamicDim(0)) return failure();
-
-    // Infer resulting shape rank if possible.
-    int64_t maxRank = 0;
-    for (Value shape : op.shapes()) {
-      if (auto extentTensorTy = shape.getType().dyn_cast<RankedTensorType>()) {
-        // Cannot infer resulting shape rank if any operand is dynamically
-        // ranked.
-        if (extentTensorTy.isDynamicDim(0)) return failure();
-        maxRank = std::max(maxRank, extentTensorTy.getDimSize(0));
-      }
-    }
-
-    auto newOp = rewriter.create<shape::BroadcastOp>(
-        op.getLoc(), RankedTensorType::get({maxRank}, rewriter.getIndexType()),
-        op.shapes());
-    rewriter.replaceOpWithNewOp<tensor::CastOp>(op, op.getType(), newOp);
-    return success();
-  }
-};
-
 // TODO(frgossen): Only move up broadcasting operations if there is a consumer.
 struct MoveUpBroadcastInDimOpPattern
     : public OpRewritePattern<DynamicBroadcastInDimOp> {
@@ -432,8 +378,6 @@ void PopulateMoveUpDynamicBroadcastsForFusionPatterns(
     MLIRContext *context, OwningRewritePatternList *patterns) {
   // clang-format off
   patterns->insert<
-      CanonicalizeBroadcastPattern,
-      CanonicalizeCastedShapeOfOpPattern,
       InlineBroadcastedShapeOperandsPattern<shape::CstrBroadcastableOp>,
       MergeAssumingOpsPattern,
       MoveIntoAssumingOpPattern<shape::ShapeOfOp>,
@@ -443,6 +387,7 @@ void PopulateMoveUpDynamicBroadcastsForFusionPatterns(
       MoveUpBroadcastInDimOpPattern,
       ShapeReificationPattern>(context);
   // clang-format on
+  shape::BroadcastOp::getCanonicalizationPatterns(*patterns, context);
   tensor::CastOp::getCanonicalizationPatterns(*patterns, context);
 }