[mlir] Remove redundant shape.cstr_broadcastable canonicalization.

[tpopp]

Summary

These canonicalizations are already handled by folding which will occur
in a superset of situations, so they are being removed.

mlir/lib/Dialect/Shape/IR/Shape.cpp

if (auto inputOp = input.getDefiningOp<ShapeOfOp>()) {
     return success();
   } else if (auto inputOp = input.getDefiningOp<ConstShapeOp>()) {
     shapeValues = llvm::to_vector<6>(inputOp.shape().getValues<int64_t>());
     return success();
   } else {
     return failure();
   }
 }
-
-// For shapes that were created by some operations, we can obtain partial
-// information on the shapes and sometimes determine if they will be
-// broadcastable with that.
-struct CstrBroadcastablePartialInfo
-    : public OpRewritePattern<CstrBroadcastableOp> {
-  using OpRewritePattern<CstrBroadcastableOp>::OpRewritePattern;
-
-  LogicalResult matchAndRewrite(CstrBroadcastableOp op,
-                                PatternRewriter &rewriter) const override {
-    SmallVector<int64_t, 6> lhsShape, rhsShape;
-    if (failed(getShapeVec(op.lhs(), lhsShape)))
-      return failure();
-    if (failed(getShapeVec(op.rhs(), rhsShape)))
-      return failure();
-    if (!OpTrait::util::staticallyKnownBroadcastable(lhsShape, rhsShape))
-      return failure();
-
-    rewriter.replaceOpWithNewOp<ConstWitnessOp>(op.getOperation(), true);
-    return success();
-  }
-};
-
-// Scalars are always broadcastable.
-struct CstrBroadcastableScalar : public OpRewritePattern<CstrBroadcastableOp> {
-  using OpRewritePattern<CstrBroadcastableOp>::OpRewritePattern;
-
-  LogicalResult matchAndRewrite(CstrBroadcastableOp op,
-                                PatternRewriter &rewriter) const override {
-    SmallVector<int64_t, 6> shape;
-    if (failed(getShapeVec(op.lhs(), shape)) || shape.size() > 0)
-      return failure();
-    if (failed(getShapeVec(op.rhs(), shape)) || shape.size() > 0)
-      return failure();
-
-    rewriter.replaceOpWithNewOp<ConstWitnessOp>(op.getOperation(), true);
-    return success();
-  }
-};
-
 } // namespace
 
 void CstrBroadcastableOp::getCanonicalizationPatterns(
     OwningRewritePatternList &patterns, MLIRContext *context) {
   // Canonicalization patterns have overlap with the considerations during
   // folding in case additional shape information is inferred at some point that
   // does not result in folding.
-  patterns.insert<CstrBroadcastableEqOps, CstrBroadcastablePartialInfo,
-                  CstrBroadcastableScalar>(context);
+  patterns.insert<CstrBroadcastableEqOps>(context);
 }
 
 OpFoldResult CstrBroadcastableOp::fold(ArrayRef<Attribute> operands) {
   // Both operands are not needed if one is a scalar.
   if (operands[0] &&
       operands[0].cast<DenseIntElementsAttr>().getNumElements() == 0)
     return BoolAttr::get(true, getContext());
   if (operands[1] &&