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	}			}
	```			```

	## Taking Advantage of Affine Optimization			## Taking Advantage of Affine Optimization

	Our naive lowering is correct, but it leaves a lot to be desired with regards to			Our naive lowering is correct, but it leaves a lot to be desired with regards to
	efficiency. For example, the lowering of `toy.mul` has generated some redundant			efficiency. For example, the lowering of `toy.mul` has generated some redundant
	loads. Let's look at how adding a few existing optimizations to the pipeline can			loads. Let's look at how adding a few existing optimizations to the pipeline can
	help clean this up. Adding the `LoopFusion` and `MemRefDataFlowOpt` passes to			help clean this up. Adding the `LoopFusion` and `AffineScalarReplacement` passes
	the pipeline gives the following result:			to the pipeline gives the following result:

	```mlir			```mlir
	func.func @main() {			func.func @main() {
	%cst = arith.constant 1.000000e+00 : f64			%cst = arith.constant 1.000000e+00 : f64
	%cst_0 = arith.constant 2.000000e+00 : f64			%cst_0 = arith.constant 2.000000e+00 : f64
	%cst_1 = arith.constant 3.000000e+00 : f64			%cst_1 = arith.constant 3.000000e+00 : f64
	%cst_2 = arith.constant 4.000000e+00 : f64			%cst_2 = arith.constant 4.000000e+00 : f64
	%cst_3 = arith.constant 5.000000e+00 : f64			%cst_3 = arith.constant 5.000000e+00 : f64
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[mlir][doc] rename MemRefDataFlowOpt -> AffineScalarReplacementAbandonedPublicDraft