Revert "update docs"

This reverts commit 79a7960.

Author: cxy-1993

mlir/docs/Canonicalization.md

@@ -33,11 +33,6 @@ together.
 
 Some important things to think about w.r.t. canonicalization patterns:
 
-*   The goal of canonicalization is to make subsequent optimizations more
-    effective. Therefore, performance improvements are not necessary for
-    canonicalization. But it is generally better to define a canonicalize
-    pattern that do not harm the performance.
-
 *   Pass pipelines should not rely on the canonicalizer pass for correctness.
     They should work correctly with all instances of the canonicalization pass
     removed.
@@ -56,60 +51,6 @@ Some important things to think about w.r.t. canonicalization patterns:
 *   It is always good to eliminate operations entirely when possible, e.g. by
     folding known identities (like "x + 0 = x").
 
-*   Canonicalize isn't a great place to put pattens with expensive compile time
-    (i.e. have O(n) complexity) or complicated cost models.
-
-*   Canonicalize shouldn't drop the semantic of original operation.
-
-For example, a pattern that transform
-
-```
-  %res = vector.transpose %0, [1, 0] : vector<nx1x<eltty>> to vector<1xnx<elty>>
-```
-
-to
-
-```
-  %res = vector.shape_cast %0 : vector<nx1x<eltty>> to vector<1xnx<elty>>
-```
-
-is not a good canonicalize pattern because it drops the transpose semantic.
-
-
-A pattern that transform (linalg.transpose is only use of %broadcast)
-
-```
-  %broadcast = linalg.broadcast
-      ins(%input : tensor<2x4x5xf32>)
-      outs(%init1 : tensor<1x2x3x4x5x6xf32>)
-      dimensions = [0, 2, 5]
-  %transpose = linalg.transpose
-      ins(%broadcast : tensor<1x2x3x4x5x6xf32>)
-      outs(%init2 : tensor<1x6x2x3x5x4xf32>)
-      permutation = [0, 5, 1, 2, 4, 3]
-```
-
-to
-
-```
-  %tranpose = linalg.transpose
-      ins(%input : tensor<2x4x5xf32>)
-      outs(%tmp_init : tensor<2x5x4xf32>)
-      permutation = [0, 2, 1]
-  %broadcast = linalg.broadcast
-      ins(%transpose : tensor<2x5x4xf32>)
-      outs(%init2 : tensor<1x6x2x3x5x4xf32>)
-      dimensions = [0, 3, 1]
-```
-
-is a good canonicalize pattern because:
-
-1. This pattern is converge.
-2. This pattern always transforms the program towards reducing the amount of
-   computational data, which is a clear lattice.
-3. This is not a one-off pattern, new matches may be generated during the
-   application process.
-
 ## Globally Applied Rules
 
 These transformations are applied to all levels of IR:
@@ -248,7 +189,7 @@ each of the operands, returning the corresponding constant attribute. These
 operands are those that implement the `ConstantLike` trait. If any of the
 operands are non-constant, a null `Attribute` value is provided instead. For
 example, if MyOp provides three operands [`a`, `b`, `c`], but only `b` is
-constant then `adaptor` will return Attribute() for `getA()` and `getC()`,
+constant then `adaptor` will return Attribute() for `getA()` and `getC()`, 
 and b-value for `getB()`.
 
 Also above, is the use of `OpFoldResult`. This class represents the possible