Fix broken docs links (WritingAPass.md was renamed PassManagement.md)

mlir/docs/SymbolsAndSymbolTables.md

@@ -5,7 +5,7 @@
 With [Regions](LangRef.md#regions), the multi-level aspect of MLIR is structural
 in the IR. A lot of infrastructure within the compiler is built around this
 nesting structure; including the processing of operations within the
-[pass manager](WritingAPass.md#pass-manager). One advantage of the MLIR design
+[pass manager](PassManagement.md#pass-manager). One advantage of the MLIR design
 is that it is able to process operations in parallel, utilizing multiple
 threads. This is possible due to a property of the IR known as
 [`IsolatedFromAbove`](Traits.md#isolatedfromabove).

mlir/docs/Traits.md

@@ -217,7 +217,7 @@ foo.region_op {
 ```
 
 This trait is an important structural property of the IR, and enables operations
-to have [passes](WritingAPass.md) scheduled under them.
+to have [passes](PassManagement.md) scheduled under them.
 
 ### Single Block with Implicit Terminator
 

mlir/docs/Tutorials/QuickstartRewrites.md

@@ -251,5 +251,5 @@ constant propagation or other, unrelated, optimization passes.
 
 Optimization passes that do not fit/are difficult to specify in the above
 structure can be specified as general iterations across modules/functions. See
-[Writing a Pass](WritingAPass.md) for a general overview and introduction to
+[Writing a Pass](../PassManagement.md) for a general overview and introduction to
 optimization passes in MLIR.

mlir/docs/Tutorials/Toy/Ch-4.md

@@ -310,7 +310,7 @@ void MulOp::inferShapes() { getResult().setType(getOperand(0).getType()); }
 At this point, each of the necessary Toy operations provide a mechanism by which
 to infer their output shapes. The ShapeInferencePass is a FunctionPass: it will
 run on each Function in isolation. MLIR also supports general
-[OperationPasses](../../WritingAPass.md#operation-pass) that run on any isolated
+[OperationPasses](../../PassManagement.md#operation-pass) that run on any isolated
 operation (i.e. other function-like operations), but here our module only
 contains functions, so there is no need to generalize to all operations.
 

mlir/docs/Tutorials/Toy/Ch-6.md

@@ -315,7 +315,7 @@ $ echo 'def main() { print([[1, 2], [3, 4]]); }' | ./bin/toyc-ch6 -emit=jit
 
 You can also play with `-emit=mlir`, `-emit=mlir-affine`, `-emit=mlir-llvm`, and
 `-emit=llvm` to compare the various levels of IR involved. Also try options like
-[`--print-ir-after-all`](../../WritingAPass.md#ir-printing) to track the
+[`--print-ir-after-all`](../../PassManagement.md#ir-printing) to track the
 evolution of the IR throughout the pipeline.
 
 The example code used throughout this section can be found in