[Mlir-commits] [mlir] 1b012a9 - Fix broken docs links (WritingAPass.md was renamed PassManagement.md)
Mehdi Amini
llvmlistbot at llvm.org
Sat Apr 18 21:39:22 PDT 2020
Author: Mehdi Amini
Date: 2020-04-19T04:38:56Z
New Revision: 1b012a9146b85d30083a47d4929e86f843a5938d
URL: https://github.com/llvm/llvm-project/commit/1b012a9146b85d30083a47d4929e86f843a5938d
DIFF: https://github.com/llvm/llvm-project/commit/1b012a9146b85d30083a47d4929e86f843a5938d.diff
LOG: Fix broken docs links (WritingAPass.md was renamed PassManagement.md)
Added:
Modified:
mlir/docs/SymbolsAndSymbolTables.md
mlir/docs/Traits.md
mlir/docs/Tutorials/QuickstartRewrites.md
mlir/docs/Tutorials/Toy/Ch-4.md
mlir/docs/Tutorials/Toy/Ch-6.md
Removed:
################################################################################
diff --git a/mlir/docs/SymbolsAndSymbolTables.md b/mlir/docs/SymbolsAndSymbolTables.md
index f4d5d6b446b8..c004435fc040 100644
--- a/mlir/docs/SymbolsAndSymbolTables.md
+++ b/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).
diff --git a/mlir/docs/Traits.md b/mlir/docs/Traits.md
index 72f7d8a2b8f3..5931fd3f9698 100644
--- a/mlir/docs/Traits.md
+++ b/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
diff --git a/mlir/docs/Tutorials/QuickstartRewrites.md b/mlir/docs/Tutorials/QuickstartRewrites.md
index 71e8f0164fc9..ac33aee3c8fc 100644
--- a/mlir/docs/Tutorials/QuickstartRewrites.md
+++ b/mlir/docs/Tutorials/QuickstartRewrites.md
@@ -251,5 +251,5 @@ constant propagation or other, unrelated, optimization passes.
Optimization passes that do not fit/are
diff icult 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.
diff --git a/mlir/docs/Tutorials/Toy/Ch-4.md b/mlir/docs/Tutorials/Toy/Ch-4.md
index dcdf8d83840d..11e6ddf50120 100644
--- a/mlir/docs/Tutorials/Toy/Ch-4.md
+++ b/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.
diff --git a/mlir/docs/Tutorials/Toy/Ch-6.md b/mlir/docs/Tutorials/Toy/Ch-6.md
index 2808961f378f..734eafb0a99b 100644
--- a/mlir/docs/Tutorials/Toy/Ch-6.md
+++ b/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
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