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Fix broken docs links (WritingAPass.md was renamed PassManagement.md)
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With [Regions](LangRef.md#regions), the multi-level aspect of MLIR is structural
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With [Regions](LangRef.md#regions), the multi-level aspect of MLIR is structural
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in the IR. A lot of infrastructure within the compiler is built around this
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in the IR. A lot of infrastructure within the compiler is built around this
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nesting structure; including the processing of operations within the
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nesting structure; including the processing of operations within the
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[pass manager](WritingAPass.md#pass-manager). One advantage of the MLIR design
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[pass manager](PassManagement.md#pass-manager). One advantage of the MLIR design
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is that it is able to process operations in parallel, utilizing multiple
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is that it is able to process operations in parallel, utilizing multiple
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threads. This is possible due to a property of the IR known as
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threads. This is possible due to a property of the IR known as
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[`IsolatedFromAbove`](Traits.md#isolatedfromabove).
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[`IsolatedFromAbove`](Traits.md#isolatedfromabove).
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@ -217,7 +217,7 @@ foo.region_op {
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```
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```
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This trait is an important structural property of the IR, and enables operations
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This trait is an important structural property of the IR, and enables operations
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to have [passes](WritingAPass.md) scheduled under them.
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to have [passes](PassManagement.md) scheduled under them.
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### Single Block with Implicit Terminator
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### Single Block with Implicit Terminator
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@ -251,5 +251,5 @@ constant propagation or other, unrelated, optimization passes.
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Optimization passes that do not fit/are difficult to specify in the above
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Optimization passes that do not fit/are difficult to specify in the above
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structure can be specified as general iterations across modules/functions. See
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structure can be specified as general iterations across modules/functions. See
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[Writing a Pass](WritingAPass.md) for a general overview and introduction to
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[Writing a Pass](../PassManagement.md) for a general overview and introduction to
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optimization passes in MLIR.
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optimization passes in MLIR.
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@ -310,7 +310,7 @@ void MulOp::inferShapes() { getResult().setType(getOperand(0).getType()); }
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At this point, each of the necessary Toy operations provide a mechanism by which
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At this point, each of the necessary Toy operations provide a mechanism by which
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to infer their output shapes. The ShapeInferencePass is a FunctionPass: it will
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to infer their output shapes. The ShapeInferencePass is a FunctionPass: it will
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run on each Function in isolation. MLIR also supports general
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run on each Function in isolation. MLIR also supports general
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[OperationPasses](../../WritingAPass.md#operation-pass) that run on any isolated
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[OperationPasses](../../PassManagement.md#operation-pass) that run on any isolated
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operation (i.e. other function-like operations), but here our module only
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operation (i.e. other function-like operations), but here our module only
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contains functions, so there is no need to generalize to all operations.
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contains functions, so there is no need to generalize to all operations.
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@ -315,7 +315,7 @@ $ echo 'def main() { print([[1, 2], [3, 4]]); }' | ./bin/toyc-ch6 -emit=jit
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You can also play with `-emit=mlir`, `-emit=mlir-affine`, `-emit=mlir-llvm`, and
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You can also play with `-emit=mlir`, `-emit=mlir-affine`, `-emit=mlir-llvm`, and
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`-emit=llvm` to compare the various levels of IR involved. Also try options like
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`-emit=llvm` to compare the various levels of IR involved. Also try options like
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[`--print-ir-after-all`](../../WritingAPass.md#ir-printing) to track the
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[`--print-ir-after-all`](../../PassManagement.md#ir-printing) to track the
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evolution of the IR throughout the pipeline.
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evolution of the IR throughout the pipeline.
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The example code used throughout this section can be found in
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The example code used throughout this section can be found in
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