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minor spelling tweaks 

Closes tensorflow/mlir#250

COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/250 from kiszk:spelling_tweaks_201911 50fc04443723190b764e824b6fcd2469fecb56e6
PiperOrigin-RevId: 283733032
This commit is contained in:
Kazuaki Ishizaki 2019-12-04 04:58:12 -08:00 committed by A. Unique TensorFlower
parent da0b0b1a0e
commit c8c36e7979
8 changed files with 12 additions and 12 deletions
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4
mlir/examples/toy/Ch5/mlir/LowerToAffineLoops.cpp
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@ -241,8 +241,8 @@ struct TransposeOpLowering : public ConversionPattern {
// Generate an adaptor for the remapped operands of the TransposeOp.
// This allows for using the nice named accessors that are generated
// by the ODS.
toy::TransposeOpOperandAdaptor tranposeAdaptor(memRefOperands);
Value *input = tranposeAdaptor.input();
toy::TransposeOpOperandAdaptor transposeAdaptor(memRefOperands);
Value *input = transposeAdaptor.input();
// Transpose the elements by generating a load from the reverse
// indices.

4
mlir/examples/toy/Ch6/mlir/LowerToAffineLoops.cpp
View File

@ -241,8 +241,8 @@ struct TransposeOpLowering : public ConversionPattern {
// Generate an adaptor for the remapped operands of the TransposeOp.
// This allows for using the nice named accessors that are generated
// by the ODS.
toy::TransposeOpOperandAdaptor tranposeAdaptor(memRefOperands);
Value *input = tranposeAdaptor.input();
toy::TransposeOpOperandAdaptor transposeAdaptor(memRefOperands);
Value *input = transposeAdaptor.input();
// Transpose the elements by generating a load from the reverse
// indices.

4
mlir/examples/toy/Ch7/mlir/LowerToAffineLoops.cpp
View File

@ -241,8 +241,8 @@ struct TransposeOpLowering : public ConversionPattern {
// Generate an adaptor for the remapped operands of the TransposeOp.
// This allows for using the nice named accessors that are generated
// by the ODS.
toy::TransposeOpOperandAdaptor tranposeAdaptor(memRefOperands);
Value *input = tranposeAdaptor.input();
toy::TransposeOpOperandAdaptor transposeAdaptor(memRefOperands);
Value *input = transposeAdaptor.input();
// Transpose the elements by generating a load from the reverse
// indices.

2
mlir/g3doc/DeclarativeRewrites.md
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@ -50,7 +50,7 @@ features:
* Matching and generating ops with block arguments.
* Matching multi-result ops in nested patterns.
* Matching and generating variadic operand/result ops in nested patterns.
* Packing and unpacking variaidc operands/results during generation.
* Packing and unpacking variadic operands/results during generation.
* [`NativeCodeCall`](#native-code-call-transforming-the-generated-op)
returning more than one results.

2
mlir/g3doc/Dialects/SPIR-V.md
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@ -474,7 +474,7 @@ the representational differences between SPIR-V dialect and binary format:
Similarly, a few transformations are performed during deserialization:
* Instructions for execution environment requirements will be placed as
attribues on `spv.module`.
attributes on `spv.module`.
* `OpConstant*` instructions are materialized as `spv.constant` at each use
site.
* `OpPhi` instructions are converted to block arguments.

2
mlir/g3doc/OpDefinitions.md
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@ -263,7 +263,7 @@ TODO: Design and implement more primitive constraints
Similar to operands, results are specified inside the `dag`-typed `results`, led
by `outs`:
```tablgen
```tablegen
let results = (outs
<type-constraint>:$<result-name>,
...

2
mlir/g3doc/Tutorials/Toy/Ch-2.md
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@ -434,7 +434,7 @@ invariants of the operation have already been verified:
```tablegen
def ConstantOp : Toy_Op<"constant", [NoSideEffect]> {
// Provide a summary and description for this operation. This can be used to
// auto-generate documenatation of the operations within our dialect.
// auto-generate documentation of the operations within our dialect.
let summary = "constant operation";
let description = [{
Constant operation turns a literal into an SSA value. The data is attached

4
mlir/g3doc/Tutorials/Toy/Ch-5.md
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@ -118,8 +118,8 @@ struct TransposeOpLowering : public mlir::ConversionPattern {
// This allows for using the nice named accessors that are generated
// by the ODS. This adaptor is automatically provided by the ODS
// framework.
TransposeOpOperandAdaptor tranposeAdaptor(memRefOperands);
mlir::Value *input = tranposeAdaptor.input();
TransposeOpOperandAdaptor transposeAdaptor(memRefOperands);
mlir::Value *input = transposeAdaptor.input();
// Transpose the elements by generating a load from the reverse
// indices.