[NFC] Fix some typos in libc and mlir comments #133374
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@llvm/pr-subscribers-mlir-gpu @llvm/pr-subscribers-mlir Author: Qinkun Bao (qinkunbao) ChangesFull diff: https://github.com/llvm/llvm-project/pull/133374.diff 10 Files Affected:
diff --git a/libc/src/__support/CPP/atomic.h b/libc/src/__support/CPP/atomic.h
index f21755293102e..2f00b3ed32811 100644
--- a/libc/src/__support/CPP/atomic.h
+++ b/libc/src/__support/CPP/atomic.h
@@ -97,7 +97,7 @@ template <typename T> struct Atomic {
LIBC_INLINE constexpr Atomic() = default;
- // Intializes the value without using atomic operations.
+ // Initializes the value without using atomic operations.
LIBC_INLINE constexpr Atomic(value_type v) : val(v) {}
LIBC_INLINE Atomic(const Atomic &) = delete;
diff --git a/mlir/include/mlir/Analysis/Presburger/Simplex.h b/mlir/include/mlir/Analysis/Presburger/Simplex.h
index 4c40c4cdcb655..d89bbe37a6b3e 100644
--- a/mlir/include/mlir/Analysis/Presburger/Simplex.h
+++ b/mlir/include/mlir/Analysis/Presburger/Simplex.h
@@ -344,7 +344,7 @@ class SimplexBase {
SmallVector<UndoLogEntry, 8> undoLog;
/// Holds a vector of bases. The ith saved basis is the basis that should be
- /// restored when processing the ith occurrance of UndoLogEntry::RestoreBasis
+ /// restored when processing the ith occurrence of UndoLogEntry::RestoreBasis
/// in undoLog. This is used by getSnapshotBasis.
SmallVector<SmallVector<int, 8>, 8> savedBases;
@@ -367,7 +367,7 @@ class SimplexBase {
///
/// This does not directly support negative-valued variables, so it uses the big
/// M parameter trick to make all the variables non-negative. Basically we
-/// introduce an artifical variable M that is considered to have a value of
+/// introduce an artificial variable M that is considered to have a value of
/// +infinity and instead of the variables x, y, z, we internally use variables
/// M + x, M + y, M + z, which are now guaranteed to be non-negative. See the
/// documentation for SimplexBase for more details. M is also considered to be
@@ -561,7 +561,7 @@ struct SymbolicLexOpt {
/// negative for all values in the symbol domain, the row needs to be pivoted
/// irrespective of the precise value of the symbols. To answer queries like
/// "Is this symbolic sample always negative in the symbol domain?", we maintain
-/// a `LexSimplex domainSimplex` correponding to the symbol domain.
+/// a `LexSimplex domainSimplex` corresponding to the symbol domain.
///
/// In other cases, it may be that the symbolic sample is violated at some
/// values in the symbol domain and not violated at others. In this case,
diff --git a/mlir/include/mlir/AsmParser/AsmParser.h b/mlir/include/mlir/AsmParser/AsmParser.h
index 3c1bff1fbc7f1..33daf7ca26f49 100644
--- a/mlir/include/mlir/AsmParser/AsmParser.h
+++ b/mlir/include/mlir/AsmParser/AsmParser.h
@@ -47,7 +47,7 @@ parseAsmSourceFile(const llvm::SourceMgr &sourceMgr, Block *block,
/// not, an error diagnostic is emitted to the context and a null value is
/// returned.
/// If `numRead` is provided, it is set to the number of consumed characters on
-/// succesful parse. Otherwise, parsing fails if the entire string is not
+/// successful parse. Otherwise, parsing fails if the entire string is not
/// consumed.
/// Some internal copying can be skipped if the source string is known to be
/// null terminated.
@@ -58,7 +58,7 @@ Attribute parseAttribute(llvm::StringRef attrStr, MLIRContext *context,
/// This parses a single MLIR type to an MLIR context if it was valid. If not,
/// an error diagnostic is emitted to the context.
/// If `numRead` is provided, it is set to the number of consumed characters on
-/// succesful parse. Otherwise, parsing fails if the entire string is not
+/// successful parse. Otherwise, parsing fails if the entire string is not
/// consumed.
/// Some internal copying can be skipped if the source string is known to be
/// null terminated.
diff --git a/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_loose.mlir b/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_loose.mlir
index f9816584b4655..1af9dc6cf2061 100644
--- a/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_loose.mlir
+++ b/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_loose.mlir
@@ -41,7 +41,7 @@ module {
//
// Note: position for loose_compressed level can vary in the end,
- // therefore we loosly check it with {{.*}}.
+ // therefore we loosely check it with {{.*}}.
//
// CHECK: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 17
diff --git a/mlir/test/Integration/GPU/CUDA/TensorCore/sm80/transform-mma-sync-matmul-f16-f16-accum.mlir b/mlir/test/Integration/GPU/CUDA/TensorCore/sm80/transform-mma-sync-matmul-f16-f16-accum.mlir
index f38b9ddfaa10e..690c4d2636a44 100644
--- a/mlir/test/Integration/GPU/CUDA/TensorCore/sm80/transform-mma-sync-matmul-f16-f16-accum.mlir
+++ b/mlir/test/Integration/GPU/CUDA/TensorCore/sm80/transform-mma-sync-matmul-f16-f16-accum.mlir
@@ -96,21 +96,21 @@ func.func @main() {
%f0 = arith.constant 0.0e+00 : f16
%c32 = arith.constant 32 : index
- // Intialize the lhs matrix with a linspace function.
+ // Initialize the lhs matrix with a linspace function.
scf.for %r = %c0 to %M step %c1 {
scf.for %c = %c0 to %K step %c1 {
%idx = func.call @compute_linspace_val(%r, %c, %K) : (index, index, index) -> f16
memref.store %idx, %lhs[%r, %c] : !lhs_memref_type
}
}
- // Intialize the rhs matrix with a linspace function.
+ // Initialize the rhs matrix with a linspace function.
scf.for %r = %c0 to %K step %c1 {
scf.for %c = %c0 to %N step %c1 {
%idx = func.call @compute_linspace_val(%r, %c, %N) : (index, index, index) -> f16
memref.store %idx, %rhs[%r, %c] : !rhs_memref_type
}
}
- // Intialize the rhs matrix with a linspace function.
+ // Initialize the rhs matrix with a linspace function.
scf.for %r = %c0 to %M step %c1 {
scf.for %c = %c0 to %N step %c1 {
%idx = func.call @compute_linspace_val(%r, %c, %N) : (index, index, index) -> f16
diff --git a/mlir/test/Integration/GPU/CUDA/TensorCore/sm80/transform-mma-sync-matmul-f32.mlir b/mlir/test/Integration/GPU/CUDA/TensorCore/sm80/transform-mma-sync-matmul-f32.mlir
index 1eae22f0b85d0..2eef2ff8f3564 100644
--- a/mlir/test/Integration/GPU/CUDA/TensorCore/sm80/transform-mma-sync-matmul-f32.mlir
+++ b/mlir/test/Integration/GPU/CUDA/TensorCore/sm80/transform-mma-sync-matmul-f32.mlir
@@ -47,21 +47,21 @@ func.func @main() {
%f0 = arith.constant 0.0e+00 : f32
%c32 = arith.constant 32 : index
- // Intialize the lhs matrix with a linspace function.
+ // Initialize the lhs matrix with a linspace function.
scf.for %r = %c0 to %M step %c1 {
scf.for %c = %c0 to %K step %c1 {
%idx = func.call @compute_linspace_val(%r, %c, %K) : (index, index, index) -> f32
memref.store %idx, %lhs[%r, %c] : !lhs_memref_type
}
}
- // Intialize the rhs matrix with a linspace function.
+ // Initialize the rhs matrix with a linspace function.
scf.for %r = %c0 to %K step %c1 {
scf.for %c = %c0 to %N step %c1 {
%idx = func.call @compute_linspace_val(%r, %c, %N) : (index, index, index) -> f32
memref.store %idx, %rhs[%r, %c] : !rhs_memref_type
}
}
- // Intialize the rhs matrix with a linspace function.
+ // Initialize the rhs matrix with a linspace function.
scf.for %r = %c0 to %M step %c1 {
scf.for %c = %c0 to %N step %c1 {
%idx = func.call @compute_linspace_val(%r, %c, %N) : (index, index, index) -> f32
diff --git a/mlir/test/Integration/GPU/CUDA/TensorCore/wmma-matmul-f16.mlir b/mlir/test/Integration/GPU/CUDA/TensorCore/wmma-matmul-f16.mlir
index 01144b3495866..2d91aa8f12b5e 100644
--- a/mlir/test/Integration/GPU/CUDA/TensorCore/wmma-matmul-f16.mlir
+++ b/mlir/test/Integration/GPU/CUDA/TensorCore/wmma-matmul-f16.mlir
@@ -20,7 +20,7 @@ func.func @main() {
%c32 = arith.constant 32 : index
%c1 = arith.constant 1 : index
- // Intialize the Input matrix with the column index in each row.
+ // Initialize the Input matrix with the column index in each row.
scf.for %arg0 = %c0 to %c16 step %c1 {
scf.for %arg1 = %c0 to %c16 step %c1 {
%2 = arith.index_cast %arg1 : index to i16
@@ -28,7 +28,7 @@ func.func @main() {
memref.store %3, %0[%arg0, %arg1] : memref<16x16xf16>
}
}
- // Intialize the accumulator matrix with zeros.
+ // Initialize the accumulator matrix with zeros.
scf.for %arg0 = %c0 to %c16 step %c1 {
scf.for %arg1 = %c0 to %c16 step %c1 {
memref.store %f0, %22[%arg0, %arg1] : memref<16x16xf16>
diff --git a/mlir/test/Integration/GPU/CUDA/TensorCore/wmma-matmul-f32-bare-ptr.mlir b/mlir/test/Integration/GPU/CUDA/TensorCore/wmma-matmul-f32-bare-ptr.mlir
index 3301e68e5d123..18b1c1a1b0b45 100644
--- a/mlir/test/Integration/GPU/CUDA/TensorCore/wmma-matmul-f32-bare-ptr.mlir
+++ b/mlir/test/Integration/GPU/CUDA/TensorCore/wmma-matmul-f32-bare-ptr.mlir
@@ -20,13 +20,13 @@ func.func @main() {
%c32 = arith.constant 32 : index
%c1 = arith.constant 1 : index
- // Intialize the Input matrix with ones.
+ // Initialize the Input matrix with ones.
scf.for %arg0 = %c0 to %c16 step %c1 {
scf.for %arg1 = %c0 to %c16 step %c1 {
memref.store %f1, %h0[%arg0, %arg1] : memref<16x16xf16>
}
}
- // Intialize the accumulator matrix with zeros.
+ // Initialize the accumulator matrix with zeros.
scf.for %arg0 = %c0 to %c16 step %c1 {
scf.for %arg1 = %c0 to %c16 step %c1 {
memref.store %f0, %h_out[%arg0, %arg1] : memref<16x16xf32>
diff --git a/mlir/test/Integration/GPU/CUDA/TensorCore/wmma-matmul-f32.mlir b/mlir/test/Integration/GPU/CUDA/TensorCore/wmma-matmul-f32.mlir
index 4db9aa056e757..78895cda72c5a 100644
--- a/mlir/test/Integration/GPU/CUDA/TensorCore/wmma-matmul-f32.mlir
+++ b/mlir/test/Integration/GPU/CUDA/TensorCore/wmma-matmul-f32.mlir
@@ -18,13 +18,13 @@ func.func @main() {
%c32 = arith.constant 32 : index
%c1 = arith.constant 1 : index
- // Intialize the Input matrix with ones.
+ // Initialize the Input matrix with ones.
scf.for %arg0 = %c0 to %c16 step %c1 {
scf.for %arg1 = %c0 to %c16 step %c1 {
memref.store %f1, %0[%arg0, %arg1] : memref<16x16xf16>
}
}
- // Intialize the accumulator matrix with zeros.
+ // Initialize the accumulator matrix with zeros.
scf.for %arg0 = %c0 to %c16 step %c1 {
scf.for %arg1 = %c0 to %c16 step %c1 {
memref.store %f0, %22[%arg0, %arg1] : memref<16x16xf32>
diff --git a/mlir/test/mlir-tblgen/op-properties-predicates.td b/mlir/test/mlir-tblgen/op-properties-predicates.td
index de59f5166d7e1..9834edd0cbb57 100644
--- a/mlir/test/mlir-tblgen/op-properties-predicates.td
+++ b/mlir/test/mlir-tblgen/op-properties-predicates.td
@@ -36,7 +36,7 @@ def OpWithPredicates : NS_Op<"op_with_predicates"> {
}
// CHECK-LABEL: OpWithPredicates::verifyInvariantsImpl()
-// Note: for test readibility, we capture [[maybe_unused]] into the variable maybe_unused
+// Note: for test readability, we capture [[maybe_unused]] into the variable maybe_unused
// CHECK: [[maybe_unused:\[\[maybe_unused\]\]]] int64_t tblgen_scalar = this->getScalar();
// CHECK: if (!((tblgen_scalar >= 0)))
// CHECK-NEXT: return emitOpError("property 'scalar' failed to satisfy constraint: non-negative int64_t");
|
|
@llvm/pr-subscribers-mlir-core Author: Qinkun Bao (qinkunbao) ChangesFull diff: https://github.com/llvm/llvm-project/pull/133374.diff 10 Files Affected:
diff --git a/libc/src/__support/CPP/atomic.h b/libc/src/__support/CPP/atomic.h
index f21755293102e..2f00b3ed32811 100644
--- a/libc/src/__support/CPP/atomic.h
+++ b/libc/src/__support/CPP/atomic.h
@@ -97,7 +97,7 @@ template <typename T> struct Atomic {
LIBC_INLINE constexpr Atomic() = default;
- // Intializes the value without using atomic operations.
+ // Initializes the value without using atomic operations.
LIBC_INLINE constexpr Atomic(value_type v) : val(v) {}
LIBC_INLINE Atomic(const Atomic &) = delete;
diff --git a/mlir/include/mlir/Analysis/Presburger/Simplex.h b/mlir/include/mlir/Analysis/Presburger/Simplex.h
index 4c40c4cdcb655..d89bbe37a6b3e 100644
--- a/mlir/include/mlir/Analysis/Presburger/Simplex.h
+++ b/mlir/include/mlir/Analysis/Presburger/Simplex.h
@@ -344,7 +344,7 @@ class SimplexBase {
SmallVector<UndoLogEntry, 8> undoLog;
/// Holds a vector of bases. The ith saved basis is the basis that should be
- /// restored when processing the ith occurrance of UndoLogEntry::RestoreBasis
+ /// restored when processing the ith occurrence of UndoLogEntry::RestoreBasis
/// in undoLog. This is used by getSnapshotBasis.
SmallVector<SmallVector<int, 8>, 8> savedBases;
@@ -367,7 +367,7 @@ class SimplexBase {
///
/// This does not directly support negative-valued variables, so it uses the big
/// M parameter trick to make all the variables non-negative. Basically we
-/// introduce an artifical variable M that is considered to have a value of
+/// introduce an artificial variable M that is considered to have a value of
/// +infinity and instead of the variables x, y, z, we internally use variables
/// M + x, M + y, M + z, which are now guaranteed to be non-negative. See the
/// documentation for SimplexBase for more details. M is also considered to be
@@ -561,7 +561,7 @@ struct SymbolicLexOpt {
/// negative for all values in the symbol domain, the row needs to be pivoted
/// irrespective of the precise value of the symbols. To answer queries like
/// "Is this symbolic sample always negative in the symbol domain?", we maintain
-/// a `LexSimplex domainSimplex` correponding to the symbol domain.
+/// a `LexSimplex domainSimplex` corresponding to the symbol domain.
///
/// In other cases, it may be that the symbolic sample is violated at some
/// values in the symbol domain and not violated at others. In this case,
diff --git a/mlir/include/mlir/AsmParser/AsmParser.h b/mlir/include/mlir/AsmParser/AsmParser.h
index 3c1bff1fbc7f1..33daf7ca26f49 100644
--- a/mlir/include/mlir/AsmParser/AsmParser.h
+++ b/mlir/include/mlir/AsmParser/AsmParser.h
@@ -47,7 +47,7 @@ parseAsmSourceFile(const llvm::SourceMgr &sourceMgr, Block *block,
/// not, an error diagnostic is emitted to the context and a null value is
/// returned.
/// If `numRead` is provided, it is set to the number of consumed characters on
-/// succesful parse. Otherwise, parsing fails if the entire string is not
+/// successful parse. Otherwise, parsing fails if the entire string is not
/// consumed.
/// Some internal copying can be skipped if the source string is known to be
/// null terminated.
@@ -58,7 +58,7 @@ Attribute parseAttribute(llvm::StringRef attrStr, MLIRContext *context,
/// This parses a single MLIR type to an MLIR context if it was valid. If not,
/// an error diagnostic is emitted to the context.
/// If `numRead` is provided, it is set to the number of consumed characters on
-/// succesful parse. Otherwise, parsing fails if the entire string is not
+/// successful parse. Otherwise, parsing fails if the entire string is not
/// consumed.
/// Some internal copying can be skipped if the source string is known to be
/// null terminated.
diff --git a/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_loose.mlir b/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_loose.mlir
index f9816584b4655..1af9dc6cf2061 100644
--- a/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_loose.mlir
+++ b/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_loose.mlir
@@ -41,7 +41,7 @@ module {
//
// Note: position for loose_compressed level can vary in the end,
- // therefore we loosly check it with {{.*}}.
+ // therefore we loosely check it with {{.*}}.
//
// CHECK: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 17
diff --git a/mlir/test/Integration/GPU/CUDA/TensorCore/sm80/transform-mma-sync-matmul-f16-f16-accum.mlir b/mlir/test/Integration/GPU/CUDA/TensorCore/sm80/transform-mma-sync-matmul-f16-f16-accum.mlir
index f38b9ddfaa10e..690c4d2636a44 100644
--- a/mlir/test/Integration/GPU/CUDA/TensorCore/sm80/transform-mma-sync-matmul-f16-f16-accum.mlir
+++ b/mlir/test/Integration/GPU/CUDA/TensorCore/sm80/transform-mma-sync-matmul-f16-f16-accum.mlir
@@ -96,21 +96,21 @@ func.func @main() {
%f0 = arith.constant 0.0e+00 : f16
%c32 = arith.constant 32 : index
- // Intialize the lhs matrix with a linspace function.
+ // Initialize the lhs matrix with a linspace function.
scf.for %r = %c0 to %M step %c1 {
scf.for %c = %c0 to %K step %c1 {
%idx = func.call @compute_linspace_val(%r, %c, %K) : (index, index, index) -> f16
memref.store %idx, %lhs[%r, %c] : !lhs_memref_type
}
}
- // Intialize the rhs matrix with a linspace function.
+ // Initialize the rhs matrix with a linspace function.
scf.for %r = %c0 to %K step %c1 {
scf.for %c = %c0 to %N step %c1 {
%idx = func.call @compute_linspace_val(%r, %c, %N) : (index, index, index) -> f16
memref.store %idx, %rhs[%r, %c] : !rhs_memref_type
}
}
- // Intialize the rhs matrix with a linspace function.
+ // Initialize the rhs matrix with a linspace function.
scf.for %r = %c0 to %M step %c1 {
scf.for %c = %c0 to %N step %c1 {
%idx = func.call @compute_linspace_val(%r, %c, %N) : (index, index, index) -> f16
diff --git a/mlir/test/Integration/GPU/CUDA/TensorCore/sm80/transform-mma-sync-matmul-f32.mlir b/mlir/test/Integration/GPU/CUDA/TensorCore/sm80/transform-mma-sync-matmul-f32.mlir
index 1eae22f0b85d0..2eef2ff8f3564 100644
--- a/mlir/test/Integration/GPU/CUDA/TensorCore/sm80/transform-mma-sync-matmul-f32.mlir
+++ b/mlir/test/Integration/GPU/CUDA/TensorCore/sm80/transform-mma-sync-matmul-f32.mlir
@@ -47,21 +47,21 @@ func.func @main() {
%f0 = arith.constant 0.0e+00 : f32
%c32 = arith.constant 32 : index
- // Intialize the lhs matrix with a linspace function.
+ // Initialize the lhs matrix with a linspace function.
scf.for %r = %c0 to %M step %c1 {
scf.for %c = %c0 to %K step %c1 {
%idx = func.call @compute_linspace_val(%r, %c, %K) : (index, index, index) -> f32
memref.store %idx, %lhs[%r, %c] : !lhs_memref_type
}
}
- // Intialize the rhs matrix with a linspace function.
+ // Initialize the rhs matrix with a linspace function.
scf.for %r = %c0 to %K step %c1 {
scf.for %c = %c0 to %N step %c1 {
%idx = func.call @compute_linspace_val(%r, %c, %N) : (index, index, index) -> f32
memref.store %idx, %rhs[%r, %c] : !rhs_memref_type
}
}
- // Intialize the rhs matrix with a linspace function.
+ // Initialize the rhs matrix with a linspace function.
scf.for %r = %c0 to %M step %c1 {
scf.for %c = %c0 to %N step %c1 {
%idx = func.call @compute_linspace_val(%r, %c, %N) : (index, index, index) -> f32
diff --git a/mlir/test/Integration/GPU/CUDA/TensorCore/wmma-matmul-f16.mlir b/mlir/test/Integration/GPU/CUDA/TensorCore/wmma-matmul-f16.mlir
index 01144b3495866..2d91aa8f12b5e 100644
--- a/mlir/test/Integration/GPU/CUDA/TensorCore/wmma-matmul-f16.mlir
+++ b/mlir/test/Integration/GPU/CUDA/TensorCore/wmma-matmul-f16.mlir
@@ -20,7 +20,7 @@ func.func @main() {
%c32 = arith.constant 32 : index
%c1 = arith.constant 1 : index
- // Intialize the Input matrix with the column index in each row.
+ // Initialize the Input matrix with the column index in each row.
scf.for %arg0 = %c0 to %c16 step %c1 {
scf.for %arg1 = %c0 to %c16 step %c1 {
%2 = arith.index_cast %arg1 : index to i16
@@ -28,7 +28,7 @@ func.func @main() {
memref.store %3, %0[%arg0, %arg1] : memref<16x16xf16>
}
}
- // Intialize the accumulator matrix with zeros.
+ // Initialize the accumulator matrix with zeros.
scf.for %arg0 = %c0 to %c16 step %c1 {
scf.for %arg1 = %c0 to %c16 step %c1 {
memref.store %f0, %22[%arg0, %arg1] : memref<16x16xf16>
diff --git a/mlir/test/Integration/GPU/CUDA/TensorCore/wmma-matmul-f32-bare-ptr.mlir b/mlir/test/Integration/GPU/CUDA/TensorCore/wmma-matmul-f32-bare-ptr.mlir
index 3301e68e5d123..18b1c1a1b0b45 100644
--- a/mlir/test/Integration/GPU/CUDA/TensorCore/wmma-matmul-f32-bare-ptr.mlir
+++ b/mlir/test/Integration/GPU/CUDA/TensorCore/wmma-matmul-f32-bare-ptr.mlir
@@ -20,13 +20,13 @@ func.func @main() {
%c32 = arith.constant 32 : index
%c1 = arith.constant 1 : index
- // Intialize the Input matrix with ones.
+ // Initialize the Input matrix with ones.
scf.for %arg0 = %c0 to %c16 step %c1 {
scf.for %arg1 = %c0 to %c16 step %c1 {
memref.store %f1, %h0[%arg0, %arg1] : memref<16x16xf16>
}
}
- // Intialize the accumulator matrix with zeros.
+ // Initialize the accumulator matrix with zeros.
scf.for %arg0 = %c0 to %c16 step %c1 {
scf.for %arg1 = %c0 to %c16 step %c1 {
memref.store %f0, %h_out[%arg0, %arg1] : memref<16x16xf32>
diff --git a/mlir/test/Integration/GPU/CUDA/TensorCore/wmma-matmul-f32.mlir b/mlir/test/Integration/GPU/CUDA/TensorCore/wmma-matmul-f32.mlir
index 4db9aa056e757..78895cda72c5a 100644
--- a/mlir/test/Integration/GPU/CUDA/TensorCore/wmma-matmul-f32.mlir
+++ b/mlir/test/Integration/GPU/CUDA/TensorCore/wmma-matmul-f32.mlir
@@ -18,13 +18,13 @@ func.func @main() {
%c32 = arith.constant 32 : index
%c1 = arith.constant 1 : index
- // Intialize the Input matrix with ones.
+ // Initialize the Input matrix with ones.
scf.for %arg0 = %c0 to %c16 step %c1 {
scf.for %arg1 = %c0 to %c16 step %c1 {
memref.store %f1, %0[%arg0, %arg1] : memref<16x16xf16>
}
}
- // Intialize the accumulator matrix with zeros.
+ // Initialize the accumulator matrix with zeros.
scf.for %arg0 = %c0 to %c16 step %c1 {
scf.for %arg1 = %c0 to %c16 step %c1 {
memref.store %f0, %22[%arg0, %arg1] : memref<16x16xf32>
diff --git a/mlir/test/mlir-tblgen/op-properties-predicates.td b/mlir/test/mlir-tblgen/op-properties-predicates.td
index de59f5166d7e1..9834edd0cbb57 100644
--- a/mlir/test/mlir-tblgen/op-properties-predicates.td
+++ b/mlir/test/mlir-tblgen/op-properties-predicates.td
@@ -36,7 +36,7 @@ def OpWithPredicates : NS_Op<"op_with_predicates"> {
}
// CHECK-LABEL: OpWithPredicates::verifyInvariantsImpl()
-// Note: for test readibility, we capture [[maybe_unused]] into the variable maybe_unused
+// Note: for test readability, we capture [[maybe_unused]] into the variable maybe_unused
// CHECK: [[maybe_unused:\[\[maybe_unused\]\]]] int64_t tblgen_scalar = this->getScalar();
// CHECK: if (!((tblgen_scalar >= 0)))
// CHECK-NEXT: return emitOpError("property 'scalar' failed to satisfy constraint: non-negative int64_t");
|
lntue
approved these changes
Mar 28, 2025
fmayer
approved these changes
Mar 28, 2025
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