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Fix clang-tidy issues in mlir/ (NFC)
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Differential Revision: https://reviews.llvm.org/D115956
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@joker-eph
joker-eph committed Dec 21, 2021
1 parent 6274452 commit bb56c2b
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Showing 4 changed files with 40 additions and 35 deletions.
8 changes: 5 additions & 3 deletions mlir/lib/Bindings/Python/IRAttributes.cpp
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Expand Up @@ -513,11 +513,13 @@ class PyDenseElementsAttribute
if (mlirTypeIsAF64(elementType)) {
// f64
return bufferInfo<double>(shapedType);
} else if (mlirTypeIsAF16(elementType)) {
}
if (mlirTypeIsAF16(elementType)) {
// f16
return bufferInfo<uint16_t>(shapedType, "e");
} else if (mlirTypeIsAInteger(elementType) &&
mlirIntegerTypeGetWidth(elementType) == 32) {
}
if (mlirTypeIsAInteger(elementType) &&
mlirIntegerTypeGetWidth(elementType) == 32) {
if (mlirIntegerTypeIsSignless(elementType) ||
mlirIntegerTypeIsSigned(elementType)) {
// i32
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12 changes: 8 additions & 4 deletions mlir/lib/Dialect/Quant/IR/QuantTypes.cpp
View file
Expand Up @@ -113,7 +113,8 @@ Type QuantizedType::castFromStorageType(Type candidateType) {
if (candidateType.isa<UnrankedTensorType>()) {
// i.e. tensor<i8> -> tensor<!quant<"uniform[i8:f32]{1.0}">>
return UnrankedTensorType::get(getStorageType());
} else if (candidateType.isa<VectorType>()) {
}
if (candidateType.isa<VectorType>()) {
// i.e. tensor<4xi8> -> tensor<4x!quant<"uniform[i8:f32]{1.0}">>
return VectorType::get(candidateType.cast<VectorType>().getShape(),
getStorageType());
Expand All @@ -140,7 +141,8 @@ Type QuantizedType::castToStorageType(Type quantizedType) {
}
if (quantizedType.isa<UnrankedTensorType>()) {
return UnrankedTensorType::get(storageType);
} else if (quantizedType.isa<VectorType>()) {
}
if (quantizedType.isa<VectorType>()) {
return VectorType::get(sType.getShape(), storageType);
}
}
Expand All @@ -166,7 +168,8 @@ Type QuantizedType::castFromExpressedType(Type candidateType) {
if (candidateType.isa<UnrankedTensorType>()) {
// i.e. tensor<xf32> -> tensor<x!quant<"uniform[i8:f32]{1.0}">>
return UnrankedTensorType::get(*this);
} else if (candidateType.isa<VectorType>()) {
}
if (candidateType.isa<VectorType>()) {
// i.e. tensor<4xf32> -> tensor<4x!quant<"uniform[i8:f32]{1.0}">>
return VectorType::get(candidateShapedType.getShape(), *this);
}
Expand All @@ -193,7 +196,8 @@ Type QuantizedType::castToExpressedType(Type quantizedType) {
}
if (quantizedType.isa<UnrankedTensorType>()) {
return UnrankedTensorType::get(expressedType);
} else if (quantizedType.isa<VectorType>()) {
}
if (quantizedType.isa<VectorType>()) {
return VectorType::get(sType.getShape(), expressedType);
}
}
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6 changes: 3 additions & 3 deletions mlir/lib/Dialect/Shape/IR/Shape.cpp
View file
Expand Up @@ -54,13 +54,13 @@ LogicalResult shape::getShapeVec(Value input,
if (auto inputOp = input.getDefiningOp<ConstShapeOp>()) {
shapeValues = llvm::to_vector<6>(inputOp.getShape().getValues<int64_t>());
return success();
} else if (auto inputOp = input.getDefiningOp<arith::ConstantOp>()) {
}
if (auto inputOp = input.getDefiningOp<arith::ConstantOp>()) {
shapeValues = llvm::to_vector<6>(
inputOp.getValue().cast<DenseIntElementsAttr>().getValues<int64_t>());
return success();
} else {
return failure();
}
return failure();
}

static bool isErrorPropagationPossible(TypeRange operandTypes) {
Expand Down
49 changes: 24 additions & 25 deletions mlir/lib/ExecutionEngine/SparseTensorUtils.cpp
View file
Expand Up @@ -550,7 +550,7 @@ static char *toLower(char *token) {

/// Read the MME header of a general sparse matrix of type real.
static void readMMEHeader(FILE *file, char *filename, char *line,
uint64_t *idata, bool *is_symmetric) {
uint64_t *idata, bool *isSymmetric) {
char header[64];
char object[64];
char format[64];
Expand All @@ -562,12 +562,12 @@ static void readMMEHeader(FILE *file, char *filename, char *line,
fprintf(stderr, "Corrupt header in %s\n", filename);
exit(1);
}
*is_symmetric = (strcmp(toLower(symmetry), "symmetric") == 0);
*isSymmetric = (strcmp(toLower(symmetry), "symmetric") == 0);
// Make sure this is a general sparse matrix.
if (strcmp(toLower(header), "%%matrixmarket") ||
strcmp(toLower(object), "matrix") ||
strcmp(toLower(format), "coordinate") || strcmp(toLower(field), "real") ||
(strcmp(toLower(symmetry), "general") && !(*is_symmetric))) {
(strcmp(toLower(symmetry), "general") && !(*isSymmetric))) {
fprintf(stderr,
"Cannot find a general sparse matrix with type real in %s\n",
filename);
Expand Down Expand Up @@ -636,9 +636,9 @@ static SparseTensorCOO<V> *openSparseTensorCOO(char *filename, uint64_t rank,
// Perform some file format dependent set up.
char line[kColWidth];
uint64_t idata[512];
bool is_symmetric = false;
bool isSymmetric = false;
if (strstr(filename, ".mtx")) {
readMMEHeader(file, filename, line, idata, &is_symmetric);
readMMEHeader(file, filename, line, idata, &isSymmetric);
} else if (strstr(filename, ".tns")) {
readExtFROSTTHeader(file, filename, line, idata);
} else {
Expand Down Expand Up @@ -674,7 +674,7 @@ static SparseTensorCOO<V> *openSparseTensorCOO(char *filename, uint64_t rank,
// We currently chose to deal with symmetric matrices by fully constructing
// them. In the future, we may want to make symmetry implicit for storage
// reasons.
if (is_symmetric && indices[0] != indices[1])
if (isSymmetric && indices[0] != indices[1])
tensor->add({indices[1], indices[0]}, value);
}
// Close the file and return tensor.
Expand Down Expand Up @@ -718,17 +718,16 @@ typedef uint64_t index_t;
} \
return SparseTensorStorage<P, I, V>::newSparseTensor(rank, sizes, perm, \
sparsity, tensor); \
} else if (action == Action::kEmptyCOO) { \
} \
if (action == Action::kEmptyCOO) \
return SparseTensorCOO<V>::newSparseTensorCOO(rank, sizes, perm); \
tensor = static_cast<SparseTensorStorage<P, I, V> *>(ptr)->toCOO(perm); \
if (action == Action::kToIterator) { \
tensor->startIterator(); \
} else { \
tensor = static_cast<SparseTensorStorage<P, I, V> *>(ptr)->toCOO(perm); \
if (action == Action::kToIterator) { \
tensor->startIterator(); \
} else { \
assert(action == Action::kToCOO); \
} \
return tensor; \
assert(action == Action::kToCOO); \
} \
return tensor; \
}

#define CASE_SECSAME(p, v, P, V) CASE(p, p, v, P, P, V)
Expand Down Expand Up @@ -1092,15 +1091,15 @@ void *convertToMLIRSparseTensor(uint64_t rank, uint64_t nse, uint64_t *shape,
//
// TODO: for now f64 tensors only, no dim ordering, all dimensions compressed
//
void convertFromMLIRSparseTensor(void *tensor, uint64_t *p_rank,
uint64_t *p_nse, uint64_t **p_shape,
double **p_values, uint64_t **p_indices) {
SparseTensorStorage<uint64_t, uint64_t, double> *sparse_tensor =
void convertFromMLIRSparseTensor(void *tensor, uint64_t *pRank, uint64_t *pNse,
uint64_t **pShape, double **pValues,
uint64_t **pIndices) {
SparseTensorStorage<uint64_t, uint64_t, double> *sparseTensor =
static_cast<SparseTensorStorage<uint64_t, uint64_t, double> *>(tensor);
uint64_t rank = sparse_tensor->getRank();
uint64_t rank = sparseTensor->getRank();
std::vector<uint64_t> perm(rank);
std::iota(perm.begin(), perm.end(), 0);
SparseTensorCOO<double> *coo = sparse_tensor->toCOO(perm.data());
SparseTensorCOO<double> *coo = sparseTensor->toCOO(perm.data());

const std::vector<Element<double>> &elements = coo->getElements();
uint64_t nse = elements.size();
Expand All @@ -1120,11 +1119,11 @@ void convertFromMLIRSparseTensor(void *tensor, uint64_t *p_rank,
}

delete coo;
*p_rank = rank;
*p_nse = nse;
*p_shape = shape;
*p_values = values;
*p_indices = indices;
*pRank = rank;
*pNse = nse;
*pShape = shape;
*pValues = values;
*pIndices = indices;
}
} // extern "C"

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