colin
/
onnx-mlir
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Fix a bug in createArrayAttribute (#43) 

* Fix a bug in createArrayAttribute

* Use size_t

* Use const auto&
This commit is contained in:
Tung D. Le 2020-03-24 15:04:23 +09:00 committed by GitHub
parent b3719d486b
commit ddff0f1256
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 19 additions and 24 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

43
src/Builder/FrontendDialectHelper.cpp
View File

@ -96,20 +96,22 @@ struct TransformValueToONNXData<int64_t> {
// Helper method for constructing an array attribute from a model input.
template <typename T>
static T* CreateArrayAttribute(onnx::TensorProto initializer, int *size) {
static std::vector<T> CreateArrayAttribute(onnx::TensorProto initializer) {
size_t size;
if (initializer.raw_data().size()) {
// copy & take care of endianness
std::vector<char> byteInitializer;
std::copy(initializer.raw_data().begin(), initializer.raw_data().end(),
back_inserter(byteInitializer));
*size = initializer.raw_data().size() / sizeof(T);
return reinterpret_cast<T*>(&byteInitializer[0]);
size = initializer.raw_data().size() / sizeof(T);
T *res = reinterpret_cast<T *>(&byteInitializer[0]);
return std::vector<T>(res, res + size);
}
// copy, no need to take care of endianness
auto data = TransformValueToONNXData<T>::data(initializer);
*size = data.size();
return &data[0];
size = data.size();
return std::vector<T>(&data[0], &data[0] + size);
}
void InitializedTensorMapping::AddMapping(
@ -139,39 +141,32 @@ mlir::Value InitializedTensorMapping::EmitInitializerForInputTensor(
mlir::DenseElementsAttr constantDenseAttribute;
mlir::Type elementType;
mlir::ShapedType tensorType;
int length;
switch (initializer.data_type()) {
case (onnx::TensorProto::FLOAT): {
float *typeArray =
CreateArrayAttribute<float>(initializer, &length);
std::vector<float> arrayAttrInitializer(
typeArray, typeArray + length);
llvm::ArrayRef<float> array(typeArray, length);
const auto& arrayAttrInitializer =
CreateArrayAttribute<float>(initializer);
elementType = builder.getF32Type();
tensorType = mlir::RankedTensorType::get(tensorDims, elementType);
constantDenseAttribute = mlir::DenseElementsAttr::get(tensorType, array);
constantDenseAttribute = mlir::DenseElementsAttr::get(
tensorType, llvm::makeArrayRef(arrayAttrInitializer));
break;
}
case (onnx::TensorProto::INT32): {
int32_t *typeArray =
CreateArrayAttribute<int32_t>(initializer, &length);
std::vector<int32_t> arrayAttrInitializer(
typeArray, typeArray + length);
llvm::ArrayRef<int32_t> array(typeArray, length);
const auto& arrayAttrInitializer =
CreateArrayAttribute<int32_t>(initializer);
elementType = builder.getIntegerType(32);
tensorType = mlir::RankedTensorType::get(tensorDims, elementType);
constantDenseAttribute = mlir::DenseElementsAttr::get(tensorType, array);
constantDenseAttribute = mlir::DenseElementsAttr::get(
tensorType, llvm::makeArrayRef(arrayAttrInitializer));
break;
}
case (onnx::TensorProto::INT64): {
int64_t *typeArray =
CreateArrayAttribute<int64_t>(initializer, &length);
std::vector<int64_t> arrayAttrInitializer(
typeArray, typeArray + length);
llvm::ArrayRef<int64_t> array(typeArray, length);
const auto& arrayAttrInitializer =
CreateArrayAttribute<int64_t>(initializer);
elementType = builder.getIntegerType(64);
tensorType = mlir::RankedTensorType::get(tensorDims, elementType);
constantDenseAttribute = mlir::DenseElementsAttr::get(tensorType, array);
constantDenseAttribute = mlir::DenseElementsAttr::get(
tensorType, llvm::makeArrayRef(arrayAttrInitializer));
break;
}
}