mirror of https://github.com/llvm/torch-mlir
Clean up some compiler warnings on my machine.
parent
99178a167d
commit
641098be54
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@ -76,15 +76,15 @@ static refbackrt::MutableArrayRef<T> toRefbackrt(llvm::MutableArrayRef<T> a) {
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}
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static std::string stringifyShape(refbackrt::ArrayRef<std::int32_t> extents) {
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static constexpr char *kDynamicDimAsString = "?";
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static constexpr char kDynamicDimAsString[] = "?";
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std::stringstream ss;
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ss << "(";
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for (int i = 0; i < extents.size(); i++) {
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for (int i = 0, e = extents.size(); i < e; i++) {
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if (extents[i] < 0)
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ss << kDynamicDimAsString;
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else
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ss << extents[i];
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if (i != extents.size() - 1)
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if (i != e - 1)
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ss << "x";
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}
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ss << ")";
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@ -135,8 +135,8 @@ JITModule::invoke(llvm::StringRef functionName,
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// Tag::kNone) currently without passing the ArgInfo structs down to the
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// Runtime level, so we deal with the output type creation here.
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for (int i = 0; i < metadata.numOutputs; i++) {
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outputs[i] = std::move(
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refbackrt::createRtValueFromOutputArgInfo(metadata.outputArgInfos[i]));
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outputs[i] =
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refbackrt::createRtValueFromOutputArgInfo(metadata.outputArgInfos[i]);
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}
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refbackrt::invoke(
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@ -307,7 +307,7 @@ createFuncDescriptorArray(ArrayRef<refbackrt::FuncMetadataOp> funcMetadatas,
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Value inputDescriptorArray =
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builder.create<LLVM::UndefOp>(loc, inputDescriptorArrayTy);
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for (int i = 0; i < funcMetadata.numInputs(); i++) {
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for (int i = 0, e = funcMetadata.numInputs(); i < e; i++) {
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// Arg Type
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if (!funcMetadata.inputArgTypes().hasValue())
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funcMetadata.emitError()
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@ -365,7 +365,7 @@ createFuncDescriptorArray(ArrayRef<refbackrt::FuncMetadataOp> funcMetadatas,
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Value outputDescriptorArray =
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builder.create<LLVM::UndefOp>(loc, outputDescriptorArrayTy);
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for (int i = 0; i < funcMetadata.numOutputs(); i++) {
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for (int i = 0, e = funcMetadata.numOutputs(); i < e; i++) {
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if (!funcMetadata.outputArgTypes().hasValue())
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funcMetadata.emitError()
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<< "numOutputs > 0 but there are no outputArgTypes?";
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@ -599,11 +599,6 @@ static Type getUnrankedMemrefDescriptorType(MLIRContext *context) {
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/*memorySpace=*/0));
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}
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static Type getDoubleType(MLIRContext *context) {
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LLVMTypeConverter converter(context);
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return converter.convertType(FloatType::getF64(context));
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}
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static Type getFloatType(MLIRContext *context) {
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LLVMTypeConverter converter(context);
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return converter.convertType(FloatType::getF32(context));
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@ -358,8 +358,6 @@ getExternalInputArgInfo(const refbackrt::InputDescriptor &inputDescriptor) {
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ret.argType = ArgType::kF64;
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ret.elementType = ElementType::NONE;
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break;
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default:
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assert(false && "need to update external internal map");
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}
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// Extract shape information
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@ -393,8 +391,6 @@ getExternalOutputArgInfo(const refbackrt::OutputDescriptor &outputDescriptor) {
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ret.argType = ArgType::kF64;
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ret.elementType = ElementType::NONE;
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break;
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default:
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assert(false && "need to update external internal map");
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}
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// Extract shape information
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@ -516,4 +512,4 @@ RtValue refbackrt::createRtValueFromOutputArgInfo(const OutputArgInfo &info) {
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return RtValue();
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}
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}
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}
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}
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@ -96,8 +96,9 @@ static Type convertToMLIRType(refbackrt::ElementType type, Builder &builder) {
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switch (type) {
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case refbackrt::ElementType::F32:
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return builder.getF32Type();
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default:
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llvm_unreachable("unsupported dtype");
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}
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llvm_unreachable("unsupported dtype");
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}
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static RankedTensorType getCorrespondingMLIRTensorType(refbackrt::Tensor &tensor,
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@ -122,13 +123,13 @@ static Attribute convertToMLIRAttribute(const refbackrt::RtValue &value,
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values.push_back(basePtr[i]);
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return DenseFPElementsAttr::get(type, values);
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}
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default:
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llvm_unreachable("unsupported element type");
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}
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} else if (value.isFloat()) {
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return builder.getF32FloatAttr(value.toFloat());
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} else {
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assert(false && "could not convert value to mlir attribute");
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}
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llvm_unreachable("unsupported dtype");
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llvm_unreachable("unsupported type");
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}
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static void printOutput(const refbackrt::RtValue &value, llvm::raw_ostream &os) {
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