Make getTypeForScalarType safer by returning FailureOr<Type> (llv…

…m#1814) One of the potential values for a `torch_upstream::ScalarType` is `Undefined`. This means that conversion of a `ScalarType` to another type is a computation that can fail. To enforce handling of the failure case, this commit makes the two helper functions that convert `ScalarType`s into other types return `failure()` when the `ScalarType` is `Undefined`.
web-logs2 · Jan 20, 2023 · d849cba · d849cba
1 parent d3c6183
commit d849cba
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Showing 6 changed files with 40 additions and 14 deletions.
diff --git a/include/torch-mlir/Dialect/Torch/Utils/Utils.h b/include/torch-mlir/Dialect/Torch/Utils/Utils.h
@@ -26,7 +26,7 @@ bool getListConstructElements(Value v, SmallVectorImpl<Value> &elems);
 std::optional<int64_t> matchLegalConstantIndexIntoListOfSize(Value v,
                                                              int64_t length);
 torch_upstream::ScalarType getScalarTypeForType(Type type);
-Type getTypeForScalarType(
+FailureOr<Type> getTypeForScalarType(
     MLIRContext *context, torch_upstream::ScalarType dtypeInt,
     mlir::IntegerType::SignednessSemantics signedness = IntegerType::Signed);
 

diff --git a/lib/Conversion/TorchToLinalg/TensorConstructors.cpp b/lib/Conversion/TorchToLinalg/TensorConstructors.cpp
@@ -127,9 +127,14 @@ class ConvertConstantTensorAllocOp : public OpConversionPattern<OpTy> {
       if (!matchPattern(op.getDtype(), m_TorchConstantInt(&dtypeInt)))
         return rewriter.notifyMatchFailure(
             op, "unimplemented: dtype must be a constant integer or none");
-      resultElementType = getTypeForScalarType(
+      FailureOr<Type> maybeResultElementType = getTypeForScalarType(
           op->getContext(), (torch_upstream::ScalarType)dtypeInt,
           IntegerType::Signless);
+      if (failed(maybeResultElementType)) {
+        return rewriter.notifyMatchFailure(
+            op, "unable to convert `dtypeInt` to builtin type");
+      }
+      resultElementType = *maybeResultElementType;
     }
 
     // Create an uninitialized tensor of `resultSize` shape and fill it with
@@ -227,9 +232,14 @@ class ConvertAtenEmptyMemoryFormatOp
       if (!matchPattern(op.getDtype(), m_TorchConstantInt(&dtypeInt)))
         return rewriter.notifyMatchFailure(
             op, "unimplemented: dtype must be a constant integer or none");
-      resultElementType = getTypeForScalarType(
+      FailureOr<Type> maybeResultElementType = getTypeForScalarType(
           op->getContext(), (torch_upstream::ScalarType)dtypeInt,
           IntegerType::Signless);
+      if (failed(maybeResultElementType)) {
+        return rewriter.notifyMatchFailure(
+            op, "unable to convert `dtypeInt` to builtin type");
+      }
+      resultElementType = *maybeResultElementType;
     }
 
     // Create an uninitialized tensor of `resultSize` shape.

diff --git a/lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp b/lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp
@@ -33,9 +33,11 @@ static bool isNoneOrFloatDtype(MLIRContext *context, Value dtype) {
   int64_t dtypeInt;
   if (!matchPattern(dtype, m_TorchConstantInt(&dtypeInt)))
     return false;
-  Type resDtype =
+  FailureOr<Type> resDtype =
       getTypeForScalarType(context, (torch_upstream::ScalarType)dtypeInt);
-  return resDtype.isa<mlir::FloatType>();
+  if (failed(resDtype))
+    return false;
+  return resDtype->isa<mlir::FloatType>();
 }
 
 // Helper function to compute the return type of the reduction function.
@@ -3803,4 +3805,4 @@ std::unique_ptr<OperationPass<func::FuncOp>>
 mlir::torch::Torch::createDecomposeComplexOpsPass(
     ArrayRef<std::string> legalOps) {
   return std::make_unique<DecomposeComplexOpsPass>(legalOps);
-}
+}
diff --git a/lib/Dialect/Torch/Transforms/RefineTypes.cpp b/lib/Dialect/Torch/Transforms/RefineTypes.cpp
@@ -81,7 +81,9 @@ using namespace mlir::torch::Torch;
 // -----------------------------------------------------------------------------
 
 static Type getTypeForDTypeInteger(MLIRContext *context, int64_t dtypeInt) {
-  return getTypeForScalarType(context, (torch_upstream::ScalarType)dtypeInt);
+  FailureOr<Type> result =
+      getTypeForScalarType(context, (torch_upstream::ScalarType)dtypeInt);
+  return failed(result) ? Type() : *result;
 }
 
 static Type getDtypeOrDefault(MLIRContext *context, Value optionalDtype,
@@ -563,7 +565,9 @@ static Type getPromotedResultDType(ValueKnowledge *tensor, Type scalarType) {
                                 /*skipRankCheck=*/true);
   state =
       updateResultTypeState(getDefaultDtypeForTorchScalar(scalarType), state);
-  return getTypeForScalarType(scalarType.getContext(), result_type(state));
+  FailureOr<Type> result =
+      getTypeForScalarType(scalarType.getContext(), result_type(state));
+  return failed(result) ? Type() : *result;
 }
 
 static SmallVector<std::optional<bool>>
@@ -600,7 +604,8 @@ static Type getPromotedResultType(MLIRContext *context,
       return Type();
     state = updateResultTypeState(tensor, rankIsNonZero, state, skipRankCheck);
   }
-  return getTypeForScalarType(context, result_type(state));
+  FailureOr<Type> result = getTypeForScalarType(context, result_type(state));
+  return failed(result) ? Type() : *result;
 }
 
 static Type getPromotedResultTypeAssumingNonZeroRank(

diff --git a/lib/Dialect/Torch/Transforms/SimplifyDtypeCalculations.cpp b/lib/Dialect/Torch/Transforms/SimplifyDtypeCalculations.cpp
@@ -46,10 +46,15 @@ static LogicalResult refineDtypeCalculateResult(DtypeCalculateOp op,
     impliedTypeFromDtype = *torchType;
   } else if (auto originalResultType =
                  result.getType().dyn_cast<BaseTensorType>()) {
+    FailureOr<Type> builtinType =
+        getTypeForScalarType(op->getContext(), dtypeScalarType);
+    if (failed(builtinType)) {
+      return rewriter.notifyMatchFailure(
+          op, "Failed to convert `dtypeScalarType` to a builtin type");
+    }
     impliedTypeFromDtype =
         originalResultType.cast<BaseTensorType>().getWithSizesAndDtype(
-            originalResultType.getOptionalSizes(),
-            getTypeForScalarType(op->getContext(), dtypeScalarType));
+            originalResultType.getOptionalSizes(), *builtinType);
   } else {
     return rewriter.notifyMatchFailure(op,
                                        "Unimplemented: Expected result type to "

diff --git a/lib/Dialect/Torch/Utils/Utils.cpp b/lib/Dialect/Torch/Utils/Utils.cpp
@@ -83,9 +83,10 @@ Type Torch::getTypeForTorchType(
   llvm::report_fatal_error("unhandled type for getTypeForTorchType");
 }
 
-Type Torch::getTypeForScalarType(
-    MLIRContext *context, torch_upstream::ScalarType dtypeInt,
-    mlir::IntegerType::SignednessSemantics signedness) {
+FailureOr<Type>
+Torch::getTypeForScalarType(MLIRContext *context,
+                            torch_upstream::ScalarType dtypeInt,
+                            mlir::IntegerType::SignednessSemantics signedness) {
   switch (dtypeInt) {
   case torch_upstream::ScalarType::Float:
     return Float32Type::get(context);
@@ -110,6 +111,8 @@ Type Torch::getTypeForScalarType(
     return mlir::ComplexType::get(Float64Type::get(context));
   case torch_upstream::ScalarType::ComplexDouble:
     return mlir::ComplexType::get(Float128Type::get(context));
+  case torch_upstream::ScalarType::Undefined:
+    return failure();
   default:
     llvm::report_fatal_error("unhandled type for getTypeForScalarType");
   }
@@ -123,6 +126,7 @@ Torch::getTorchTypeForScalarType(MLIRContext *context,
     return Torch::FloatType::get(context);
   case torch_upstream::ScalarType::Long:
     return Torch::IntType::get(context);
+  case torch_upstream::ScalarType::Undefined:
   default:
     return failure();
   }