[AutoBump] Merge with fixes of afca88a0 (May 31) (51)

CMakeLists.txt

@@ -54,13 +54,6 @@ cmake_dependent_option(TORCH_MLIR_ENABLE_LTC "Enables LTC backend" OFF TORCH_MLI
 
 option(TORCH_MLIR_ENABLE_ONNX_C_IMPORTER "Enables the ONNX C importer" OFF)
 
-# TODO(#3299): migrate to from member x.cast<T>() to mlir::cast<T>(x).
-if(MSVC)
-  add_compile_options(/wd4996)
-else()
-  add_compile_options(-Wno-deprecated-declarations)
-endif()
-
 macro(torch_mlir_enable_werror)
   if(TORCH_MLIR_ENABLE_WERROR_FLAG)
     if(NOT MSVC)

include/torch-mlir-dialects/Dialect/TMTensor/IR/TMTensorInterfaces.td

@@ -125,7 +125,7 @@ def TMTensorInterface : OpInterface<"TMTensorOp"> {
         llvm::copy_if(getInputOperands(),
           std::back_inserter(result),
           [](OpOperand *opOperand) {
-            return opOperand->get().getType().template isa<MemRefType>();
+            return isa<MemRefType>(opOperand->get().getType());
           });
         return result;
       }]
@@ -144,7 +144,7 @@ def TMTensorInterface : OpInterface<"TMTensorOp"> {
         llvm::copy_if(getInputOperands(),
           std::back_inserter(result),
           [](OpOperand *opOperand) {
-            return opOperand->get().getType().template isa<RankedTensorType>();
+            return isa<RankedTensorType>(opOperand->get().getType());
           });
         return result;
       }]
@@ -200,7 +200,7 @@ def TMTensorInterface : OpInterface<"TMTensorOp"> {
         llvm::copy_if(getOutputOperands(),
           std::back_inserter(result),
           [](OpOperand *opOperand) {
-            return opOperand->get().getType().template isa<MemRefType>();
+            return isa<MemRefType>(opOperand->get().getType());
           });
         return result;
       }]
@@ -219,7 +219,7 @@ def TMTensorInterface : OpInterface<"TMTensorOp"> {
         llvm::copy_if(getOutputOperands(),
           std::back_inserter(result),
           [](OpOperand *opOperand) {
-            return opOperand->get().getType().template isa<RankedTensorType>();
+            return isa<RankedTensorType>(opOperand->get().getType());
           });
         return result;
       }]
@@ -238,7 +238,7 @@ def TMTensorInterface : OpInterface<"TMTensorOp"> {
         llvm::transform(getOutputBufferOperands(),
           std::back_inserter(result),
           [](OpOperand *opOperands) {
-            return opOperands->get().getType().cast<MemRefType>();
+            return cast<MemRefType>(opOperands->get().getType());
           });
         return result;
       }]
@@ -257,7 +257,7 @@ def TMTensorInterface : OpInterface<"TMTensorOp"> {
         llvm::transform(getOutputTensorOperands(),
           std::back_inserter(result),
           [](OpOperand *opOperands) {
-            return opOperands->get().getType().cast<RankedTensorType>();
+            return cast<RankedTensorType>(opOperands->get().getType());
           });
         return result;
       }]
@@ -318,7 +318,7 @@ def TMTensorInterface : OpInterface<"TMTensorOp"> {
       /*args=*/(ins "OpOperand *":$opOperand),
       /*methodBody=*/"",
       /*defaultImplementation=*/[{
-        if (!opOperand->get().getType().template isa<RankedTensorType>())
+        if (!isa<RankedTensorType>(opOperand->get().getType()))
           return false;
         if (opOperand->getOperandNumber() < $_op.getNumInputs())
           return true;
@@ -334,7 +334,7 @@ def TMTensorInterface : OpInterface<"TMTensorOp"> {
       /*args=*/(ins "OpOperand *":$opOperand),
       /*methodBody=*/"",
       /*defaultImplementation=*/[{
-        if (!opOperand->get().getType().template isa<RankedTensorType>())
+        if (!isa<RankedTensorType>(opOperand->get().getType()))
           return false;
         if (opOperand->getOperandNumber() >= $_op.getNumInputs())
           return true;
@@ -367,7 +367,7 @@ def TMTensorInterface : OpInterface<"TMTensorOp"> {
       /*defaultImplementation=*/[{
         assert(opOperand->getOwner() == this->getOperation());
         if (auto shapedType =
-              opOperand->get().getType().template dyn_cast<ShapedType>())
+              dyn_cast<ShapedType>(opOperand->get().getType()))
           return shapedType.getRank();
         return 0;
       }]
@@ -383,7 +383,7 @@ def TMTensorInterface : OpInterface<"TMTensorOp"> {
       /*defaultImplementation=*/[{
         assert(opOperand->getOwner() == this->getOperation());
         if (auto shapedType =
-              opOperand->get().getType().template dyn_cast<ShapedType>())
+              dyn_cast<ShapedType>(opOperand->get().getType()))
           return shapedType.getShape();
         return {};
       }]
@@ -398,7 +398,7 @@ def TMTensorInterface : OpInterface<"TMTensorOp"> {
       /*methodBody=*/"",
       /*defaultImplementation=*/[{
         assert(opOperand->getOwner() == this->getOperation());
-        return !opOperand->get().getType().template isa<ShapedType>();
+        return !isa<ShapedType>(opOperand->get().getType());
       }]
     >,
     //===------------------------------------------------------------------===//
@@ -416,10 +416,10 @@ def TMTensorInterface : OpInterface<"TMTensorOp"> {
         return this->getOperation()->getNumResults() == 0 &&
           llvm::all_of(getInputOperands(), [&](OpOperand *opOperand) {
             return isScalar(opOperand) ||
-              opOperand->get().getType().template isa<MemRefType>();
+              isa<MemRefType>(opOperand->get().getType());
           }) &&
           llvm::all_of(getOutputOperands(), [](OpOperand *opOperand) {
-            return opOperand->get().getType().template isa<MemRefType>();
+            return isa<MemRefType>(opOperand->get().getType());
           });
       }]
     >,
@@ -435,10 +435,10 @@ def TMTensorInterface : OpInterface<"TMTensorOp"> {
         return
           llvm::all_of(getInputOperands(), [&](OpOperand *opOperand) {
             return isScalar(opOperand) ||
-              opOperand->get().getType().template isa<RankedTensorType>();
+              isa<RankedTensorType>(opOperand->get().getType());
           }) &&
           llvm::all_of(getOutputOperands(), [](OpOperand *opOperand) {
-            return opOperand->get().getType().template isa<RankedTensorType>();
+            return isa<RankedTensorType>(opOperand->get().getType());
           });
       }]
     >,
@@ -478,8 +478,8 @@ def TMTensorInterface : OpInterface<"TMTensorOp"> {
 
     private:
     void setOperandSegmentAt(unsigned idx, unsigned val) {
-      auto attr = (*this)->getAttr("operand_segment_sizes")
-        .cast<DenseIntElementsAttr>();
+      auto attr = cast<DenseIntElementsAttr>((*this)->getAttr("operand_segment_sizes")
+        );
       unsigned i = 0;
       auto newAttr = attr.mapValues(IntegerType::get(getContext(), 32),
         [&](const APInt &v) { return (i++ == idx) ? APInt(32, val) : v; });

include/torch-mlir-dialects/Dialect/TMTensor/IR/TMTensorOps.td

@@ -88,7 +88,7 @@ def TMTensor_ScanOp : TMTensor_Op<"scan",
       return getOutputOperand(0)->get();
     }
     ShapedType getOperandType() {
-      return input().getType().cast<ShapedType>();
+      return cast<ShapedType>(input().getType());
     }
     int64_t getOperandRank() {
       return getOperandType().getRank();
@@ -151,10 +151,10 @@ def TMTensor_ScatterOp : TMTensor_Op<"scatter",
   let extraClassDeclaration = extraTMTensorOpClassDeclaration # [{
 
     int64_t getIndexDepth() {
-      return getInputOperand(1)
+      return cast<ShapedType>(getInputOperand(1)
           ->get()
           .getType()
-          .cast<ShapedType>()
+          )
           .getShape()
           .back();
     }
@@ -164,27 +164,27 @@ def TMTensor_ScatterOp : TMTensor_Op<"scatter",
     }
 
     ShapedType getUpdateType() {
-      return updates().getType().cast<ShapedType>();
+      return cast<ShapedType>(updates().getType());
     }
 
     Value indices() {
       return getInputOperand(1)->get();
     }
 
     ShapedType getIndicesType() {
-      return indices().getType().cast<ShapedType>();
+      return cast<ShapedType>(indices().getType());
     }
 
     Value original() {
       return getOutputOperand(0)->get();
     }
 
     ShapedType getOriginalType() {
-      return original().getType().cast<ShapedType>();
+      return cast<ShapedType>(original().getType());
     }
 
     int64_t getUpdateSliceRank() {
-      return updates().getType().cast<ShapedType>().getRank() - 1;
+      return cast<ShapedType>(updates().getType()).getRank() - 1;
     }
 
     bool isScalarUpdate() {
@@ -224,7 +224,7 @@ def TMTensor_SortOp : TMTensor_Op<"sort",
       return getOutputs()[index];
     }
     ShapedType getOperandType(int index) {
-      return operand(index).getType().cast<ShapedType>();
+      return cast<ShapedType>(operand(index).getType());
     }
     int64_t getOperandRank() {
       return getOperandType(0).getRank();
@@ -291,16 +291,16 @@ def TMTensor_AttentionOp : TMTensor_Op<"attention",
       return getOutputOperand(0)->get();
     }
     ShapedType getQueryType() {
-      return getQuery().getType().cast<ShapedType>();
+      return cast<ShapedType>(getQuery().getType());
     }
     ShapedType getKeyType() {
-      return getKey().getType().cast<ShapedType>();
+      return cast<ShapedType>(getKey().getType());
     }
     ShapedType getValueType() {
-      return getValue().getType().cast<ShapedType>();
+      return cast<ShapedType>(getValue().getType());
     }
     ShapedType getOutputType() {
-      return getOutput().getType().cast<ShapedType>();
+      return cast<ShapedType>(getOutput().getType());
     }
     int64_t getQueryRank() {
       return getQueryType().getRank();

include/torch-mlir/Conversion/TorchOnnxToTorch/Utils.h

@@ -61,12 +61,12 @@ struct onnx_list_of_constant_ints_op_binder {
 
   bool match(Operation *op) {
     auto constOp = dyn_cast<Torch::OperatorOp>(op);
-    if (!constOp || !constOp.getName().equals("onnx.Constant"))
+    if (!constOp || !(constOp.getName() == "onnx.Constant"))
       return false;
 
     if (DenseResourceElementsAttr attr =
-            constOp->getAttr("torch.onnx.value")
-                .dyn_cast_or_null<DenseResourceElementsAttr>()) {
+            dyn_cast_or_null<DenseResourceElementsAttr>(
+                constOp->getAttr("torch.onnx.value"))) {
       // Bytes are stored in little endian order. Big endian support will
       // require swizzling.
       if (!Endian::little) {

include/torch-mlir/Dialect/Torch/IR/TorchOps.h

@@ -190,7 +190,7 @@ struct torch_list_of_optional_constant_ints_op_binder {
       int64_t num;
       if (matchPattern(value, m_TorchConstantInt(&num)))
         bind_values.push_back(num);
-      else if (value.getType().isa<Torch::NoneType>())
+      else if (isa<Torch::NoneType>(value.getType()))
         bind_values.push_back(std::nullopt);
       else
         return false;

include/torch-mlir/Dialect/Torch/IR/TorchOps.td

@@ -442,8 +442,8 @@ def Torch_PrimDictConstructOp: Torch_Op<"prim.DictConstruct", [
   }];
 
   let extraClassDeclaration = [{
-    Type getKeyType() { return getType().cast<DictType>().getKeyType(); }
-    Type getValueType() { return getType().cast<DictType>().getValueType(); }
+    Type getKeyType() { return cast<DictType>(getType()).getKeyType(); }
+    Type getValueType() { return cast<DictType>(getType()).getValueType(); }
   }];
 }
 
@@ -1003,7 +1003,7 @@ def Torch_CopyToNonValueTensorOp : Torch_Op<"copy.to_tensor", [
     DeclareOpInterfaceMethods<InferTypeOpInterface>,
     TypesMatchWith<"operand is corresponding !torch.vtensor",
                    "result", "operand",
-                   "$_self.cast<NonValueTensorType>().getWithValueSemantics()">,
+                   "cast<NonValueTensorType>($_self).getWithValueSemantics()">,
   ]> {
   let summary = "Create a !torch.tensor with the same contents as the operand";
   let description = [{
@@ -1036,7 +1036,7 @@ def Torch_CopyToValueTensorOp : Torch_Op<"copy.to_vtensor", [
     DeclareOpInterfaceMethods<InferTypeOpInterface>,
     TypesMatchWith<"operand is corresponding !torch.tensor",
                    "result", "operand",
-                   "$_self.cast<ValueTensorType>().getWithoutValueSemantics()">,
+                   "cast<ValueTensorType>($_self).getWithoutValueSemantics()">,
   ]> {
   let summary = "Create a !torch.vtensor with the same contents as the operand";
   let description = [{
@@ -1064,7 +1064,7 @@ def Torch_CopyToValueTensorOp : Torch_Op<"copy.to_vtensor", [
 def Torch_OverwriteTensorContentsOp : Torch_Op<"overwrite.tensor.contents", [
     TypesMatchWith<"overwritten tensor type is corresponding !torch.tensor of value tensor type",
                    "value", "overwritten",
-                   "$_self.cast<ValueTensorType>().getWithoutValueSemantics()">
+                   "cast<ValueTensorType>($_self).getWithoutValueSemantics()">
   ]> {
   let summary = "Ovewrite the contents of tensor with values from another.";
   let description = [{

include/torch-mlir/Dialect/Torch/IR/TorchTypes.td

@@ -199,7 +199,7 @@ def Torch_ValueTensorType : AnyTorchTensorType<"ValueTensor", "vtensor"> {
 }
 
 def AnyTorchTensorType : Type<
-    CPred<"$_self.isa<::mlir::torch::Torch::BaseTensorType>()">,
+    CPred<"isa<::mlir::torch::Torch::BaseTensorType>($_self)">,
     "Any Torch tensor type"
 >;
 
@@ -410,11 +410,11 @@ def AnyTorchOptionalDeviceType:
 def AnyTorchOptionalGeneratorType:
       OptionalOf<Torch_GeneratorType, "Optional torch Generator type">;
 
-def IsListTypePred : CPred<"$_self.isa<::mlir::torch::Torch::ListType>()">;
+def IsListTypePred : CPred<"isa<::mlir::torch::Torch::ListType>($_self)">;
 class ListOf<list<Type> allowedTypes, string descr> :
     ContainerType<AnyTypeOf<allowedTypes>,
             IsListTypePred,
-            "$_self.cast<::mlir::torch::Torch::ListType>().getContainedType()",
+            "cast<::mlir::torch::Torch::ListType>($_self).getContainedType()",
             descr, "::mlir::torch::Torch::ListType">;
 
 def AnyTorchListOfTorchBoolType : ListOf<[Torch_BoolType], "Bool list type (bool[])">;