Add decomposition of `aten.masked.tensor` op.

`aten.masked.tensor` op has been decomposed to `aten.masked.scalar` op.

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

@@ -1928,6 +1928,55 @@ def Torch_AtenMaskedFill_ScalarOp : Torch_Op<"aten.masked_fill_.Scalar", [
   }];
 }
 
+def Torch_AtenMaskedFillTensorOp : Torch_Op<"aten.masked_fill.Tensor", [
+    AllowsTypeRefinement,
+    HasValueSemantics,
+    ReadOnly
+  ]> {
+  let summary = "Generated op for `aten::masked_fill.Tensor : (Tensor, Tensor, Tensor) -> (Tensor)`";
+  let arguments = (ins
+    AnyTorchTensorType:$self,
+    AnyTorchTensorType:$mask,
+    AnyTorchTensorType:$value
+  );
+  let results = (outs
+    AnyTorchTensorType:$result
+  );
+  let hasCustomAssemblyFormat = 1;
+  let extraClassDefinition = [{
+    ParseResult AtenMaskedFillTensorOp::parse(OpAsmParser &parser, OperationState &result) {
+      return parseDefaultTorchOp(parser, result, 3, 1);
+    }
+    void AtenMaskedFillTensorOp::print(OpAsmPrinter &printer) {
+      printDefaultTorchOp(printer, *this, 3, 1);
+    }
+  }];
+}
+
+def Torch_AtenMaskedFill_TensorOp : Torch_Op<"aten.masked_fill_.Tensor", [
+    IsTrailingUnderscoreInplaceVariant,
+    AllowsTypeRefinement
+  ]> {
+  let summary = "Generated op for `aten::masked_fill_.Tensor : (Tensor, Tensor, Tensor) -> (Tensor)`";
+  let arguments = (ins
+    AnyTorchTensorType:$self,
+    AnyTorchTensorType:$mask,
+    AnyTorchTensorType:$value
+  );
+  let results = (outs
+    AnyTorchTensorType:$result
+  );
+  let hasCustomAssemblyFormat = 1;
+  let extraClassDefinition = [{
+    ParseResult AtenMaskedFill_TensorOp::parse(OpAsmParser &parser, OperationState &result) {
+      return parseDefaultTorchOp(parser, result, 3, 1);
+    }
+    void AtenMaskedFill_TensorOp::print(OpAsmPrinter &printer) {
+      printDefaultTorchOp(printer, *this, 3, 1);
+    }
+  }];
+}
+
 def Torch_AtenClampOp : Torch_Op<"aten.clamp", [
     AllowsTypeRefinement,
     HasValueSemantics,

lib/Conversion/TorchToLinalg/Uncategorized.cpp

@@ -884,9 +884,9 @@ static Value createLinalgPayloadCalculationForElementwiseOp(
                                           threshold);
     return b.create<arith::SelectOp>(loc, predicate, constantZero, grad);
   }
-  if (auto maskedFill = dyn_cast<AtenMaskedFillScalarOp>(op)) {
+  if (auto maskedFillScalar = dyn_cast<AtenMaskedFillScalarOp>(op)) {
     AtenMaskedFillScalarOp::Adaptor adaptor(operands);
-    Type dtype = converter->convertType(maskedFill.getType())
+    Type dtype = converter->convertType(maskedFillScalar.getType())
                      .cast<RankedTensorType>()
                      .getElementType();
 
@@ -896,6 +896,17 @@ static Value createLinalgPayloadCalculationForElementwiseOp(
 
     return b.create<arith::SelectOp>(loc, mask, fillValue, input);
   }
+  if (auto maskedFillTensor = dyn_cast<AtenMaskedFillTensorOp>(op)) {
+    AtenMaskedFillScalarOp::Adaptor adaptor(operands);
+    Type dtype = converter->convertType(maskedFillTensor.getType())
+                     .cast<RankedTensorType>()
+                     .getElementType();
+
+    Value input = payloadArgs[0];
+    Value mask = payloadArgs[1];
+    Value fillValue = convertScalarToDtype(b, loc, payloadArgs[2], dtype);
+    return b.create<arith::SelectOp>(loc, mask, fillValue, input);
+  }
 
   if (auto triu = dyn_cast<AtenTriuOp>(op)) {
     // Check if the rank of the input tensor is valid.
@@ -970,7 +981,7 @@ public:
              AtenEqTensorOp, AtenLtTensorOp, AtenSubScalarOp, AtenAddScalarOp,
              AtenThresholdOp, AtenThresholdBackwardOp, AtenCloneOp, AtenSinOp,
              AtenCosOp, AtenNeScalarOp, AtenNegOp, AtenMaskedFillScalarOp,
-             AtenLogicalOrOp, AtenTriuOp>(op))
+             AtenMaskedFillTensorOp, AtenLogicalOrOp, AtenTriuOp>(op))
       return rewriter.notifyMatchFailure(op, "not a supported elementwise op");
 
     if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
@@ -1708,7 +1719,8 @@ void mlir::torch::torch_to_linalg::populateUncategorizedPatternsAndLegality(
       AtenEqScalarOp, AtenLtScalarOp, AtenLeScalarOp, AtenWhereSelfOp,
       AtenGtTensorOp, AtenEqTensorOp, AtenLtTensorOp, AtenThresholdOp,
       AtenThresholdBackwardOp, AtenCloneOp, AtenSinOp, AtenCosOp,
-      AtenNeScalarOp, AtenMaskedFillScalarOp, AtenLogicalOrOp, AtenTriuOp, AtenRemainderScalarOp>();
+      AtenNeScalarOp, AtenMaskedFillScalarOp, AtenMaskedFillTensorOp,
+      AtenLogicalOrOp, AtenTriuOp, AtenRemainderScalarOp>();
   patterns.add<ConvertElementwiseOp>(typeConverter, context);
   target.addIllegalOp<AtenNllLossForwardOp>();
   patterns.add<ConvertAtenDetachOp>(typeConverter, context);

lib/Dialect/Torch/Transforms/RefineTypes.cpp

@@ -658,7 +658,8 @@ void TypeAnalysis::visitOperation(Operation *op,
           AtenZero_Op, AtenIndexTensorOp, ValsemVariantAtenIndexPutImplOp,
           AtenIndexPutOp, ValsemVariantAtenCopyOp, AtenZeroOp,
           AtenIndexPutHackedTwinOp, AtenMaskedFillScalarOp, AtenFlipOp,
-          PrimAbsScalarOp, AtenNumpyTOp, AtenTriuOp>(op)) {
+          PrimAbsScalarOp, AtenNumpyTOp, AtenTriuOp, AtenMaskedFillTensorOp>(
+          op)) {
     return incorporateKnowledge(op->getResult(0), operands[0]->getValue());
   }
 

lib/Dialect/Torch/Transforms/ShapeLibrary.cpp

@@ -6214,6 +6214,10 @@ module {
     %0 = call @__torch__.torch.jit._shape_functions.unary(%arg0) : (!torch.list<int>) -> !torch.list<int>
     return %0 : !torch.list<int>
   }
+  func.func @"__torch_mlir_shape_fn.aten.masked_fill.Tensor"(%arg0: !torch.list<int>, %arg1: !torch.list<int>, %arg2: !torch.list<int>) -> !torch.list<int> {
+    %0 = call @__torch__.torch.jit._shape_functions.unary(%arg0) : (!torch.list<int>) -> !torch.list<int>
+    return %0 : !torch.list<int>
+  }
   func.func @"__torch_mlir_shape_fn.aten.zero"(%arg0: !torch.list<int>) -> !torch.list<int> {
     return %arg0 : !torch.list<int>
   }

python/torch_mlir/dialects/torch/importer/jit_ir/build_tools/shape_lib_gen.py

@@ -777,6 +777,9 @@ def aten〇_to_copy(self: List[int], dtype: Optional[int] = None, layout: Option
 def aten〇masked_fill〇Scalar(self: List[int], mask: List[int], value: float) -> List[int]:
     return upstream_shape_functions.unary(self)
 
+def aten〇masked_fill〇Tensor(self: List[int], mask: List[int], value: List[int]) -> List[int]:
+    return upstream_shape_functions.unary(self)
+
 def aten〇zero(self: List[int]) -> List[int]:
     return self
 

python/torch_mlir/dialects/torch/importer/jit_ir/build_tools/torch_ods_gen.py

@@ -279,6 +279,7 @@ def emit_ops(emitter_td: TextEmitter, registry: Registry):
             "aten::le.Scalar : (Tensor, Scalar) -> (Tensor)",
             "aten::fmod.Scalar : (Tensor, Scalar) -> (Tensor)",
             "aten::masked_fill.Scalar : (Tensor, Tensor, Scalar) -> (Tensor)",
+            "aten::masked_fill.Tensor : (Tensor, Tensor, Tensor) -> (Tensor)",
             "aten::clamp : (Tensor, Scalar?, Scalar?) -> (Tensor)",
             "aten::clamp_min : (Tensor, Scalar) -> (Tensor)",
             "aten::clamp_max : (Tensor, Scalar) -> (Tensor)",

python/torch_mlir_e2e_test/test_suite/constant_alloc.py

@@ -342,7 +342,8 @@ class EmptyLikeMemoryFormatModule(torch.nn.Module):
         ([-1, -1, -1, -1], torch.float32, True),
     ])
     def forward(self, a):
-        return torch.empty_like(a, memory_format=torch.preserve_format).fill_(0)
+        return torch.empty_like(a,
+                                memory_format=torch.preserve_format).fill_(0)
 
 
 @register_test_case(module_factory=lambda: EmptyLikeMemoryFormatModule())
@@ -1421,3 +1422,25 @@ class MaskedFillScalarFloatValueModule(torch.nn.Module):
 def MaskedFillScalarFloatValueModule_basic(module, tu: TestUtils):
     module.forward(torch.randint(-10, 10, (2, 3)),
                    torch.randint(0, 2, (2, 3)).to(dtype=torch.bool))
+
+
+class MaskedFillTensorFloatValueModule(torch.nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1, -1], torch.int64, True),
+        ([-1, -1], torch.bool, True),
+        ([], torch.float32, True),
+    ])
+    def forward(self, x, mask, value):
+        return torch.ops.aten.masked_fill(x, mask, value=value)
+
+
+@register_test_case(module_factory=lambda: MaskedFillTensorFloatValueModule())
+def MaskedFillTensorFloatValueModule_basic(module, tu: TestUtils):
+    module.forward(torch.randint(-10, 10, (2, 3)),
+                   torch.randint(0, 2, (2, 3)).to(dtype=torch.bool), tu.rand())