[LINALG] Add lowering for aten::round op.

-- Added the lowering for aten::round op.
-- Added the folding for integer cases.

e2e_testing/xfail_sets.py

@@ -279,6 +279,7 @@ MHLO_PASS_SET = {
     "Permute0RankModule_basic",
     "UnsafeViewCollapseModule_basic",
     "UnsafeViewDynamicExpandModule_basic",
+    "AtenRoundIntModule_basic",
 }
 
 # Write the TOSA set as a "passing" set as it is very early in development
@@ -470,6 +471,7 @@ TOSA_PASS_SET = {
     "ToDtypeBoolLayoutNoneStaticModule_basic",
     "ToCopyBoolDTypeStaticModule_basic",
     "HardTanhIntModule_basic",
+    "AtenRoundIntModule_basic"
 }
 
 LTC_XFAIL_SET = {

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

@@ -3255,6 +3255,52 @@ def Torch_AtenTriu_Op : Torch_Op<"aten.triu_", [
   }];
 }
 
+def Torch_AtenRoundOp : Torch_Op<"aten.round", [
+    AllowsTypeRefinement,
+    HasValueSemantics,
+    ReadOnly
+  ]> {
+  let summary = "Generated op for `aten::round : (Tensor) -> (Tensor)`";
+  let arguments = (ins
+    AnyTorchTensorType:$self
+  );
+  let results = (outs
+    AnyTorchTensorType:$result
+  );
+  let hasCustomAssemblyFormat = 1;
+  let extraClassDefinition = [{
+    ParseResult AtenRoundOp::parse(OpAsmParser &parser, OperationState &result) {
+      return parseDefaultTorchOp(parser, result, 1, 1);
+    }
+    void AtenRoundOp::print(OpAsmPrinter &printer) {
+      printDefaultTorchOp(printer, *this, 1, 1);
+    }
+  }];
+  let hasFolder = 1;
+}
+
+def Torch_AtenRound_Op : Torch_Op<"aten.round_", [
+    IsTrailingUnderscoreInplaceVariant,
+    AllowsTypeRefinement
+  ]> {
+  let summary = "Generated op for `aten::round_ : (Tensor) -> (Tensor)`";
+  let arguments = (ins
+    AnyTorchTensorType:$self
+  );
+  let results = (outs
+    AnyTorchTensorType:$result
+  );
+  let hasCustomAssemblyFormat = 1;
+  let extraClassDefinition = [{
+    ParseResult AtenRound_Op::parse(OpAsmParser &parser, OperationState &result) {
+      return parseDefaultTorchOp(parser, result, 1, 1);
+    }
+    void AtenRound_Op::print(OpAsmPrinter &printer) {
+      printDefaultTorchOp(printer, *this, 1, 1);
+    }
+  }];
+}
+
 def Torch_AtenIndexPutOp : Torch_Op<"aten.index_put", [
     AllowsTypeRefinement,
     HasValueSemantics,

lib/Conversion/TorchToLinalg/Uncategorized.cpp

@@ -252,6 +252,16 @@ static Value createLinalgPayloadCalculationForElementwiseOp(
                                          payloadArgs[0], constZero);
     return b.create<arith::SelectOp>(loc, pred, payloadArgs[0], constZero);
   }
+  if (auto round = dyn_cast<AtenRoundOp>(op)) {
+    if (!round.getType()
+             .cast<ValueTensorType>()
+             .getDtype()
+             .isa<mlir::FloatType>()) {
+      round.emitError("unimplemented: non-floating point dtype");
+      return nullptr;
+    }
+    return b.create<math::RoundOp>(loc, payloadArgs[0]);
+  }
   if (auto lrelu = dyn_cast<AtenLeakyReluOp>(op)) {
     if (!lrelu.getType()
              .cast<ValueTensorType>()
@@ -1029,7 +1039,7 @@ public:
              AtenThresholdOp, AtenThresholdBackwardOp, AtenCloneOp, AtenSinOp,
              AtenCosOp, AtenNeScalarOp, AtenNegOp, AtenMaskedFillScalarOp,
              AtenMaskedFillTensorOp, AtenLogicalOrOp, AtenTriuOp,
-             AtenBitwiseNotOp>(op))
+             AtenBitwiseNotOp, AtenRoundOp>(op))
       return rewriter.notifyMatchFailure(op, "not a supported elementwise op");
 
     if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
@@ -1504,7 +1514,7 @@ void mlir::torch::torch_to_linalg::populateUncategorizedPatternsAndLegality(
       AtenEqTensorOp, AtenLtTensorOp, AtenThresholdOp, AtenThresholdBackwardOp,
       AtenCloneOp, AtenSinOp, AtenCosOp, AtenNeScalarOp, AtenMaskedFillScalarOp,
       AtenMaskedFillTensorOp, AtenLogicalOrOp, AtenTriuOp,
-      AtenRemainderScalarOp, AtenBitwiseNotOp>();
+      AtenRemainderScalarOp, AtenBitwiseNotOp, AtenRoundOp>();
   patterns.add<ConvertElementwiseOp>(typeConverter, context);
   target.addIllegalOp<AtenNllLossForwardOp>();
   patterns.add<ConvertAtenDetachOp>(typeConverter, context);

lib/Dialect/Torch/IR/TorchOps.cpp

@@ -673,6 +673,18 @@ OpFoldResult AtenSqueezeDimOp::fold(ArrayRef<Attribute> operands) {
   return nullptr;
 }
 
+//===----------------------------------------------------------------------===//
+// AtenRoundOp
+//===----------------------------------------------------------------------===//
+
+OpFoldResult AtenRoundOp::fold(ArrayRef<Attribute> operands) {
+  if (auto selfType = self().getType().dyn_cast<BaseTensorType>()) {
+    if (selfType.hasDtype() && selfType.getDtype().isa<mlir::IntegerType>())
+      return self();
+  }
+  return nullptr;
+}
+
 //===----------------------------------------------------------------------===//
 // AtenTypeAsOp
 //===----------------------------------------------------------------------===//

lib/Dialect/Torch/Transforms/RefineTypes.cpp

@@ -701,8 +701,8 @@ void TypeAnalysis::visitOperation(Operation *op,
           AtenIndexPutHackedTwinOp, AtenMaskedFillScalarOp, AtenFlipOp,
           PrimAbsScalarOp, AtenNumpyTOp, AtenTriuOp, AtenMaskedFillTensorOp,
           AtenRollOp, AtenPowTensorTensorOp, AtenLiftFreshCopyOp,
-          AtenIndexTensorHackedTwinOp, AtenUpsampleNearest2dVecOp, AtenMishOp>(
-          op)) {
+          AtenIndexTensorHackedTwinOp, AtenUpsampleNearest2dVecOp, AtenMishOp,
+          AtenRoundOp>(op)) {
     return incorporateKnowledge(op->getResult(0), operands[0]->getValue());
   }
 

lib/Dialect/Torch/Transforms/ShapeLibrary.cpp

@@ -5582,6 +5582,10 @@ StringRef mlir::torch::Torch::getShapeLibrary() {
 "    %0 = call @__torch__.torch.jit._shape_functions.unary(%arg0) : (!torch.list<int>) -> !torch.list<int>\n"
 "    return %0 : !torch.list<int>\n"
 "  }\n"
+"  func.func @\"__torch_mlir_shape_fn.aten.round\"(%arg0: !torch.list<int>) -> !torch.list<int> {\n"
+"    %0 = call @__torch__.torch.jit._shape_functions.unary(%arg0) : (!torch.list<int>) -> !torch.list<int>\n"
+"    return %0 : !torch.list<int>\n"
+"  }\n"
 "  func.func @\"__torch_mlir_shape_fn.aten._softmax\"(%arg0: !torch.list<int>, %arg1: !torch.int, %arg2: !torch.bool) -> !torch.list<int> {\n"
 "    %0 = call @__torch__.torch.jit._shape_functions.unary(%arg0) : (!torch.list<int>) -> !torch.list<int>\n"
 "    return %0 : !torch.list<int>\n"

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

@@ -403,6 +403,9 @@ def aten〇relu(self: List[int]) -> List[int]:
 def aten〇relu6(self: List[int]) -> List[int]:
     return upstream_shape_functions.unary(self)
 
+def aten〇round(self: List[int]) -> List[int]:
+    return upstream_shape_functions.unary(self)
+
 def aten〇_softmax(self: List[int], dim: int, half_to_float: bool) -> List[int]:
     return upstream_shape_functions.unary(self)
 

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

@@ -327,6 +327,7 @@ def emit_ops(emitter_td: TextEmitter, registry: Registry):
     emit("aten::bernoulli_.Tensor : (Tensor, Tensor, Generator?) -> (Tensor)")
 
     emit_with_mutating_variants("aten::triu : (Tensor, int) -> (Tensor)")
+    emit_with_mutating_variants("aten::round : (Tensor) -> (Tensor)", has_folder=True)
     emit_with_mutating_variants(
         "aten::index_put : (Tensor, Tensor?[], Tensor, bool) -> (Tensor)")
     emit_with_mutating_variants(

python/torch_mlir_e2e_test/test_suite/elementwise.py

@@ -2273,3 +2273,42 @@ class AtenTriuWithNegDiagonalModule(torch.nn.Module):
 @register_test_case(module_factory=lambda: AtenTriuWithNegDiagonalModule())
 def AtenTriuWithNegDiagonalModule_basic(module, tu: TestUtils):
     module.forward(tu.rand(3, 1, 5, 9))
+
+# ==============================================================================
+
+
+class AtenRoundFloatModule(torch.nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1, -1], torch.float32, True),
+    ])
+    def forward(self, x):
+        return torch.ops.aten.round(x)
+
+
+@register_test_case(module_factory=lambda: AtenRoundFloatModule())
+def AtenRoundFloatModule_basic(module, tu: TestUtils):
+    module.forward(tu.rand(5, 5, low = -3.0, high = 3.0))
+
+class AtenRoundIntModule(torch.nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1, -1], torch.int64, True),
+    ])
+    def forward(self, x):
+        return torch.ops.aten.round(x)
+
+
+@register_test_case(module_factory=lambda: AtenRoundIntModule())
+def AtenRoundIntModule_basic(module, tu: TestUtils):
+    module.forward(tu.randint(5, 5, low = -10))