Add E2E support for AtenLogicalOrOp. (#883)

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

@@ -982,6 +982,53 @@ def Torch_AtenDiv_TensorOp : Torch_Op<"aten.div_.Tensor", [
   }];
 }
 
+def Torch_AtenLogicalOrOp : Torch_Op<"aten.logical_or", [
+    AllowsTypeRefinement,
+    HasValueSemantics,
+    ReadOnly
+  ]> {
+  let summary = "Generated op for `aten::logical_or : (Tensor, Tensor) -> (Tensor)`";
+  let arguments = (ins
+    AnyTorchTensorType:$self,
+    AnyTorchTensorType:$other
+  );
+  let results = (outs
+    AnyTorchTensorType:$result
+  );
+  let hasCustomAssemblyFormat = 1;
+  let extraClassDefinition = [{
+    ParseResult AtenLogicalOrOp::parse(OpAsmParser &parser, OperationState &result) {
+      return parseDefaultTorchOp(parser, result, 2, 1);
+    }
+    void AtenLogicalOrOp::print(OpAsmPrinter &printer) {
+      printDefaultTorchOp(printer, *this, 2, 1);
+    }
+  }];
+}
+
+def Torch_AtenLogicalOr_Op : Torch_Op<"aten.logical_or_", [
+    IsTrailingUnderscoreInplaceVariant,
+    AllowsTypeRefinement
+  ]> {
+  let summary = "Generated op for `aten::logical_or_ : (Tensor, Tensor) -> (Tensor)`";
+  let arguments = (ins
+    AnyTorchTensorType:$self,
+    AnyTorchTensorType:$other
+  );
+  let results = (outs
+    AnyTorchTensorType:$result
+  );
+  let hasCustomAssemblyFormat = 1;
+  let extraClassDefinition = [{
+    ParseResult AtenLogicalOr_Op::parse(OpAsmParser &parser, OperationState &result) {
+      return parseDefaultTorchOp(parser, result, 2, 1);
+    }
+    void AtenLogicalOr_Op::print(OpAsmPrinter &printer) {
+      printDefaultTorchOp(printer, *this, 2, 1);
+    }
+  }];
+}
+
 def Torch_AtenLerpTensorOp : Torch_Op<"aten.lerp.Tensor", [
     AllowsTypeRefinement,
     HasValueSemantics,

lib/Conversion/TorchToLinalg/Uncategorized.cpp

@@ -190,6 +190,17 @@ static Value createLinalgPayloadCalculationForElementwiseOp(
     Value rhs = convertScalarToDtype(b, loc, payloadArgs[1], dtype);
     return b.create<arith::AndIOp>(loc, lhs, rhs);
   }
+  if (auto logicalOr = dyn_cast<AtenLogicalOrOp>(op)) {
+    MLIRContext *context = op->getContext();
+    Type floatDtype = mlir::FloatType::getF64(context);
+    Value lhs = convertScalarToDtype(b, loc, payloadArgs[0], floatDtype);
+    Value rhs = convertScalarToDtype(b, loc, payloadArgs[1], floatDtype);
+    Value zero =
+        b.create<arith::ConstantOp>(loc, b.getFloatAttr(floatDtype, 0));
+    Value lhsTest = createNotEqual(b, loc, floatDtype, lhs, zero);
+    Value rhsTest = createNotEqual(b, loc, floatDtype, rhs, zero);
+    return b.create<arith::OrIOp>(loc, lhsTest, rhsTest);
+  }
   if (isa<AtenAbsOp>(op))
     return b.create<math::AbsOp>(loc, payloadArgs[0]);
   if (isa<AtenSigmoidOp>(op)) {
@@ -844,7 +855,8 @@ public:
              AtenWhereSelfOp, AtenCeilOp, AtenGtTensorOp, AtenEqTensorOp,
              AtenLtTensorOp, AtenSubScalarOp, AtenAddScalarOp, AtenThresholdOp,
              AtenThresholdBackwardOp, AtenCloneOp, AtenSinOp, AtenCosOp,
-             AtenNeScalarOp, AtenNegOp, AtenMaskedFillScalarOp>(op))
+             AtenNeScalarOp, AtenNegOp, AtenMaskedFillScalarOp,
+             AtenLogicalOrOp>(op))
       return rewriter.notifyMatchFailure(op, "not a supported elementwise op");
 
     if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
@@ -1581,7 +1593,8 @@ void mlir::torch::torch_to_linalg::populateUncategorizedPatternsAndLegality(
       AtenGtScalarOp, AtenGeScalarOp, AtenEqScalarOp, AtenLtScalarOp,
       AtenLeScalarOp, AtenWhereSelfOp, AtenGtTensorOp, AtenEqTensorOp,
       AtenLtTensorOp, AtenThresholdOp, AtenThresholdBackwardOp, AtenCloneOp,
-      AtenSinOp, AtenCosOp, AtenNeScalarOp, AtenMaskedFillScalarOp>();
+      AtenSinOp, AtenCosOp, AtenNeScalarOp, AtenMaskedFillScalarOp,
+      AtenLogicalOrOp>();
   patterns.add<ConvertElementwiseOp>(typeConverter, context);
   target.addIllegalOp<AtenNllLossForwardOp>();
   patterns.add<ConvertAtenDetachOp>(typeConverter, context);

lib/Dialect/Torch/Transforms/RefineTypes.cpp

@@ -673,7 +673,7 @@ ChangeResult TypeAnalyzer::visitOperation(
   // Dtype is always i1.
   if (isa<AtenEqScalarOp, AtenGeScalarOp, AtenGtScalarOp, AtenLtScalarOp,
           AtenLeScalarOp, AtenNeScalarOp, AtenAnyOp, AtenAllOp, AtenEqTensorOp,
-          AtenGtTensorOp, AtenLtTensorOp>(op)) {
+          AtenGtTensorOp, AtenLtTensorOp, AtenLogicalOrOp>(op)) {
     auto knowledge =
         ValueKnowledge::getTensorPessimisticValueState(op->getContext());
     knowledge.dtype = IntegerType::get(op->getContext(), 1);

lib/Dialect/Torch/Transforms/ShapeLibrary.cpp

@@ -2155,6 +2155,10 @@ module {
     %0 = call @__torch__.torch_mlir.dialects.torch.importer.jit_ir.build_tools.upstream_shape_helpers.broadcast(%arg0, %arg1) : (!torch.list<int>, !torch.list<int>) -> !torch.list<int>
     return %0 : !torch.list<int>
   }
+  func.func @"__torch_mlir_shape_fn.aten.logical_or"(%arg0: !torch.list<int>, %arg1: !torch.list<int>) -> !torch.list<int> {
+    %0 = call @__torch__.torch_mlir.dialects.torch.importer.jit_ir.build_tools.upstream_shape_helpers.broadcast(%arg0, %arg1) : (!torch.list<int>, !torch.list<int>) -> !torch.list<int>
+    return %0 : !torch.list<int>
+  }
   func.func @"__torch_mlir_shape_fn.aten.threshold"(%arg0: !torch.list<int>, %arg1: !torch.float, %arg2: !torch.float) -> !torch.list<int> {
     %0 = call @__torch__.torch_mlir.dialects.torch.importer.jit_ir.build_tools.upstream_shape_helpers.unary(%arg0) : (!torch.list<int>) -> !torch.list<int>
     return %0 : !torch.list<int>

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

@@ -710,6 +710,9 @@ def aten〇maximum(self: List[int], other: List[int]) -> List[int]:
 def aten〇bitwise_and〇Tensor(self: List[int], other: List[int]) -> List[int]:
     return upstream_shape_helpers.broadcast(self, other)
 
+def aten〇logical_or(self: List[int], other: List[int]) -> List[int]:
+    return upstream_shape_helpers.broadcast(self, other)
+
 def aten〇threshold(self: List[int], threshold: float, value: float) -> List[int]:
     return upstream_shape_helpers.unary(self)
 

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

@@ -249,6 +249,7 @@ def emit_ops(emitter_td: TextEmitter, registry: Registry):
             "aten::sub.Tensor : (Tensor, Tensor, Scalar) -> (Tensor)",
             "aten::mul.Tensor : (Tensor, Tensor) -> (Tensor)",
             "aten::div.Tensor : (Tensor, Tensor) -> (Tensor)",
+            "aten::logical_or : (Tensor, Tensor) -> (Tensor)",
             "aten::lerp.Tensor : (Tensor, Tensor, Tensor) -> (Tensor)",
             "aten::eq.Tensor : (Tensor, Tensor) -> (Tensor)",
             "aten::gt.Tensor : (Tensor, Tensor) -> (Tensor)",

python/torch_mlir_e2e_test/test_suite/elementwise.py

@@ -1302,3 +1302,156 @@ class ElementwiseNegModule(torch.nn.Module):
 @register_test_case(module_factory=lambda: ElementwiseNegModule())
 def ElementwiseNegModule_basic(module, tu: TestUtils):
     module.forward(tu.rand(3, 4))
+
+# ==============================================================================
+
+class ElementwiseAtenLogicalOrOpModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1], torch.bool, True),
+        ([-1], torch.bool, True),
+    ])
+    def forward(self, x, y):
+        return torch.ops.aten.logical_or(x, y)
+
+@register_test_case(module_factory=lambda: ElementwiseAtenLogicalOrOpModule())
+def ElementwiseAtenLogicalOrOpModule_basic(module, tu: TestUtils):
+    module.forward(torch.tensor([False, True]), torch.tensor([False, False]))
+
+class ElementwiseAtenLogicalOrOpDiffArgs1Module(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1], torch.float64, True),
+        ([-1], torch.int64, True),
+    ])
+    def forward(self, x, y):
+        return torch.ops.aten.logical_or(x, y)
+
+@register_test_case(module_factory=lambda: ElementwiseAtenLogicalOrOpDiffArgs1Module())
+def ElementwiseAtenLogicalOrOpDiffArgs1Module_basic(module, tu: TestUtils):
+    module.forward(torch.tensor([0.2, 0.1]), torch.tensor([0, 1]))
+
+# ==============================================================================
+
+class ElementwiseAtenLogicalOrOpDiffArgs2Module(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1], torch.bool, True),
+        ([-1], torch.int64, True),
+    ])
+    def forward(self, x, y):
+        return torch.ops.aten.logical_or(x, y)
+
+@register_test_case(module_factory=lambda: ElementwiseAtenLogicalOrOpDiffArgs2Module())
+def ElementwiseAtenLogicalOrOpDiffArgs2Module_basic(module, tu: TestUtils):
+    module.forward(torch.tensor([True, False]), torch.tensor([0, 1]))
+
+# ==============================================================================
+
+class ElementwiseAtenLogicalOrOpDiffArgs3Module(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1], torch.int64, True),
+        ([-1], torch.bool, True),
+    ])
+    def forward(self, x, y):
+        return torch.ops.aten.logical_or(x, y)
+
+@register_test_case(module_factory=lambda: ElementwiseAtenLogicalOrOpDiffArgs3Module())
+def ElementwiseAtenLogicalOrOpDiffArgs3Module_basic(module, tu: TestUtils):
+    module.forward(torch.tensor([1, 2]), torch.tensor([False, True]))
+
+# ==============================================================================
+
+class ElementwiseAtenLogicalOrOpRandomModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1, -1, -1, -1], torch.int64, True),
+        ([-1, -1, -1, -1], torch.int64, True),
+    ])
+    def forward(self, x, y):
+        return torch.ops.aten.logical_or(x, y)
+
+@register_test_case(module_factory=lambda: ElementwiseAtenLogicalOrOpRandomModule())
+def ElementwiseAtenLogicalOrOpRandomModule_basic(module, tu: TestUtils):
+    module.forward(torch.randint(3, 10, (2, 3, 4, 5)), torch.randint(10, 100, (2, 3, 4, 5)))
+
+# ==============================================================================
+
+class ElementwiseAtenLogicalOrOpRandomFloatModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1, -1, -1, -1], torch.float32, True),
+        ([-1, -1, -1, -1], torch.float32, True),
+    ])
+    def forward(self, x, y):
+        return torch.ops.aten.logical_or(x, y)
+
+@register_test_case(module_factory=lambda: ElementwiseAtenLogicalOrOpRandomFloatModule())
+def ElementwiseAtenLogicalOrOpRandomFloatModule_basic(module, tu: TestUtils):
+    module.forward(torch.rand(2, 3, 3, 5), torch.rand(2, 3, 3, 5))
+
+# ==============================================================================
+
+class ElementwiseAtenLogicalOrOpNegativeModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+    
+    @export
+    @annotate_args([
+        None,
+        ([-1, -1, -1, -1], torch.int64, True),
+        ([-1, -1, -1, -1], torch.int64, True),
+    ])
+    def forward(self, x, y):
+        return torch.ops.aten.logical_or(x, y)
+
+@register_test_case(module_factory=lambda: ElementwiseAtenLogicalOrOpNegativeModule())
+def ElementwiseAtenLogicalOrOpNegativeModule_basic(module, tu: TestUtils):
+    module.forward(torch.neg(torch.randint(3, 10, (2, 3, 4, 5))), torch.neg(torch.randint(10, 100, (2, 3, 4, 5))))
+
+# ==============================================================================
+
+class ElementwiseAtenLogicalOrOpBrodcastModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+    
+    @export
+    @annotate_args([
+        None,
+        ([-1],     torch.int64, True),
+        ([-1, -1], torch.int64, True),
+    ])
+    def forward(self, x, y):
+        return torch.ops.aten.logical_or(x, y)
+
+@register_test_case(module_factory=lambda: ElementwiseAtenLogicalOrOpBrodcastModule())
+def ElementwiseAtenLogicalOrOpBrodcastModule_basic(module, tu: TestUtils):
+    module.forward(torch.randint(3, (3,)), torch.randint(3, (4, 3)))
+
+
+