[Torch Dialect] emit aten.__or__Tensor Op (#2437)

* emit aten.__or__TensorOp

* bug fix

* remove convert to stablehlo

* code style refinement

e2e_testing/xfail_sets.py

@@ -452,6 +452,7 @@ STABLEHLO_PASS_SET = {
     "ElementwiseBitwiseAndStaticShapeModule_basic",
     "ElementwiseBitwiseNotInt64Module_basic",
     "ElementwiseBitwiseNotInt32Module_basic",
+    "ElementwiseOrTensorStaticShapeModule_basic",
     "ElementwiseBitwiseOrStaticShapeModule_basic",
     "ElementwiseBitwiseXorStaticShapeModule_basic",
     "ElementwiseClampModule_basic",
@@ -985,6 +986,8 @@ TOSA_PASS_SET = {
     "ElementwiseBitwiseAndStaticShapeModule_basic",
     "ElementwiseBitwiseNotInt32Module_basic",
     "ElementwiseBitwiseNotInt64Module_basic",
+    "ElementwiseOrTensorStaticShapeModule_basic",
+    "ElementwiseOrTensorModule_basic",
     "ElementwiseBitwiseOrModule_basic",
     "ElementwiseBitwiseOrStaticShapeModule_basic",
     "ElementwiseBitwiseXorModule_basic",

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

@@ -6141,6 +6141,31 @@ def Torch_Aten__And__TensorOp : Torch_Op<"aten.__and__.Tensor", [
   }];
 }
 
+def Torch_Aten__Or__TensorOp : Torch_Op<"aten.__or__.Tensor", [
+    AllowsTypeRefinement,
+    HasValueSemantics,
+    ReadOnly
+  ]> {
+  let summary = "Generated op for `aten::__or__.Tensor : (Tensor, Tensor) -> (Tensor)`";
+  let arguments = (ins
+    AnyTorchTensorType:$self,
+    AnyTorchTensorType:$other
+  );
+  let results = (outs
+    AnyTorchTensorType:$result
+  );
+  let hasCustomAssemblyFormat = 1;
+  let extraClassDefinition = [{
+    ParseResult Aten__Or__TensorOp::parse(OpAsmParser &parser, OperationState &result) {
+      return parseDefaultTorchOp(parser, result, 2, 1);
+    }
+    void Aten__Or__TensorOp::print(OpAsmPrinter &printer) {
+      printDefaultTorchOp(printer, *this, 2, 1);
+    }
+  }];
+  let hasCanonicalizer = 1;
+}
+
 def Torch_Aten_SoftmaxOp : Torch_Op<"aten._softmax", [
     AllowsTypeRefinement,
     HasValueSemantics,

lib/Dialect/Torch/IR/TorchOps.cpp

@@ -1124,6 +1124,19 @@ void AtenDivTensorModeOp::getCanonicalizationPatterns(
   });
 }
 
+//===----------------------------------------------------------------------===//
+// Aten__Or__TensorOp
+//===----------------------------------------------------------------------===//
+
+void Aten__Or__TensorOp::getCanonicalizationPatterns(
+    RewritePatternSet &patterns, MLIRContext *context) {
+  patterns.add(+[](Aten__Or__TensorOp op, PatternRewriter &rewriter) {
+    rewriter.replaceOpWithNewOp<AtenBitwiseOrTensorOp>(
+        op, op.getType(), op.getSelf(), op.getOther());
+    return success();
+  });
+}
+
 //===----------------------------------------------------------------------===//
 // AtenScalarImplicitOp
 //===----------------------------------------------------------------------===//

lib/Dialect/Torch/Transforms/AbstractInterpLibrary.cpp

@@ -7376,6 +7376,10 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    %0 = call @__torch__.torch.jit._shape_functions.broadcast(%arg0, %arg1) : (!torch.list<int>, !torch.list<int>) -> !torch.list<int>\n"
 "    return %0 : !torch.list<int>\n"
 "  }\n"
+"  func.func @\"__torch_mlir_shape_fn.aten.__or__.Tensor\"(%arg0: !torch.list<int>, %arg1: !torch.list<int>) -> !torch.list<int> {\n"
+"    %0 = call @__torch__.torch.jit._shape_functions.broadcast(%arg0, %arg1) : (!torch.list<int>, !torch.list<int>) -> !torch.list<int>\n"
+"    return %0 : !torch.list<int>\n"
+"  }\n"
 "  func.func @\"__torch_mlir_shape_fn.aten.minimum\"(%arg0: !torch.list<int>, %arg1: !torch.list<int>) -> !torch.list<int> {\n"
 "    %0 = call @__torch__.torch.jit._shape_functions.broadcast(%arg0, %arg1) : (!torch.list<int>, !torch.list<int>) -> !torch.list<int>\n"
 "    return %0 : !torch.list<int>\n"
@@ -9154,6 +9158,14 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    %4 = call @__torch__.torch_mlir.dialects.torch.importer.jit_ir.build_tools.library_generator.promote_dtypes(%2, %3) : (!torch.list<optional<int>>, !torch.list<int>) -> !torch.int\n"
 "    return %4 : !torch.int\n"
 "  }\n"
+"  func.func @\"__torch_mlir_dtype_fn.aten.__or__.Tensor\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.tuple<int, int>) -> !torch.int {\n"
+"    %0:2 = torch.prim.TupleUnpack %arg1 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
+"    %1:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
+"    %2 = torch.prim.ListConstruct %1#0, %0#0 : (!torch.int, !torch.int) -> !torch.list<optional<int>>\n"
+"    %3 = torch.prim.ListConstruct %1#1, %0#1 : (!torch.int, !torch.int) -> !torch.list<int>\n"
+"    %4 = call @__torch__.torch_mlir.dialects.torch.importer.jit_ir.build_tools.library_generator.promote_dtypes(%2, %3) : (!torch.list<optional<int>>, !torch.list<int>) -> !torch.int\n"
+"    return %4 : !torch.int\n"
+"  }\n"
 "  func.func @\"__torch_mlir_dtype_fn.aten.add.Tensor\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.tuple<int, int>, %arg2: !torch.number) -> !torch.int {\n"
 "    %0:2 = torch.prim.TupleUnpack %arg1 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
 "    %1:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"

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

@@ -771,6 +771,9 @@ def aten〇atan2〡shape(self: List[int], other: List[int]) -> List[int]:
 def aten〇__and__〇Tensor〡shape(self: List[int], other: List[int]) -> List[int]:
     return upstream_shape_functions.broadcast(self, other)
 
+def aten〇__or__〇Tensor〡shape(self: List[int], other: List[int]) -> List[int]:
+    return upstream_shape_functions.broadcast(self, other)
+
 def aten〇minimum〡shape(self: List[int], other: List[int]) -> List[int]:
     return upstream_shape_functions.broadcast(self, other)
 
@@ -2223,6 +2226,14 @@ def aten〇__and__〇Tensor〡dtype(self_rank_dtype: Tuple[int, int], other_rank
     dtypes = [self_dtype, other_dtype]
     return promote_dtypes(ranks, dtypes)
 
+@check_dtype_function(_check_two_tensor_op())
+def aten〇__or__〇Tensor〡dtype(self_rank_dtype: Tuple[int, int], other_rank_dtype: Tuple[int, int]) -> int:
+    other_rank, other_dtype = other_rank_dtype
+    self_rank, self_dtype = self_rank_dtype
+    ranks: List[Optional[int]] = [self_rank, other_rank]
+    dtypes = [self_dtype, other_dtype]
+    return promote_dtypes(ranks, dtypes)
+
 @check_dtype_function(_check_two_tensor_op())
 def aten〇add〇Tensor〡dtype(self_rank_dtype: Tuple[int, int], other_rank_dtype: Tuple[int, int], alpha: Union[int, float, complex] = 1) -> int:
     other_rank, other_dtype = other_rank_dtype

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

@@ -456,6 +456,7 @@ def emit_ops(emitter_td: TextEmitter, registry: Registry):
     emit("aten::logsumexp : (Tensor, int[], bool) -> (Tensor)")
     emit("aten::mean.dim : (Tensor, int[]?, bool, int?) -> (Tensor)")
     emit("aten::__and__.Tensor : (Tensor, Tensor) -> (Tensor)")
+    emit("aten::__or__.Tensor : (Tensor, Tensor) -> (Tensor)",  has_canonicalizer=True)
     emit("aten::_softmax : (Tensor, int, bool) -> (Tensor)")
     emit("aten::mean : (Tensor, int?) -> (Tensor)")
     emit("aten::std : (Tensor, bool) -> (Tensor)")

python/torch_mlir_e2e_test/test_suite/elementwise.py

@@ -2024,6 +2024,56 @@ def ElementwiseBitwiseOrStaticShapeModule_basic(module, tu: TestUtils):
 # ==============================================================================
 
 
+class ElementwiseOrTensorModule(torch.nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1, -1], torch.int32, True),
+        ([-1, -1], torch.int64, True),
+    ])
+    def forward(self, x, y):
+        return torch.ops.aten.__or__(x, y)
+
+
+@register_test_case(module_factory=lambda: ElementwiseOrTensorModule())
+def ElementwiseOrTensorModule_basic(module, tu: TestUtils):
+    module.forward(
+        tu.randint(3, 4, low=-10, high=10).to(torch.int32),
+        tu.randint(3, 4, low=-10, high=10))
+
+
+# ==============================================================================
+
+
+class ElementwiseOrTensorStaticShapeModule(torch.nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([3, 4], torch.int32, True),
+        ([4], torch.int64, True),
+    ])
+    def forward(self, x, y):
+        return torch.ops.aten.__or__(x, y)
+
+
+@register_test_case(module_factory=lambda: ElementwiseOrTensorStaticShapeModule())
+def ElementwiseOrTensorStaticShapeModule_basic(module, tu: TestUtils):
+    module.forward(
+        tu.randint(3, 4, low=-10, high=10).to(torch.int32),
+        tu.randint(4, low=-10, high=10))
+
+
+# ==============================================================================
+
+
 class ElementwiseBitwiseXorModule(torch.nn.Module):
 
     def __init__(self):