[Torch Dialect] canonicalize aten.sign to aten.sgn (#3112)

* `aten.sign` is a sub-set of `aten.sgn` (`aten.sgn` support complex
type).

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

@@ -391,51 +391,6 @@ def Torch_AtenSigmoid_Op : Torch_Op<"aten.sigmoid_", [
   }];
 }
 
-def Torch_AtenSignOp : Torch_Op<"aten.sign", [
-    AllowsTypeRefinement,
-    HasValueSemantics,
-    ReadOnly
-  ]> {
-  let summary = "Generated op for `aten::sign : (Tensor) -> (Tensor)`";
-  let arguments = (ins
-    AnyTorchTensorType:$self
-  );
-  let results = (outs
-    AnyTorchOptionalTensorType:$result
-  );
-  let hasCustomAssemblyFormat = 1;
-  let extraClassDefinition = [{
-    ParseResult AtenSignOp::parse(OpAsmParser &parser, OperationState &result) {
-      return parseDefaultTorchOp(parser, result, 1, 1);
-    }
-    void AtenSignOp::print(OpAsmPrinter &printer) {
-      printDefaultTorchOp(printer, *this, 1, 1);
-    }
-  }];
-}
-
-def Torch_AtenSign_Op : Torch_Op<"aten.sign_", [
-    IsTrailingUnderscoreInplaceVariant,
-    AllowsTypeRefinement
-  ]> {
-  let summary = "Generated op for `aten::sign_ : (Tensor) -> (Tensor)`";
-  let arguments = (ins
-    Torch_NonValueTensorType:$self
-  );
-  let results = (outs
-    AnyTorchOptionalNonValueTensorType:$result
-  );
-  let hasCustomAssemblyFormat = 1;
-  let extraClassDefinition = [{
-    ParseResult AtenSign_Op::parse(OpAsmParser &parser, OperationState &result) {
-      return parseDefaultTorchOp(parser, result, 1, 1);
-    }
-    void AtenSign_Op::print(OpAsmPrinter &printer) {
-      printDefaultTorchOp(printer, *this, 1, 1);
-    }
-  }];
-}
-
 def Torch_AtenSinhOp : Torch_Op<"aten.sinh", [
     AllowsTypeRefinement,
     HasValueSemantics,
@@ -4218,6 +4173,52 @@ def Torch_AtenRound_Op : Torch_Op<"aten.round_", [
   }];
 }
 
+def Torch_AtenSignOp : Torch_Op<"aten.sign", [
+    AllowsTypeRefinement,
+    HasValueSemantics,
+    ReadOnly
+  ]> {
+  let summary = "Generated op for `aten::sign : (Tensor) -> (Tensor)`";
+  let arguments = (ins
+    AnyTorchTensorType:$self
+  );
+  let results = (outs
+    AnyTorchOptionalTensorType:$result
+  );
+  let hasCustomAssemblyFormat = 1;
+  let extraClassDefinition = [{
+    ParseResult AtenSignOp::parse(OpAsmParser &parser, OperationState &result) {
+      return parseDefaultTorchOp(parser, result, 1, 1);
+    }
+    void AtenSignOp::print(OpAsmPrinter &printer) {
+      printDefaultTorchOp(printer, *this, 1, 1);
+    }
+  }];
+  let hasCanonicalizer = 1;
+}
+
+def Torch_AtenSign_Op : Torch_Op<"aten.sign_", [
+    IsTrailingUnderscoreInplaceVariant,
+    AllowsTypeRefinement
+  ]> {
+  let summary = "Generated op for `aten::sign_ : (Tensor) -> (Tensor)`";
+  let arguments = (ins
+    Torch_NonValueTensorType:$self
+  );
+  let results = (outs
+    AnyTorchOptionalNonValueTensorType:$result
+  );
+  let hasCustomAssemblyFormat = 1;
+  let extraClassDefinition = [{
+    ParseResult AtenSign_Op::parse(OpAsmParser &parser, OperationState &result) {
+      return parseDefaultTorchOp(parser, result, 1, 1);
+    }
+    void AtenSign_Op::print(OpAsmPrinter &printer) {
+      printDefaultTorchOp(printer, *this, 1, 1);
+    }
+  }];
+}
+
 def Torch_AtenMaskedFillTensorOp : Torch_Op<"aten.masked_fill.Tensor", [
     AllowsTypeRefinement,
     HasValueSemantics,

lib/Dialect/Torch/IR/TorchOps.cpp

@@ -1793,6 +1793,17 @@ OpFoldResult AtenRoundOp::fold(FoldAdaptor adaptor) {
   return {};
 }
 
+//===----------------------------------------------------------------------===//
+// AtenSignOp
+//===----------------------------------------------------------------------===//
+void AtenSignOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
+                                             MLIRContext *context) {
+  patterns.add(+[](AtenSignOp op, PatternRewriter &rewriter) {
+    rewriter.replaceOpWithNewOp<AtenSgnOp>(op, op.getType(), op.getSelf());
+    return success();
+  });
+}
+
 //===----------------------------------------------------------------------===//
 // AtenMulScalarOp
 //===----------------------------------------------------------------------===//

lib/Dialect/Torch/Transforms/AbstractInterpLibrary.cpp

@@ -6442,6 +6442,10 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    %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.sgn\"(%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.detach\"(%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"
@@ -10129,6 +10133,10 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    %0:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
 "    return %0#1 : !torch.int\n"
 "  }\n"
+"  func.func @\"__torch_mlir_dtype_fn.aten.sgn\"(%arg0: !torch.tuple<int, int>) -> !torch.int {\n"
+"    %0:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
+"    return %0#1 : !torch.int\n"
+"  }\n"
 "  func.func @\"__torch_mlir_dtype_fn.aten.floor\"(%arg0: !torch.tuple<int, int>) -> !torch.int {\n"
 "    %0:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
 "    return %0#1 : !torch.int\n"

lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp

@@ -6971,46 +6971,54 @@ public:
 } // namespace
 
 namespace {
-// Decompose `aten.sign` op into comparisons and aten.where.
+// Decompose `aten.sgn` op into comparisons and aten.where.
-class DecomposeAtenSignOp : public OpRewritePattern<AtenSignOp> {
+class DecomposeAtenSgnOp : public OpRewritePattern<AtenSgnOp> {
 public:
   using OpRewritePattern::OpRewritePattern;
-  LogicalResult matchAndRewrite(AtenSignOp op,
+  LogicalResult matchAndRewrite(AtenSgnOp op,
                                 PatternRewriter &rewriter) const override {
     Location loc = op.getLoc();
-    auto outType = op.getType().dyn_cast<BaseTensorType>();
+    auto outType = op.getType().cast<BaseTensorType>();
-    if (!outType)
+    if (!outType.hasDtype()) {
-      return rewriter.notifyMatchFailure(
+      return rewriter.notifyMatchFailure(op,
-          op, "Only tensor types input are currently supported");
+                                         "expected result type to have dtype");
+    }
+    // TODO: support complex type in future.
+    if (outType.getDtype().isa<mlir::ComplexType>()) {
+      return rewriter.notifyMatchFailure(op,
+                                         "doesn't support complex type now");
+    }
 
     auto zero =
-        rewriter.create<ConstantFloatOp>(loc, rewriter.getF64FloatAttr(0.0));
+        rewriter.create<ConstantIntOp>(loc, rewriter.getI64IntegerAttr(0));
     auto one =
-        rewriter.create<ConstantFloatOp>(loc, rewriter.getF64FloatAttr(1.0));
+        rewriter.create<ConstantIntOp>(loc, rewriter.getI64IntegerAttr(1));
     auto minusOne =
-        rewriter.create<ConstantFloatOp>(loc, rewriter.getF64FloatAttr(-1.0));
+        rewriter.create<ConstantIntOp>(loc, rewriter.getI64IntegerAttr(-1));
 
     auto compTy = outType.getWithSizesAndDtype(outType.getOptionalSizes(),
                                                rewriter.getI1Type());
 
     auto greater =
         rewriter.create<AtenGtScalarOp>(loc, compTy, op.getSelf(), zero);
-    auto greaterEqual =
+    auto less =
-        rewriter.create<AtenGeScalarOp>(loc, compTy, op.getSelf(), zero);
+        rewriter.create<AtenLtScalarOp>(loc, compTy, op.getSelf(), zero);
 
     // Pseudo code:
-    // if (in >= 0)
+    // if (in > 0)
-    //   if (in > 0)
     //     return 1
-    //   else
+    // else if (in < 0)
-    //     return 0
-    // else
     //   return -1
+    // else
+    //   return 0
+    // note: return 0 if nan/0.0/-0.0
+    //       return 1 if inf
+    //       return -1 if -inf
     auto selectGreater =
         rewriter.create<AtenWhereScalarOp>(loc, outType, greater, one, zero);
 
-    rewriter.replaceOpWithNewOp<AtenWhereScalarOtherOp>(
+    rewriter.replaceOpWithNewOp<AtenWhereScalarSelfOp>(op, outType, less,
-        op, outType, greaterEqual, selectGreater, minusOne);
+                                                       minusOne, selectGreater);
     return success();
   }
 };
@@ -7606,7 +7614,7 @@ public:
     addPatternIfTargetOpIsIllegal<DecomposeAtenTopkOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenScalarTensor>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenScatterValueOp>(patterns);
-    addPatternIfTargetOpIsIllegal<DecomposeAtenSignOp>(patterns);
+    addPatternIfTargetOpIsIllegal<DecomposeAtenSgnOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenTypeAsOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenTileOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenReshapeAsOp>(patterns);

projects/pt1/e2e_testing/xfail_sets.py

@@ -888,6 +888,8 @@ STABLEHLO_PASS_SET = {
     "ViewTwoFiveThreeStaticModule_basic",
     "ViewTwoToThreeStaticModule_basic",
     "ElementwiseLog1pModule_basic",
+    "ElementwiseSgnModule_basic",
+    "ElementwiseSignIntModule_basic",
 }
 
 STABLEHLO_CRASHING_SET =  {
@@ -897,6 +899,8 @@ STABLEHLO_CRASHING_SET =  {
 # Write the TOSA set as a "passing" set as it is very early in development
 # and very few tests work yet.
 TOSA_PASS_SET = {
+    "ElementwiseSgnModule_basic",
+    "ElementwiseSignIntModule_basic",
     "AdaptiveAvgPool2dNonUnitOutputSizeStaticModule_basic",
     "AdaptiveAvgPool2dUnitOutputSizeStaticModule_basic",
     "AddCDivModule_basic",
@@ -1567,6 +1571,7 @@ ONNX_XFAIL_SET = {
     "ViewSizeFromOtherTensor_basic",
 
     # Failure - onnx_export
+    "ElementwiseSgnModule_basic",
     "AdaptiveAvgPool1dGeneralDynamic_basic",
     "AdaptiveAvgPool1dNonUnitOutputSizeDynamicModule_basic",
     "AdaptiveAvgPool1dStaticLargerOutput_basic",

projects/pt1/python/torch_mlir/jit_ir_importer/build_tools/abstract_interp_lib_gen.py

@@ -200,6 +200,9 @@ def aten〇floor〡shape(self: List[int]) -> List[int]:
 def aten〇sign〡shape(self: List[int]) -> List[int]:
     return upstream_shape_functions.unary(self)
 
+def aten〇sgn〡shape(self: List[int]) -> List[int]:
+    return upstream_shape_functions.unary(self)
+
 def aten〇detach〡shape(self: List[int]) -> List[int]:
     return upstream_shape_functions.unary(self)
 
@@ -2346,6 +2349,11 @@ def aten〇sign〡dtype(self_rank_dtype: Tuple[int, int]) -> int:
     self_rank, self_dtype = self_rank_dtype
     return self_dtype
 
+@check_dtype_function(_check_tensors_with_the_same_dtype(num_of_tensors=1))
+def aten〇sgn〡dtype(self_rank_dtype: Tuple[int, int]) -> int:
+    self_rank, self_dtype = self_rank_dtype
+    return self_dtype
+
 @check_dtype_function(_check_tensors_with_the_same_dtype(num_of_tensors=1))
 def aten〇floor〡dtype(self_rank_dtype: Tuple[int, int]) -> int:
     self_rank, self_dtype = self_rank_dtype

projects/pt1/python/torch_mlir/jit_ir_importer/build_tools/torch_ods_gen.py

@@ -269,7 +269,6 @@ def emit_ops(emitter_td: TextEmitter, registry: Registry):
             "aten::log : (Tensor) -> (Tensor)",
             "aten::selu : (Tensor) -> (Tensor)",
             "aten::sigmoid : (Tensor) -> (Tensor)",
-            "aten::sign : (Tensor) -> (Tensor)",
             "aten::sinh : (Tensor) -> (Tensor)",
             "aten::sgn : (Tensor) -> (Tensor)",
             "aten::hardsigmoid : (Tensor) -> (Tensor)",
@@ -357,6 +356,7 @@ def emit_ops(emitter_td: TextEmitter, registry: Registry):
     emit_with_mutating_variants("aten::floor : (Tensor) -> (Tensor)", has_folder=True)
     emit_with_mutating_variants("aten::ceil : (Tensor) -> (Tensor)", has_folder=True)
     emit_with_mutating_variants("aten::round : (Tensor) -> (Tensor)", has_folder=True)
+    emit_with_mutating_variants("aten::sign : (Tensor) -> (Tensor)", has_canonicalizer=True)
     emit_with_mutating_variants("aten::masked_fill.Tensor : (Tensor, Tensor, Tensor) -> (Tensor)", has_canonicalizer=True)
 
     emit_with_mutating_variants("aten::addcmul : (Tensor, Tensor, Tensor, Scalar) -> (Tensor)")

projects/pt1/python/torch_mlir_e2e_test/test_suite/elementwise.py

@@ -1986,6 +1986,51 @@ class ElementwiseSignModule(torch.nn.Module):
 
 @register_test_case(module_factory=lambda: ElementwiseSignModule())
 def ElementwiseSignModule_basic(module, tu: TestUtils):
+    module.forward(torch.tensor([[-2.0, 0.0, 1.1, 2.0],
+                                 [6.0, -0.0, torch.inf, -torch.inf]]))
+
+
+# ==============================================================================
+
+
+class ElementwiseSignIntModule(torch.nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([3, 4], torch.int64, True),
+    ])
+    def forward(self, a):
+        return torch.ops.aten.sign(a)
+
+
+@register_test_case(module_factory=lambda: ElementwiseSignIntModule())
+def ElementwiseSignIntModule_basic(module, tu: TestUtils):
+    module.forward(tu.randint(3, 4, low=-100, high=100))
+
+
+# ==============================================================================
+
+
+class ElementwiseSgnModule(torch.nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([3, 4], torch.float32, True),
+    ])
+    def forward(self, a):
+        return torch.ops.aten.sgn(a)
+
+
+@register_test_case(module_factory=lambda: ElementwiseSgnModule())
+def ElementwiseSgnModule_basic(module, tu: TestUtils):
     module.forward(tu.rand(3, 4))