[Torch] emit aten.celu and decompose it (#3247)

CELU(x)=max(0,x)+min(0,α∗(exp(x/α)−1))

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

@@ -4810,6 +4810,53 @@ def Torch_AtenPreluOp : Torch_Op<"aten.prelu", [
   }];
 }
 
+def Torch_AtenCeluOp : Torch_Op<"aten.celu", [
+    AllowsTypeRefinement,
+    HasValueSemantics,
+    ReadOnly
+  ]> {
+  let summary = "Generated op for `aten::celu : (Tensor, Scalar) -> (Tensor)`";
+  let arguments = (ins
+    AnyTorchTensorType:$self,
+    AnyTorchScalarType:$alpha
+  );
+  let results = (outs
+    AnyTorchOptionalTensorType:$result
+  );
+  let hasCustomAssemblyFormat = 1;
+  let extraClassDefinition = [{
+    ParseResult AtenCeluOp::parse(OpAsmParser &parser, OperationState &result) {
+      return parseDefaultTorchOp(parser, result, 2, 1);
+    }
+    void AtenCeluOp::print(OpAsmPrinter &printer) {
+      printDefaultTorchOp(printer, *this, 2, 1);
+    }
+  }];
+}
+
+def Torch_AtenCelu_Op : Torch_Op<"aten.celu_", [
+    IsTrailingUnderscoreInplaceVariant,
+    AllowsTypeRefinement
+  ]> {
+  let summary = "Generated op for `aten::celu_ : (Tensor, Scalar) -> (Tensor)`";
+  let arguments = (ins
+    Torch_NonValueTensorType:$self,
+    AnyTorchScalarType:$alpha
+  );
+  let results = (outs
+    AnyTorchOptionalNonValueTensorType:$result
+  );
+  let hasCustomAssemblyFormat = 1;
+  let extraClassDefinition = [{
+    ParseResult AtenCelu_Op::parse(OpAsmParser &parser, OperationState &result) {
+      return parseDefaultTorchOp(parser, result, 2, 1);
+    }
+    void AtenCelu_Op::print(OpAsmPrinter &printer) {
+      printDefaultTorchOp(printer, *this, 2, 1);
+    }
+  }];
+}
+
 def Torch_AtenRealOp : Torch_Op<"aten.real", [
     AllowsTypeRefinement,
     ReadOnly

lib/Dialect/Torch/Transforms/AbstractInterpLibrary.cpp

@@ -6998,6 +6998,10 @@ 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.celu\"(%arg0: !torch.list<int>, %arg1: !torch.float) -> !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.selu\"(%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"
@@ -10480,6 +10484,10 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    }\n"
 "    return %0#1 : !torch.int\n"
 "  }\n"
+"  func.func @\"__torch_mlir_dtype_fn.aten.celu\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.number) -> !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.relu6\"(%arg0: !torch.tuple<int, int>) -> !torch.int {\n"
 "    %none = torch.constant.none\n"
 "    %str = torch.constant.str \"AssertionError: \"\n"

lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp

@@ -2415,6 +2415,50 @@ public:
 
 } // namespace
 
+// CELU(x)=max(0,x)+min(0,alpha∗(exp(x/alpha)−1))
+namespace {
+class DecomposeAtenCeluOp : public OpRewritePattern<AtenCeluOp> {
+public:
+  using OpRewritePattern::OpRewritePattern;
+  LogicalResult matchAndRewrite(AtenCeluOp op,
+                                PatternRewriter &rewriter) const override {
+    Location loc = op.getLoc();
+    Value input = op.getSelf();
+    Value alpha = op.getAlpha();
+    auto resType = cast<BaseTensorType>(op.getType());
+    if (!resType.hasDtype()) {
+      return rewriter.notifyMatchFailure(op, "result should have dtype");
+    }
+
+    Value constantZero =
+        rewriter.create<ConstantIntOp>(loc, rewriter.getI64IntegerAttr(0));
+    Value constantOne =
+        rewriter.create<ConstantFloatOp>(loc, rewriter.getF64FloatAttr(1.0));
+
+    // positiveOutput = max(0,x)
+    Value zeroTensor = createRank0Tensor(rewriter, loc, resType, constantZero);
+    Value positiveOutput =
+        rewriter.create<AtenMaximumOp>(loc, resType, zeroTensor, input);
+
+    // negativeOutput = min(0,alpha∗(exp(x/alpha)−1))
+    Value scaledInput =
+        rewriter.create<AtenDivScalarOp>(loc, resType, input, alpha);
+    Value expX = rewriter.create<AtenExpOp>(loc, resType, scaledInput);
+    Value expXM1 = rewriter.create<AtenSubScalarOp>(loc, resType, expX,
+                                                    constantOne, constantOne);
+    Value scaledExpXM1 =
+        rewriter.create<AtenMulScalarOp>(loc, resType, expXM1, alpha);
+    Value negativeOutput =
+        rewriter.create<AtenMinimumOp>(loc, resType, zeroTensor, scaledExpXM1);
+    Value celuOutput = rewriter.create<AtenAddTensorOp>(
+        loc, resType, positiveOutput, negativeOutput, constantOne);
+
+    rewriter.replaceOp(op, celuOutput);
+    return success();
+  }
+};
+} // namespace
+
 namespace {
 class DecomposeAtenLerpScalarOp : public OpRewritePattern<AtenLerpScalarOp> {
 public:
@@ -7705,6 +7749,7 @@ public:
     addPatternIfTargetOpIsIllegal<DecomposeAtenHardsigmoidOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenRelu6Op>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenPreluOp>(patterns);
+    addPatternIfTargetOpIsIllegal<DecomposeAtenCeluOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenEinsumOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenTraceOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenHardswishOp>(patterns);

lib/Dialect/Torch/Transforms/LowerToBackendContract.cpp

@@ -474,6 +474,7 @@ static void markDecomposedOpsAsIllegal(MLIRContext *context,
   target.addIllegalOp<Aten_UnsafeIndexPutHackedTwinOp>();
   target.addIllegalOp<AtenPadOp>();
   target.addIllegalOp<AtenPreluOp>();
+  target.addIllegalOp<AtenCeluOp>();
   target.addIllegalOp<AtenToDtypeLayoutOp>();
   target.addIllegalOp<AtenToDeviceOp>();
   target.addIllegalOp<AtenToPrimDeviceOp>();

projects/pt1/e2e_testing/xfail_sets.py

@@ -951,6 +951,7 @@ STABLEHLO_PASS_SET = {
     "ElementwiseBitwiseRightShiftInt64Module_basic",
     "ElementwiseBitwiseRightShiftInt8Module_basic",
     "ElementwiseCeilModule_basic",
+    "ElementwiseCeluStaticModule_basic",
     "ElementwiseClampMaxModule_basic",
     "ElementwiseClampMinModule_basic",
     "ElementwiseClampMinTensorFloatModule_basic",
@@ -1571,6 +1572,8 @@ TOSA_PASS_SET = {
     "ElementwiseBitwiseXorModule_basic",
     "ElementwiseBitwiseXorStaticShapeModule_basic",
     "ElementwiseCeilModule_basic",
+    "ElementwiseCeluModule_basic",
+    "ElementwiseCeluStaticModule_basic",
     "ElementwiseClampMaxModule_basic",
     "ElementwiseClampMinModule_basic",
     "ElementwiseClampModule_basic",

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

@@ -526,6 +526,9 @@ def aten〇elu〡shape(self: List[int], alpha: float = 1, scale: float = 1, inpu
 def aten〇prelu〡shape(self: List[int], weight: List[int]) -> List[int]:
     return upstream_shape_functions.unary(self)
 
+def aten〇celu〡shape(self: List[int], alpha: float = 1.) -> List[int]:
+    return upstream_shape_functions.unary(self)
+
 def aten〇selu〡shape(self: List[int]) -> List[int]:
     return upstream_shape_functions.unary(self)
 
@@ -2652,6 +2655,11 @@ def aten〇prelu〡dtype(self_rank_dtype: Tuple[int, int], weight_rank_dtype: Tu
     assert self_dtype == weight_dtype
     return self_dtype
 
+@check_dtype_function(_check_tensors_with_the_same_dtype(num_of_tensors=1, alpha=1.))
+def aten〇celu〡dtype(self_rank_dtype: Tuple[int, int], alpha: Union[int, float, complex] = 1.) -> int:
+    self_rank, self_dtype = self_rank_dtype
+    return self_dtype
+
 @check_dtype_function(_check_tensors_with_the_same_dtype(num_of_tensors=1, error_types={torch.bool}))
 def aten〇relu6〡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

@@ -472,6 +472,7 @@ def emit_ops(emitter_td: TextEmitter, registry: Registry):
     emit("aten::floor_divide : (Tensor, Tensor) -> (Tensor)")
     emit("aten::softplus : (Tensor, Scalar, Scalar) -> (Tensor)")
     emit("aten::prelu : (Tensor, Tensor) -> (Tensor)")
+    emit_with_mutating_variants("aten::celu : (Tensor, Scalar) -> (Tensor)")
     emit("aten::real : (Tensor) -> (Tensor)")
     emit("aten::imag : (Tensor) -> (Tensor)")
     emit("aten::view_as_complex : (Tensor) -> (Tensor)")

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

@@ -1016,6 +1016,52 @@ def ElementwisePreluStaticModule_basic(module, tu: TestUtils):
 # ==============================================================================
 
 
+class ElementwiseCeluModule(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.celu(x, 0.5)
+
+
+@register_test_case(module_factory=lambda: ElementwiseCeluModule())
+def ElementwiseCeluModule_basic(module, tu: TestUtils):
+    module.forward(tu.rand(5, 3, low=-1, high=1))
+
+
+# ==============================================================================
+
+
+class ElementwiseCeluStaticModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args(
+        [
+            None,
+            ([5, 3], torch.float32, True),
+        ]
+    )
+    def forward(self, x):
+        return torch.ops.aten.celu(x)
+
+
+@register_test_case(module_factory=lambda: ElementwiseCeluStaticModule())
+def ElementwiseCeluStaticModule_basic(module, tu: TestUtils):
+    module.forward(tu.rand(5, 3, low=-1, high=1))
+
+
+# ==============================================================================
+
+
 class ElementwiseGeluModule(torch.nn.Module):
     def __init__(self):
         super().__init__()