[Torch] emit aten.log_sigmoid and decompose it to log(sigmoid) (#3246)

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

@@ -4949,6 +4949,29 @@ def Torch_AtenGluOp : Torch_Op<"aten.glu", [
   }];
 }
 
+def Torch_AtenLogSigmoidOp : Torch_Op<"aten.log_sigmoid", [
+    AllowsTypeRefinement,
+    HasValueSemantics,
+    ReadOnly
+  ]> {
+  let summary = "Generated op for `aten::log_sigmoid : (Tensor) -> (Tensor)`";
+  let arguments = (ins
+    AnyTorchTensorType:$self
+  );
+  let results = (outs
+    AnyTorchOptionalTensorType:$result
+  );
+  let hasCustomAssemblyFormat = 1;
+  let extraClassDefinition = [{
+    ParseResult AtenLogSigmoidOp::parse(OpAsmParser &parser, OperationState &result) {
+      return parseDefaultTorchOp(parser, result, 1, 1);
+    }
+    void AtenLogSigmoidOp::print(OpAsmPrinter &printer) {
+      printDefaultTorchOp(printer, *this, 1, 1);
+    }
+  }];
+}
+
 def Torch_AtenUnbindCopyIntOp : Torch_Op<"aten.unbind_copy.int", [
     AllowsTypeRefinement,
     HasValueSemantics,

lib/Dialect/Torch/Transforms/AbstractInterpLibrary.cpp

@@ -6510,6 +6510,10 @@ 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.log_sigmoid\"(%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.mish\"(%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"
@@ -9771,6 +9775,21 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    %1 = call @__torch__._get_dtype_of_floating_point_op(%0#1) : (!torch.int) -> !torch.int\n"
 "    return %1 : !torch.int\n"
 "  }\n"
+"  func.func @\"__torch_mlir_dtype_fn.aten.log_sigmoid\"(%arg0: !torch.tuple<int, int>) -> !torch.int {\n"
+"    %none = torch.constant.none\n"
+"    %str = torch.constant.str \"AssertionError: \"\n"
+"    %int11 = torch.constant.int 11\n"
+"    %0:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
+"    %1 = torch.aten.eq.int %0#1, %int11 : !torch.int, !torch.int -> !torch.bool\n"
+"    %2 = torch.aten.__not__ %1 : !torch.bool -> !torch.bool\n"
+"    torch.prim.If %2 -> () {\n"
+"      torch.prim.If.yield\n"
+"    } else {\n"
+"      torch.prim.RaiseException %str, %none : !torch.str, !torch.none\n"
+"      torch.prim.If.yield\n"
+"    }\n"
+"    return %0#1 : !torch.int\n"
+"  }\n"
 "  func.func @\"__torch_mlir_dtype_fn.aten.logit\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.optional<float>) -> !torch.int {\n"
 "    %0:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
 "    %1 = call @__torch__._get_dtype_of_floating_point_op(%0#1) : (!torch.int) -> !torch.int\n"

lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp

@@ -1900,6 +1900,20 @@ class DecomposeAten_LogSoftmaxOp : public OpRewritePattern<Aten_LogSoftmaxOp> {
 };
 } // namespace
 
+namespace {
+class DecomposeAtenLogSigmoidOp : public OpRewritePattern<AtenLogSigmoidOp> {
+public:
+  using OpRewritePattern<AtenLogSigmoidOp>::OpRewritePattern;
+  LogicalResult matchAndRewrite(AtenLogSigmoidOp op,
+                                PatternRewriter &rewriter) const override {
+    Value sigmoid =
+        rewriter.create<AtenSigmoidOp>(op.getLoc(), op.getType(), op.getSelf());
+    rewriter.replaceOpWithNewOp<AtenLogOp>(op, op.getType(), sigmoid);
+    return success();
+  }
+};
+} // namespace
+
 // Decompose aten.matmul into: aten.mm and aten.bmm according to ranks.
 namespace {
 class DecomposeAtenMatmulOp : public OpRewritePattern<AtenMatmulOp> {
@@ -7606,6 +7620,7 @@ class DecomposeComplexOpsPass
     addPatternIfTargetOpIsIllegal<DecomposeAten_SoftmaxOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAten_LogSoftmaxOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenLogSoftmaxIntOp>(patterns);
+    addPatternIfTargetOpIsIllegal<DecomposeAtenLogSigmoidOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenEmptyLikeOp>(patterns);
     addPatternIfTargetOpIsIllegal<
         DecomposeConstantTensorAllocLikeOp<AtenOnesLikeOp, 1>>(patterns);

lib/Dialect/Torch/Transforms/LowerToBackendContract.cpp

@@ -370,6 +370,7 @@ static void markDecomposedOpsAsIllegal(MLIRContext *context,
   target.addIllegalOp<Aten_SoftmaxOp>();
   target.addIllegalOp<Aten_LogSoftmaxOp>();
   target.addIllegalOp<AtenLogSoftmaxIntOp>();
+  target.addIllegalOp<AtenLogSigmoidOp>();
   target.addIllegalOp<AtenEmptyLikeOp>();
   target.addIllegalOp<AtenOnesLikeOp>();
   target.addIllegalOp<AtenZerosLikeOp>();

projects/pt1/e2e_testing/xfail_sets.py

@@ -1431,6 +1431,7 @@
     "ElementwiseSinhModule_basic",
     "ElementwiseTruncIntModule_basic",
     "ElementwiseTruncModule_basic",
+    "ElementwiseLogSigmoidModule_basic",
 }
 
 STABLEHLO_CRASHING_SET = {
@@ -1440,6 +1441,7 @@
 # Write the TOSA set as a "passing" set as it is very early in development
 # and very few tests work yet.
 TOSA_PASS_SET = {
+    "ElementwiseLogSigmoidModule_basic",
     "ElementwiseTruncModule_basic",
     "ElementwiseTruncIntModule_basic",
     "ElementwiseSgnModule_basic",
@@ -1935,6 +1937,7 @@
     # failed to legalize operation 'torch.operator'
     "ElementwisePreluModule_basic",
     "ElementwisePreluStaticModule_basic",
+    "ElementwiseLogSigmoidModule_basic",
     # Shape Related failures
     "PrimListUnpackNumMismatchModule_basic",
     "ReshapeExpandModule_basic",

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

@@ -251,6 +251,9 @@ def aten〇trunc〡shape(self: List[int]) -> List[int]:
 def aten〇log〡shape(self: List[int]) -> List[int]:
     return upstream_shape_functions.unary(self)
 
+def aten〇log_sigmoid〡shape(self: List[int]) -> List[int]:
+    return upstream_shape_functions.unary(self)
+
 def aten〇mish〡shape(self: List[int]) -> List[int]:
     return upstream_shape_functions.unary(self)
 
@@ -2083,6 +2086,12 @@ def aten〇log1p〡dtype(self_rank_dtype: Tuple[int, int]) -> int:
     self_rank, self_dtype = self_rank_dtype
     return _get_dtype_of_floating_point_op(self_dtype)
 
+@check_dtype_function(_check_tensors_with_the_same_dtype(num_of_tensors=1, error_types={torch.bool}))
+def aten〇log_sigmoid〡dtype(self_rank_dtype: Tuple[int, int]) -> int:
+    self_rank, self_dtype = self_rank_dtype
+    assert not self_dtype == torch.bool
+    return self_dtype
+
 @check_dtype_function(_check_tensors_with_the_same_dtype(num_of_tensors=1))
 def aten〇logit〡dtype(self_rank_dtype: Tuple[int, int], eps: Optional[float] = None) -> int:
     self_rank, self_dtype = self_rank_dtype

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

@@ -478,6 +478,7 @@ def emit_with_mutating_variants(key, **kwargs):
     emit("aten::view_as_real : (Tensor) -> (Tensor)")
     emit("aten::isclose : (Tensor, Tensor, float, float, bool) -> (Tensor)")
     emit("aten::glu : (Tensor, int) -> (Tensor)")
+    emit("aten::log_sigmoid : (Tensor) -> (Tensor)")
 
     # Ops with dynamic number of outputs
     emit("aten::unbind_copy.int : (Tensor, int) -> (Tensor[])")

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

@@ -2109,6 +2109,30 @@ def ElementwiseLogitModule_basic(module, tu: TestUtils):
 # ==============================================================================
 
 
+class ElementwiseLogSigmoidModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.m = torch.nn.LogSigmoid()
+
+    @export
+    @annotate_args(
+        [
+            None,
+            ([-1, -1], torch.float32, True),
+        ]
+    )
+    def forward(self, a):
+        return self.m(a)
+
+
+@register_test_case(module_factory=lambda: ElementwiseLogSigmoidModule())
+def ElementwiseLogSigmoidModule_basic(module, tu: TestUtils):
+    module.forward(tu.rand(3, 4))
+
+
+# ==============================================================================
+
+
 class ElementwiseErfModule(torch.nn.Module):
     def __init__(self):
         super().__init__()