[Stablehlo] support aten.isfinite (#3850)

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

@@ -4921,6 +4921,29 @@ def Torch_AtenFakeQuantizePerChannelAffineCachemaskOp : Torch_Op<"aten.fake_quan
   }];
 }
 
+def Torch_AtenIsfiniteOp : Torch_Op<"aten.isfinite", [
+    AllowsTypeRefinement,
+    HasValueSemantics,
+    ReadOnly
+  ]> {
+  let summary = "Generated op for `aten::isfinite : (Tensor) -> (Tensor)`";
+  let arguments = (ins
+    AnyTorchTensorType:$self
+  );
+  let results = (outs
+    AnyTorchOptionalTensorType:$result
+  );
+  let hasCustomAssemblyFormat = 1;
+  let extraClassDefinition = [{
+    ParseResult AtenIsfiniteOp::parse(OpAsmParser &parser, OperationState &result) {
+      return parseDefaultTorchOp(parser, result, 1, 1);
+    }
+    void AtenIsfiniteOp::print(OpAsmPrinter &printer) {
+      printDefaultTorchOp(printer, *this, 1, 1);
+    }
+  }];
+}
+
 def Torch_AtenMaximumOp : Torch_Op<"aten.maximum", [
     AllowsTypeRefinement,
     HasValueSemantics,

lib/Conversion/TorchToStablehlo/Basic.cpp

@@ -2075,6 +2075,30 @@ LogicalResult ConvertAtenOp<AtenTrilOp>::matchAndRewrite(
   return success();
 }
 
+template <>
+LogicalResult ConvertAtenOp<AtenIsfiniteOp>::matchAndRewrite(
+    AtenIsfiniteOp op, OpAdaptor adaptor,
+    ConversionPatternRewriter &rewriter) const {
+  Value self = adaptor.getSelf();
+  auto selfTy = cast<RankedTensorType>(self.getType());
+  if (!selfTy)
+    return rewriter.notifyMatchFailure(
+        op, "Only Tensor types are currently supported");
+
+  auto outType =
+      dyn_cast<RankedTensorType>(getTypeConverter()->convertType(op.getType()));
+  Type outElemTy = outType.getElementType();
+  if (!outElemTy.isInteger(1)) {
+    return rewriter.notifyMatchFailure(
+        op, "Only i1 output element type is supported");
+  }
+
+  rewriter.replaceOpWithNewOp<stablehlo::IsFiniteOp>(op.getOperation(), outType,
+                                                     self);
+
+  return success();
+}
+
 void mlir::torch::torch_to_stablehlo::populateBasicOpPatternsAndLegality(
     TypeConverter &typeConverter, RewritePatternSet &patterns,
     ConversionTarget &target, const TorchToStablehloOptions &options) {
@@ -2248,6 +2272,7 @@ void mlir::torch::torch_to_stablehlo::populateBasicOpPatternsAndLegality(
   INSERT_ATENOP_PATTERN(AtenBitwiseRightShiftTensorOp);
 
   INSERT_ATENOP_PATTERN(AtenTrilOp);
+  INSERT_ATENOP_PATTERN(AtenIsfiniteOp);
 #undef INSERT_ATENOP_PATTERN
 
 #define INSERT_BINARY_BROADCAST_PATTERN(AtenOp, StablehloOp)                   \

lib/Dialect/Torch/Transforms/AbstractInterpLibrary.cpp

@@ -6495,6 +6495,9 @@ 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.isfinite\"(%arg0: !torch.list<int>) -> !torch.list<int> {\n"
+"    return %arg0 : !torch.list<int>\n"
+"  }\n"
 "  func.func @\"__torch_mlir_shape_fn.aten.cosine_similarity\"(%arg0: !torch.list<int>, %arg1: !torch.list<int>, %arg2: !torch.int, %arg3: !torch.float) -> !torch.list<int> {\n"
 "    %none = torch.constant.none\n"
 "    %int1 = torch.constant.int 1\n"
@@ -11170,6 +11173,10 @@ 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.isfinite\"(%arg0: !torch.tuple<int, int>) -> !torch.int {\n"
+"    %int11 = torch.constant.int 11\n"
+"    return %int11 : !torch.int\n"
+"  }\n"
 "  func.func @\"__torch_mlir_dtype_fn.aten.rad2deg\"(%arg0: !torch.tuple<int, int>) -> !torch.int {\n"
 "    %none = torch.constant.none\n"
 "    %str = torch.constant.str \"AssertionError: \"\n"

projects/pt1/e2e_testing/xfail_sets.py

@@ -533,6 +533,7 @@
     "IndexPutImpl2DNoneIndexStaticModule_basic",
     "IndexPutImpl3DFloatNonAccumulateModule_basic",
     "IndexPutImplIndexWithNoneModule_basic",
+    "IsInfiniteModule_basic",
     "InterpolateDynamicModule_sizes_nearest",
     "IouOfModule_basic",
     "MeshgridIndexingIJ_basic",

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

@@ -222,6 +222,9 @@ def aten〇exp2〡shape(self: List[int]) -> List[int]:
 def aten〇expm1〡shape(self: List[int]) -> List[int]:
     return upstream_shape_functions.unary(self)
 
+def aten〇isfinite〡shape(self: List[int]) -> List[int]:
+    return self
+
 def aten〇cosine_similarity〡shape(x1: List[int], x2: List[int], dim: int = 1, eps: float = 1e-08) -> List[int]:
     broadcast = upstream_shape_functions.broadcast(x1, x2)
     return broadcast[:dim] + broadcast[dim + 1:]
@@ -2538,6 +2541,9 @@ def aten〇expm1〡dtype(self_rank_dtype: Tuple[int, int]) -> int:
     self_rank, self_dtype = self_rank_dtype
     return _get_dtype_of_floating_point_op(self_dtype)
 
+def aten〇isfinite〡dtype(self_rank_dtype: Tuple[int, int]) -> int:
+    return torch.bool
+
 @check_dtype_function(_check_tensors_with_the_same_dtype(num_of_tensors=1, error_types={torch.complex64, torch.complex128}))
 def aten〇rad2deg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

@@ -483,6 +483,7 @@ def emit_with_mutating_variants(key, **kwargs):
     emit(
         "aten::fake_quantize_per_channel_affine_cachemask : (Tensor, Tensor, Tensor, int, int, int) -> (Tensor, Tensor)"
     )
+    emit("aten::isfinite : (Tensor) -> (Tensor)")
     emit("aten::maximum : (Tensor, Tensor) -> (Tensor)")
     emit("aten::minimum : (Tensor, Tensor) -> (Tensor)")
     emit("aten::fmax : (Tensor, Tensor) -> (Tensor)")

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

@@ -4226,6 +4226,29 @@ def PowIntFloatModule_basic(module, tu: TestUtils):
 # ==============================================================================
 
 
+class IsInfiniteModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args(
+        [
+            None,
+            ([-1], torch.float32, True),
+        ]
+    )
+    def forward(self, x):
+        return torch.ops.aten.isfinite(x)
+
+
+@register_test_case(module_factory=lambda: IsInfiniteModule())
+def IsInfiniteModule_basic(module, tu: TestUtils):
+    module.forward(torch.tensor([-torch.inf, torch.inf, torch.nan, -2.3, 0.0, 1.5]))
+
+
+# ==============================================================================
+
+
 class BaddbmmDynamicModule(torch.nn.Module):
     def __init__(self):
         super().__init__()