Add E2E support for aten.is_floating_point

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

@@ -4149,6 +4149,29 @@ def Torch_AtenBoolTensorOp : Torch_Op<"aten.Bool.Tensor", [
   }];
 }
 
+def Torch_AtenIsFloatingPointOp : Torch_Op<"aten.is_floating_point", [
+    AllowsTypeRefinement,
+    HasValueSemantics,
+    ReadOnly
+  ]> {
+  let summary = "Generated op for `aten::is_floating_point : (Tensor) -> (bool)`";
+  let arguments = (ins
+    AnyTorchTensorType:$self
+  );
+  let results = (outs
+    Torch_BoolType:$result
+  );
+  let hasCustomAssemblyFormat = 1;
+  let extraClassDefinition = [{
+    ParseResult AtenIsFloatingPointOp::parse(OpAsmParser &parser, OperationState &result) {
+      return parseDefaultTorchOp(parser, result, 1, 1);
+    }
+    void AtenIsFloatingPointOp::print(OpAsmPrinter &printer) {
+      printDefaultTorchOp(printer, *this, 1, 1);
+    }
+  }];
+}
+
 def Torch_AtenOnesOp : Torch_Op<"aten.ones", [
     AllowsTypeRefinement,
     HasValueSemantics,

lib/Conversion/TorchToStd/TorchToStd.cpp

@@ -50,6 +50,24 @@ public:
 };
 } // namespace
 
+namespace {
+class ConvertAtenIsFloatingPointOp
+    : public OpConversionPattern<AtenIsFloatingPointOp> {
+public:
+  using OpConversionPattern::OpConversionPattern;
+  LogicalResult
+  matchAndRewrite(AtenIsFloatingPointOp op, OpAdaptor adaptor,
+                  ConversionPatternRewriter &rewriter) const override {
+    auto tensorType = op.self().getType().cast<BaseTensorType>();
+    bool result =
+        tensorType.hasDtype() && tensorType.getDtype().isa<mlir::FloatType>();
+    rewriter.replaceOpWithNewOp<arith::ConstantOp>(
+        op, BoolAttr::get(getContext(), result));
+    return success();
+  }
+};
+} // namespace
+
 namespace {
 class ConvertRuntimeAssertOp : public OpConversionPattern<RuntimeAssertOp> {
 public:
@@ -301,6 +319,8 @@ public:
     RewritePatternSet patterns(context);
     target.addIllegalOp<AtenDimOp>();
     patterns.add<ConvertAtenDimOp>(typeConverter, context);
+    target.addIllegalOp<AtenIsFloatingPointOp>();
+    patterns.add<ConvertAtenIsFloatingPointOp>(typeConverter, context);
     target.addIllegalOp<RuntimeAssertOp>();
     patterns.add<ConvertRuntimeAssertOp>(typeConverter, context);
     target.addIllegalOp<AtenNeIntOp, AtenEqIntOp, AtenGtIntOp>();

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

@@ -392,6 +392,7 @@ def emit_ops(emitter_td: TextEmitter, registry: Registry):
     emit("aten::dim : (Tensor) -> (int)", has_folder=True)
     emit("aten::size : (Tensor) -> (int[])", has_canonicalizer=True)
     emit("aten::Bool.Tensor : (Tensor) -> (bool)")
+    emit("aten::is_floating_point : (Tensor) -> (bool)")
     emit("aten::ones : (int[], int?, int?, Device?, bool?) -> (Tensor)")
     emit("aten::new_ones : (Tensor, int[], int?, int?, Device?, bool?) -> (Tensor)")
     emit("aten::zeros : (int[], int?, int?, Device?, bool?) -> (Tensor)")

python/torch_mlir_e2e_test/test_suite/basic.py

@@ -64,6 +64,50 @@ def BmmModule_basic(module, tu: TestUtils):
 # ==============================================================================
 
 
+class IsFloatingPointInt(torch.nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1, -1], torch.int32, True),
+    ])
+    def forward(self, x):
+        return torch.is_floating_point(x)
+
+
+@register_test_case(module_factory=lambda: IsFloatingPointInt())
+def IsFloatingPointInt_False(module, tu: TestUtils):
+    module.forward(torch.randint(100, (3, 3)))
+
+
+# ==============================================================================
+
+
+class IsFloatingPointFloat(torch.nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1], torch.float32, True),
+    ])
+    def forward(self, x):
+        return torch.is_floating_point(x)
+
+
+@register_test_case(module_factory=lambda: IsFloatingPointFloat())
+def IsFloatingPointFloat_True(module, tu: TestUtils):
+    module.forward(tu.rand(3))
+        
+
+# ==============================================================================
+
+
 # A subgraph with multiple mm ops.
 class MmDagModule(torch.nn.Module):