fix: Ensure proper type inheritance in aten::masked_fill

core/conversion/converters/impl/select.cpp

@@ -662,8 +662,15 @@ auto select_registrations TORCHTRT_UNUSED =
                auto self = args[0].ITensorOrFreeze(ctx);
                auto mask = args[1].ITensorOrFreeze(ctx);
                mask = addPadding(ctx, n, mask, self->getDimensions().nbDims, false, true);
-               auto val = args[2].unwrapToScalar().to<float>();
-               auto val_t = tensor_to_const(ctx, torch::full(util::toVec(self->getDimensions()), val));
+               auto val = args[2].unwrapToScalar();
+
+               // Tensor type to use for initializing constant tensor used in Select
+               // value should inherit its type from self
+               auto val_t_dtype = util::TRTDataTypeToScalarType(self->getType());
+
+               // Initialize contant tensor for fill with the inherited data type
+               auto val_t = tensor_to_const(
+                   ctx, torch::full(util::toVec(self->getDimensions()), val, {torch::dtype(val_t_dtype)}));
 
                TORCHTRT_CHECK(
                    util::broadcastable(self->getDimensions(), mask->getDimensions(), /*multidirectional=*/false),

tests/core/conversion/converters/test_select.cpp

@@ -804,6 +804,62 @@ TEST(Converters, ATenMaskedFillZerosConvertsCorrectly) {
       torch_tensorrt::tests::util::almostEqual(jit_results[0], trt_results[0].reshape_as(jit_results[0]), 2e-6));
 }
 
+TEST(Converters, ATenMaskedFillMixedTypesFloatIntConvertsCorrectly) {
+  const auto graph = R"IR(
+    graph(%x.1 : Tensor, %x.2 : Tensor):
+      %val : float = prim::Constant[value=4.0]()
+      %out : Tensor = aten::masked_fill(%x.1, %x.2, %val)
+      return (%out))IR";
+
+  auto g = std::make_shared<torch::jit::Graph>();
+
+  torch::jit::parseIR(graph, &*g);
+
+  // Input is a float tensor, filled with an int --> expecting float tensor out
+  auto in1 = at::rand({2, 3, 5, 7}, {at::kCUDA}).to(torch::kFloat32);
+  auto in2 = (2 * at::rand({2, 3, 5, 7}, {at::kCUDA})).to(torch::kBool);
+
+  auto params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto jit_results = torch_tensorrt::tests::util::RunGraph(g, params, {in1, in2});
+
+  params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto trt_results = torch_tensorrt::tests::util::RunGraphEngine(g, params, {in1, in2});
+
+  ASSERT_TRUE(
+      torch_tensorrt::tests::util::almostEqual(jit_results[0], trt_results[0].reshape_as(jit_results[0]), 2e-6));
+
+  // Ensure data types match in outputs
+  ASSERT_TRUE(jit_results[0].dtype() == trt_results[0].dtype());
+}
+
+TEST(Converters, ATenMaskedFillMixedTypesIntFloatConvertsCorrectly) {
+  const auto graph = R"IR(
+    graph(%x.1 : Tensor, %x.2 : Tensor):
+      %val : int = prim::Constant[value=4]()
+      %out : Tensor = aten::masked_fill(%x.1, %x.2, %val)
+      return (%out))IR";
+
+  auto g = std::make_shared<torch::jit::Graph>();
+
+  torch::jit::parseIR(graph, &*g);
+
+  // Input is an integer tensor, filled with a float --> expecting integer tensor out
+  auto in1 = at::rand({1, 3, 5, 7}, {at::kCUDA}).to(torch::kInt32);
+  auto in2 = (2 * at::rand({1, 3, 5, 7}, {at::kCUDA})).to(torch::kBool);
+
+  auto params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto jit_results = torch_tensorrt::tests::util::RunGraph(g, params, {in1, in2});
+
+  params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto trt_results = torch_tensorrt::tests::util::RunGraphEngine(g, params, {in1, in2});
+
+  ASSERT_TRUE(
+      torch_tensorrt::tests::util::almostEqual(jit_results[0], trt_results[0].reshape_as(jit_results[0]), 2e-6));
+
+  // Ensure data types match in outputs
+  ASSERT_TRUE(jit_results[0].dtype() == trt_results[0].dtype());
+}
+
 TEST(Converters, ATenIndexTensorOneIndiceConvertsCorrectly) {
   const auto graph = R"IR(
       graph(%x.1 : Tensor,