[Build] Refactor backends to adhere to stricter build warnings

source/bazel/cc.bzl

@@ -17,6 +17,12 @@ _warnings = [
     # We're okay with these for now, but should refactor to remove if we can
     "-Wno-global-constructors",
     "-Wno-exit-time-destructors",
+
+    # It's okay if we use `default` in switch statements
+    "-Wno-switch-enum",
+
+    # Flexible array members
+    "-Wno-c99-extensions",
 ]
 
 _test_warnings = _warnings + [

source/neuropod/backends/BUILD

@@ -13,8 +13,9 @@
 # limitations under the License.
 
 load("@bazel_tools//tools/build_defs/pkg:pkg.bzl", "pkg_tar")
+load("//bazel:cc.bzl", "neuropod_cc_library")
 
-cc_library(
+neuropod_cc_library(
     name = "neuropod_backend",
     srcs = [
         "neuropod_backend.cc",

source/neuropod/backends/neuropod_backend.cc

@@ -129,7 +129,7 @@ void validate_tensors_against_specs(const NeuropodValueMap &       tensors,
         }
 
         // Validate the shape
-        for (int i = 0; i < spec.dims.size(); i++)
+        for (size_t i = 0; i < spec.dims.size(); i++)
         {
             auto dim      = tensor->get_dims()[i];
             auto expected = spec.dims[i];
@@ -318,7 +318,7 @@ std::unique_ptr<NeuropodValueMap> NeuropodBackend::infer_internal(const Neuropod
     return out;
 }
 
-std::unique_ptr<NeuropodValueMap> NeuropodBackend::infer_internal(const NeuropodValueMap &inputs)
+std::unique_ptr<NeuropodValueMap> NeuropodBackend::infer_internal(const NeuropodValueMap & /*unused*/)
 {
     NEUROPOD_ERROR("Backend implementations must provide a `infer_internal` implementation");
 }

source/neuropod/backends/python_bridge/BUILD

@@ -13,8 +13,9 @@
 # limitations under the License.
 
 load("@bazel_tools//tools/build_defs/pkg:pkg.bzl", "pkg_tar")
+load("//bazel:cc.bzl", "neuropod_cc_binary", "neuropod_cc_library")
 
-cc_binary(
+neuropod_cc_binary(
     name = "libneuropod_pythonbridge_backend.so",
     srcs = [
         "//neuropod:libneuropod.so",
@@ -33,7 +34,7 @@ cc_binary(
     ],
 )
 
-cc_library(
+neuropod_cc_library(
     name = "python_bridge",
     srcs = [
         "python_bridge.cc",

source/neuropod/backends/tensorflow/BUILD

@@ -14,8 +14,9 @@
 
 load("@bazel_tools//tools/build_defs/pkg:pkg.bzl", "pkg_tar")
 load("//bazel:copy_libs.bzl", "copy_libs")
+load("//bazel:cc.bzl", "neuropod_cc_binary", "neuropod_cc_library")
 
-cc_binary(
+neuropod_cc_binary(
     name = "libneuropod_tensorflow_backend.so",
     srcs = [
         "//neuropod:libneuropod.so",
@@ -37,7 +38,7 @@ cc_binary(
     ],
 )
 
-cc_library(
+neuropod_cc_library(
     name = "tensorflow_backend",
     srcs = [
         "tf_backend.cc",

source/neuropod/backends/tensorflow/tf_backend.cc

@@ -186,12 +186,12 @@ void TensorflowNeuropodBackend::load_model_internal()
 
     // Create a buffer of the right size
     graph_stream->seekg(0, graph_stream->end);
-    std::streamsize   graph_length = graph_stream->tellg();
+    auto              graph_length = static_cast<size_t>(graph_stream->tellg());
     std::vector<char> buffer(graph_length);
 
     // Read into the buffer
     graph_stream->seekg(0, graph_stream->beg);
-    graph_stream->read(buffer.data(), graph_length);
+    graph_stream->read(buffer.data(), static_cast<std::streamsize>(graph_length));
     if (graph_stream->fail())
     {
         NEUROPOD_ERROR("Error reading TensorFlow GraphDef for neuropod {}", neuropod_path_);

source/neuropod/backends/tensorflow/tf_tensor.cc

@@ -81,7 +81,7 @@ class NeuropodTensorBuffer : public tensorflow::TensorBuffer
 
     void FillAllocationDescription(tensorflow::AllocationDescription *proto) const override
     {
-        tensorflow::int64 rb = size();
+        auto rb = static_cast<tensorflow::int64>(size());
         proto->set_requested_bytes(rb);
         proto->set_allocator_name(tensorflow::cpu_allocator()->Name());
     }
@@ -116,11 +116,11 @@ tensorflow::TensorShape get_tf_shape(const std::vector<int64_t> &dims)
 // Convert shapes
 std::vector<int64_t> get_dims(const tensorflow::Tensor &tensor)
 {
-    int                  num_dims = tensor.dims();
+    auto                 num_dims = static_cast<size_t>(tensor.dims());
     std::vector<int64_t> shape(num_dims);
-    for (int i = 0; i < num_dims; i++)
+    for (size_t i = 0; i < num_dims; i++)
     {
-        shape[i] = tensor.dim_size(i);
+        shape[i] = tensor.dim_size(static_cast<int>(i));
     }
 
     return shape;

source/neuropod/backends/tensorflow/tf_tensor.hh

@@ -160,26 +160,26 @@ public:
     void copy_from(const std::vector<std::string> &data)
     {
         auto flat = tensor_.flat<StringType>();
-        if (data.size() != flat.size())
+        if (data.size() != static_cast<size_t>(flat.size()))
         {
             NEUROPOD_ERROR("Supplied vector size ({}) does not match size of tensor ({})", data.size(), flat.size());
         }
 
-        for (int i = 0; i < data.size(); i++)
+        for (size_t i = 0; i < data.size(); i++)
         {
-            flat(i) = data[i];
+            flat(static_cast<long>(i)) = data[i];
         }
     }
 
     tensorflow::Tensor &get_native_data() { return tensor_; }
 
 protected:
-    std::string get(size_t index) const { return tensor_.flat<StringType>()(index); }
+    std::string get(size_t index) const { return tensor_.flat<StringType>()(static_cast<long>(index)); }
 
     void set(size_t index, const std::string &value)
     {
-        auto flat   = tensor_.flat<StringType>();
-        flat(index) = value;
+        auto flat                      = tensor_.flat<StringType>();
+        flat(static_cast<long>(index)) = value;
     }
 };
 

source/neuropod/backends/torchscript/BUILD

@@ -14,8 +14,9 @@
 
 load("@bazel_tools//tools/build_defs/pkg:pkg.bzl", "pkg_tar")
 load("//bazel:copy_libs.bzl", "copy_libs")
+load("//bazel:cc.bzl", "neuropod_cc_binary", "neuropod_cc_library")
 
-cc_binary(
+neuropod_cc_binary(
     name = "libneuropod_torchscript_backend.so",
     srcs = [
         "//neuropod:libneuropod.so",
@@ -40,7 +41,7 @@ cc_binary(
     ],
 )
 
-cc_library(
+neuropod_cc_library(
     name = "torch_backend",
     srcs = [
         "torch_backend.cc",

source/neuropod/backends/torchscript/torch_backend.cc

@@ -35,15 +35,15 @@ namespace
 {
 
 #if CAFFE2_NIGHTLY_VERSION >= 20200115
-#define MAKE_DICT(name, type) torch::Dict<std::string, type> name;
+#define MAKE_DICT(name, type) torch::Dict<std::string, type> name
 #elif CAFFE2_NIGHTLY_VERSION >= 20191010
-#define MAKE_DICT(name, type) c10::impl::GenericDict name(c10::AnyType::get(), c10::AnyType::get());
+#define MAKE_DICT(name, type) c10::impl::GenericDict name(c10::AnyType::get(), c10::AnyType::get())
 #elif CAFFE2_NIGHTLY_VERSION >= 20190717
-#define MAKE_DICT(name, type) c10::impl::GenericDict name((c10::impl::deprecatedUntypedDict()));
+#define MAKE_DICT(name, type) c10::impl::GenericDict name((c10::impl::deprecatedUntypedDict()))
 #elif CAFFE2_NIGHTLY_VERSION >= 20190601
-#define MAKE_DICT(name, type) auto name = c10::make_dict<torch::jit::IValue, torch::jit::IValue>();
+#define MAKE_DICT(name, type) auto name = c10::make_dict<torch::jit::IValue, torch::jit::IValue>()
 #else
-#define MAKE_DICT(name, type) torch::ivalue::UnorderedMap name;
+#define MAKE_DICT(name, type) torch::ivalue::UnorderedMap name
 #endif
 
 #if CAFFE2_NIGHTLY_VERSION >= 20190717
@@ -55,11 +55,11 @@ namespace
 #if CAFFE2_NIGHTLY_VERSION >= 20190601
 #define KEY(elem) (elem.key())
 #define VALUE(elem) (elem.value())
-#define DICT_INSERT(dict, key, value) dict.insert(key, value);
+#define DICT_INSERT(dict, key, value) dict.insert(key, value)
 #else
 #define KEY(elem) (elem.first)
 #define VALUE(elem) (elem.second)
-#define DICT_INSERT(dict, key, value) dict[key] = value;
+#define DICT_INSERT(dict, key, value) dict[key] = value
 #endif
 
 std::shared_ptr<torch::jit::script::Module> load_model_from_path(std::istream &                  graph_stream,
@@ -133,8 +133,8 @@ void insert_value_in_output(NeuropodValueMap & output,
         auto tensor = value.toTensor().to(torch::kCPU);
 
         // Get the type and make a TorchNeuropodTensor
-        auto tensor_type     = get_neuropod_type_from_torch_type(tensor.scalar_type());
-        auto neuropod_tensor = make_tensor<TorchNeuropodTensor>(tensor_type, tensor);
+        auto neuropod_tensor_type = get_neuropod_type_from_torch_type(tensor.scalar_type());
+        auto neuropod_tensor      = make_tensor<TorchNeuropodTensor>(neuropod_tensor_type, tensor);
 
         // Add it to our output
         auto &to_set = output[name];
@@ -287,7 +287,7 @@ torch::Device TorchNeuropodBackend::get_torch_device(NeuropodDeviceType target_d
         return torch::kCPU;
     }
 
-    return torch::Device(torch::kCUDA, options_.visible_device);
+    return torch::Device(torch::kCUDA, static_cast<short>(options_.visible_device));
 }
 
 // Run inference
@@ -381,7 +381,7 @@ std::unique_ptr<NeuropodValueMap> TorchNeuropodBackend::infer_internal(const Neu
                     schema);
             }
 
-            torch_inputs.at(arg_index.value() - (has_class_type ? 1 : 0)) = input_data;
+            torch_inputs.at(static_cast<size_t>(arg_index.value() - (has_class_type ? 1 : 0))) = input_data;
         }
     }
 
@@ -432,7 +432,7 @@ std::unique_ptr<NeuropodValueMap> TorchNeuropodBackend::infer_internal(const Neu
         {
             // This is a named tuple
             // NOLINTNEXTLINE(modernize-loop-convert): Can't always use a range based loop here
-            for (int i = 0; i < elems.size(); i++)
+            for (size_t i = 0; i < elems.size(); i++)
             {
                 insert_value_in_output(*to_return, GET_NAME(i), elems.at(i));
             }

source/neuropod/backends/torchscript/torch_tensor.hh

@@ -29,6 +29,11 @@ limitations under the License.
 // If we're not building with a nightly relase of torch,
 // set the date to match the date of the official release
 #ifndef CAFFE2_NIGHTLY_VERSION
+#if CAFFE2_VERSION == 10100
+// The date of the official torch 1.1.0 release
+#define CAFFE2_NIGHTLY_VERSION 20190430
+#endif
+
 #if CAFFE2_VERSION == 10200
 // The date of the official torch 1.2.0 release
 #define CAFFE2_NIGHTLY_VERSION 20190808
@@ -67,27 +72,27 @@ T *get_data_from_torch_tensor(const torch::Tensor &tensor)
 }
 
 template <>
-uint16_t *get_data_from_torch_tensor(const torch::Tensor &tensor)
+[[maybe_unused]] uint16_t *get_data_from_torch_tensor(const torch::Tensor & /*unused*/)
 {
     NEUROPOD_ERROR("TorchScript doesn't support type uint16_t");
 }
 
 template <>
-uint32_t *get_data_from_torch_tensor(const torch::Tensor &tensor)
+[[maybe_unused]] uint32_t *get_data_from_torch_tensor(const torch::Tensor & /*unused*/)
 {
     NEUROPOD_ERROR("TorchScript doesn't support type uint32_t");
 }
 
 template <>
-uint64_t *get_data_from_torch_tensor(const torch::Tensor &tensor)
+[[maybe_unused]] uint64_t *get_data_from_torch_tensor(const torch::Tensor & /*unused*/)
 {
     NEUROPOD_ERROR("TorchScript doesn't support type uint64_t");
 }
 
-torch::Deleter get_torch_deleter(const Deleter &deleter, void *data)
+[[maybe_unused]] torch::Deleter get_torch_deleter(const Deleter &deleter, void *data)
 {
     auto handle = register_deleter(deleter, data);
-    return [handle](void *unused) { run_deleter(handle); };
+    return [handle](void * /*unused*/) { run_deleter(handle); };
 }
 
 } // namespace
@@ -116,7 +121,7 @@ public:
     }
 
     // Wrap an existing torch tensor
-    TorchNeuropodTensor(torch::Tensor tensor) : TypedNeuropodTensor<T>(tensor.sizes().vec()), tensor(tensor) {}
+    TorchNeuropodTensor(torch::Tensor t) : TypedNeuropodTensor<T>(t.sizes().vec()), tensor(t) {}
 
     ~TorchNeuropodTensor() = default;
 
@@ -147,7 +152,7 @@ protected:
         }
         else
         {
-            out = tensor.to(torch::Device(torch::kCUDA, device));
+            out = tensor.to(torch::Device(torch::kCUDA, static_cast<short>(device)));
         }
 
         return std::make_shared<TorchNeuropodTensor<T>>(std::move(out));
@@ -158,7 +163,7 @@ protected:
 #define ELEMENTS(collection) collection
 #define GET_STRING_FROM_LIST(item) item
 #else
-#define ELEMENTS(collectionptr) collectionptr->elements();
+#define ELEMENTS(collectionptr) collectionptr->elements()
 #define GET_STRING_FROM_LIST(item) item.toStringRef()
 #endif
 
@@ -254,7 +259,7 @@ protected:
 };
 
 // Utility function to get an IValue from a torch tensor
-torch::jit::IValue get_ivalue_from_torch_tensor(const std::shared_ptr<NeuropodValue> &tensor)
+inline torch::jit::IValue get_ivalue_from_torch_tensor(const std::shared_ptr<NeuropodValue> &tensor)
 {
     return std::dynamic_pointer_cast<NativeDataContainer<torch::jit::IValue>>(tensor)->get_native_data();
 }

source/neuropod/internal/backend_registration.hh

@@ -74,8 +74,11 @@ BackendFactoryFunction get_backend_for_type(const std::vector<BackendLoadSpec> &
 
 // A macro to easily define a backend
 // Example: REGISTER_NEUROPOD_BACKEND(SomeTensorflowBackend, "tensorflow", "1.13.1")
-#define REGISTER_NEUROPOD_BACKEND(CLS, type, version) \
-    bool is_registered_##CLS = register_backend(#CLS, type, version, createNeuropodBackend<CLS>);
+#define REGISTER_NEUROPOD_BACKEND(CLS, type, version)                                             \
+    namespace                                                                                     \
+    {                                                                                             \
+    bool is_registered_##CLS = register_backend(#CLS, type, version, createNeuropodBackend<CLS>); \
+    }
 
 // Utility macros
 #define STR_HELPER(x) #x