Draft: Revisits strict build setting

- Strict build for all projects including tests - To reduce bug in test code - Introduce option to disable warning for external libraries - To reduce warning message from external libraries ONE-DCO-1.0-Signed-off-by: Hyeongseok Oh <[email protected]>
Samsung · Dec 16, 2024 · d3d519a · d3d519a
1 parent c73dd4c
commit d3d519a
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Showing 80 changed files with 224 additions and 211 deletions.
diff --git a/Makefile.template b/Makefile.template
@@ -83,6 +83,9 @@ ifeq ($(BUILD_TYPE_LC),release)
 	INSTALL_OPTIONS+= --strip
 endif
 
+# Use strict build
+OPTIONS+= -DENABLE_STRICT_BUILD=ON
+
 WORKHOME=$(CURDIR)/Product
 WORKFOLDER=$(TARGET_ARCH_LC)-$(TARGET_OS).$(BUILD_TYPE_LC)
 WORKSPACE=$(WORKHOME)/$(WORKFOLDER)

diff --git a/compiler/arser/include/arser/arser.h b/compiler/arser/include/arser/arser.h
@@ -170,12 +170,12 @@ class Argument
 public:
   explicit Argument(const std::string &arg_name) : _long_name{arg_name}, _names{arg_name} {}
   explicit Argument(const std::string &short_name, const std::string &long_name)
-    : _short_name{short_name}, _long_name{long_name}, _names{short_name, long_name}
+    : _long_name{long_name}, _short_name{short_name}, _names{short_name, long_name}
   {
   }
   explicit Argument(const std::string &short_name, const std::string &long_name,
                     const std::vector<std::string> &names)
-    : _short_name{short_name}, _long_name{long_name}, _names{names}
+    : _long_name{long_name}, _short_name{short_name}, _names{names}
   {
     // 'names' must have 'short_name' and 'long_name'.
     auto it = std::find(names.begin(), names.end(), short_name);

diff --git a/compute/ARMComputeEx/CMakeLists.txt b/compute/ARMComputeEx/CMakeLists.txt
@@ -20,7 +20,6 @@ execute_process (
 add_library(arm_compute_ex SHARED ${ACL_EX_SRCS})
 target_include_directories(arm_compute_ex PUBLIC ${ACL_EX_BASE})
 target_link_libraries(arm_compute_ex PRIVATE arm_compute)
-target_link_libraries(arm_compute_ex PRIVATE nnfw_common)
 target_link_libraries(arm_compute_ex PRIVATE nnfw_coverage)
 # Defines to enable validate check in debug build
 target_compile_definitions(arm_compute_ex PRIVATE EMBEDDED_KERNELS
@@ -29,7 +28,7 @@ target_compile_definitions(arm_compute_ex PRIVATE EMBEDDED_KERNELS
 # Validate check functions are not used on release build
 # Some parameter are used for validate check function call, and these parameter may not used on release build
 # Because clang requires to add "-Wno-unused-parameter -Wno-unused-function" after "-Wall",
-# this should be after linking nnfw_common and use interface lib linking
+# this should be after interface lib linking
 add_library(ignore_unused_warning INTERFACE)
 target_compile_options(ignore_unused_warning INTERFACE -Wno-unused-parameter -Wno-unused-function)
 target_link_libraries(arm_compute_ex PRIVATE $<$<NOT:$<CONFIG:Debug>>:ignore_unused_warning>)

diff --git a/compute/cker/src/DepthwiseConv.test.cc b/compute/cker/src/DepthwiseConv.test.cc
@@ -75,7 +75,7 @@ template <typename T> class DepthwiseConvVerifier
                       const nnfw::cker::Shape &input_shape, const T *input_data,
                       const nnfw::cker::Shape &filter_shape, const T *filter_data,
                       const nnfw::cker::Shape &bias_shape, const T *bias_data,
-                      const nnfw::cker::Shape &output_shape, const T *expected)
+                      const nnfw::cker::Shape &output_shape)
   {
     std::vector<T> output(output_shape.FlatSize());
     EXPECT_ANY_THROW(
@@ -293,11 +293,12 @@ TEST(CKer_Operation, neg_DepthwiseConv)
     nnfw::cker::Shape bias_shape{1};
     std::vector<float> bias = {0.0};
     nnfw::cker::Shape output_shape{1, 3, 3, 1}; // n, h, w, c
-    std::vector<float> expected = {4.0, 0.0, 3.0, 0.0, 0.0, 0.0, 2.0, 0.0, 1.0};
+    // Expected output but not used - not supported yet
+    // std::vector<float> expected = {4.0, 0.0, 3.0, 0.0, 0.0, 0.0, 2.0, 0.0, 1.0};
 
     DepthwiseConvVerifier<float> verifier;
     verifier.prepare(output_shape, filter_shape);
     verifier.checkException(params, input_shape, input.data(), filter_shape, filter.data(),
-                            bias_shape, bias.data(), output_shape, expected.data());
+                            bias_shape, bias.data(), output_shape);
   }
 }
diff --git a/compute/cker/src/Range.test.cc b/compute/cker/src/Range.test.cc
@@ -28,7 +28,7 @@ TEST(CKer_Operation, Range)
     std::vector<int> actual(10);
     nnfw::cker::Range<int>(&start, &limit, &delta, actual.data());
 
-    for (int i = 0; i < actual.size(); i++)
+    for (size_t i = 0; i < actual.size(); i++)
       ASSERT_EQ(actual[i], i);
   }
 
@@ -40,7 +40,7 @@ TEST(CKer_Operation, Range)
     std::vector<int> actual(expected.size());
     nnfw::cker::Range<int>(&start, &limit, &delta, actual.data());
 
-    for (int i = 0; i < actual.size(); i++)
+    for (size_t i = 0; i < actual.size(); i++)
       ASSERT_EQ(actual[i], expected[i]);
   }
 
@@ -52,7 +52,7 @@ TEST(CKer_Operation, Range)
     std::vector<float> actual(expected.size());
     nnfw::cker::Range<float>(&start, &limit, &delta, actual.data());
 
-    for (int i = 0; i < actual.size(); i++)
+    for (size_t i = 0; i < actual.size(); i++)
       ASSERT_FLOAT_EQ(actual[i], expected[i]);
   }
 }

diff --git a/compute/cker/src/train/Adam.test.cc b/compute/cker/src/train/Adam.test.cc
@@ -72,7 +72,7 @@ template <typename T> class AdamOptimizerVerifier
 
     const T alpha = _learning_rate * std::sqrt(static_cast<T>(1) - beta2_power) /
                     (static_cast<T>(1) - beta1_power);
-    for (int i = 0; i < _expected.size(); ++i)
+    for (size_t i = 0; i < _expected.size(); ++i)
     {
       T m = _m.at(i);
       T v = _v.at(i);

diff --git a/compute/cker/src/train/AveragePool.test.cc b/compute/cker/src/train/AveragePool.test.cc
@@ -44,8 +44,8 @@ template <typename T> class AvgPoolOpVerifier
   void verifyForward(const std::vector<T> input, const std::vector<T> expected_output,
                      bool expect_eq = true)
   {
-    assert(input.size() == _in_shape.FlatSize());
-    assert(expected_output.size() == _out_shape.FlatSize());
+    assert(input.size() == static_cast<size_t>(_in_shape.FlatSize()));
+    assert(expected_output.size() == static_cast<size_t>(_out_shape.FlatSize()));
 
     std::vector<T> cacluated_output(_out_shape.FlatSize());
     nnfw::cker::AveragePool<float>(_op_params, _in_shape, input.data(), _out_shape,
@@ -60,8 +60,8 @@ template <typename T> class AvgPoolOpVerifier
   void verifyBackward(const std::vector<T> incoming_data, const std::vector<T> expected_grad_data,
                       bool expect_eq = true)
   {
-    assert(incoming_data.size() == _out_shape.FlatSize());
-    assert(expected_grad_data.size() == _in_shape.FlatSize());
+    assert(incoming_data.size() == static_cast<size_t>(_out_shape.FlatSize()));
+    assert(expected_grad_data.size() == static_cast<size_t>(_in_shape.FlatSize()));
 
     std::vector<T> calcuated_grad(_in_shape.FlatSize());
     nnfw::cker::train::AveragePool2DGrad(_op_params, _out_shape, incoming_data.data(), _in_shape,

diff --git a/compute/cker/src/train/Loss.test.cc b/compute/cker/src/train/Loss.test.cc
@@ -37,29 +37,24 @@ template <typename T> class LossCCEVerifier
     assert(y_pred.size() == y_true.size());
 
     std::vector<T> output(_out_shape.FlatSize());
-    const int N = _in_shape.Dims(0);
-    const int D = _in_shape.FlatSize() / N;
 
     nnfw::cker::train::CategoricalCrossEntropy(_in_shape, y_pred.data(), _in_shape, y_true.data(),
                                                _out_shape, output.data());
 
     // Don't be panic when it fails after kernel implementation or input is changed.
     // CrossEntropy formula can be calculated slightly differently depending on the environment
     // because it involes calculations such as log or exp.
-    for (int i = 0; i < output.size(); ++i)
+    for (size_t i = 0; i < output.size(); ++i)
     {
       EXPECT_NEAR(output[i], expected[i], 1e-4f);
     }
   }
 
-  void throwForward(const std::vector<T> &y_pred, const std::vector<T> &y_true,
-                    const std::vector<T> &expected)
+  void throwForward(const std::vector<T> &y_pred, const std::vector<T> &y_true)
   {
     assert(y_pred.size() == y_true.size());
 
     std::vector<T> output(_out_shape.FlatSize());
-    const int N = _in_shape.Dims(0);
-    const int D = _in_shape.FlatSize() / N;
 
     EXPECT_ANY_THROW(nnfw::cker::train::CategoricalCrossEntropy(
       _in_shape, y_pred.data(), _in_shape, y_true.data(), _out_shape, output.data()));
@@ -72,16 +67,14 @@ template <typename T> class LossCCEVerifier
     assert(y_pred.size() == y_true.size());
 
     std::vector<T> output(_in_shape.FlatSize());
-    const int N = _in_shape.Dims(0);
-    const int D = _in_shape.FlatSize() / N;
 
     nnfw::cker::train::CategoricalCrossEntropyGrad(
       _in_shape, y_pred.data(), _in_shape, y_true.data(), _out_shape, output.data(), reduction);
 
     // Don't be panic when it fails after kernel implementation or input is changed.
     // CrossEntropy Gradient formula can be calculated slightly differently depending on the
     // environment because it involes calculations such as log or exp.
-    for (int i = 0; i < output.size(); ++i)
+    for (size_t i = 0; i < output.size(); ++i)
     {
       EXPECT_NEAR(output[i], expected[i], 1e-4f);
     }
@@ -102,33 +95,32 @@ template <typename T> class LossCCEVerifier
                                                          y_true.data(), _out_shape, loss_out.data(),
                                                          _in_shape, grad.data(), reduction);
 
-    for (int i = 0; i < loss_out.size(); ++i)
+    for (size_t i = 0; i < loss_out.size(); ++i)
     {
       EXPECT_NEAR(loss_out[i], expected_loss_out[i], 1e-4f);
     }
 
-    for (int i = 0; i < grad.size(); ++i)
+    for (size_t i = 0; i < grad.size(); ++i)
     {
       EXPECT_NEAR(grad[i], expected_grad[i], 1e-4f);
     }
   }
 
   void throwBackward(const std::vector<T> &y_pred, const std::vector<T> &y_true,
-                     const std::vector<T> &expected, nnfw::cker::train::LossReductionType reduction)
+                     [[maybe_unused]] const std::vector<T> &expected,
+                     nnfw::cker::train::LossReductionType reduction)
   {
     assert(y_pred.size() == y_true.size());
 
     std::vector<T> output(_out_shape.FlatSize());
-    const int N = _in_shape.Dims(0);
-    const int D = _in_shape.FlatSize() / N;
 
     EXPECT_ANY_THROW(nnfw::cker::train::CategoricalCrossEntropyGrad(
       _in_shape, y_pred.data(), _in_shape, y_true.data(), _out_shape, output.data(), reduction));
   }
 
   void throwBackwardWithLogits(const std::vector<T> &logits, const std::vector<T> &y_true,
-                               const std::vector<T> &expected_loss_out,
-                               const std::vector<T> &expected_grad,
+                               [[maybe_unused]] const std::vector<T> &expected_loss_out,
+                               [[maybe_unused]] const std::vector<T> &expected_grad,
                                nnfw::cker::train::LossReductionType reduction)
   {
     assert(logits.size() == y_true.size());
@@ -186,7 +178,7 @@ TEST(CKer_Operation, LossMSE)
     nnfw::cker::train::MSE(nnfw::cker::Shape{2, 3}, y_pred.data(), nnfw::cker::Shape{2, 3},
                            y_true.data(), nnfw::cker::Shape{2}, output.data());
 
-    for (int i = 0; i < output.size(); ++i)
+    for (size_t i = 0; i < output.size(); ++i)
     {
       EXPECT_FLOAT_EQ(output[i], expected[i]);
     }
@@ -204,7 +196,7 @@ TEST(CKer_Operation, LossMSE)
     nnfw::cker::train::MSE(nnfw::cker::Shape{2, 3, 4}, y_pred.data(), nnfw::cker::Shape{2, 3, 4},
                            y_true.data(), nnfw::cker::Shape{2}, output.data());
 
-    for (int i = 0; i < output.size(); ++i)
+    for (size_t i = 0; i < output.size(); ++i)
     {
       EXPECT_FLOAT_EQ(output[i], expected[i]);
     }
@@ -223,7 +215,7 @@ TEST(CKer_Operation, neg_LossMSE)
     nnfw::cker::train::MSE(nnfw::cker::Shape{2, 5}, y_pred.data(), nnfw::cker::Shape{2, 5},
                            y_true.data(), nnfw::cker::Shape{2}, output.data());
 
-    for (int i = 0; i < output.size(); ++i)
+    for (size_t i = 0; i < output.size(); ++i)
     {
       EXPECT_NE(output[i], expected[i]);
     }
@@ -400,10 +392,12 @@ TEST(CKer_Operation, neg_LossCategoricalCrossEntropy)
     std::vector<float> y_pred = {-2.86E-12, 2.82E-13, 0.99999845, 2.36E-07, 2.91E-16,
                                  2.10E-07,  1.69E-14, 1.21E-17,   1.08E-06, 6.23E-18};
     std::vector<float> y_true = {0, 0, 0, 0, 0, 0, 0, 0, 0, 1};
-    std::vector<float> expected = {39.617155};
+
+    // Expected value, but not used for verification due to exception
+    // std::vector<float> expected = {39.617155};
 
     LossCCEVerifier<float> verifier(in_shape, out_shape);
-    verifier.throwForward(y_pred, y_true, expected);
+    verifier.throwForward(y_pred, y_true);
   }
 }
 

diff --git a/compute/cker/src/train/MaxPool.test.cc b/compute/cker/src/train/MaxPool.test.cc
@@ -47,8 +47,8 @@ template <typename T> class MaxPoolOpVerifier
   void verifyForward(const std::vector<T> input, const std::vector<T> expected_output,
                      bool expect_eq = true)
   {
-    assert(input.size() == _in_shape.FlatSize());
-    assert(expected_output.size() == _out_shape.FlatSize());
+    assert(input.size() == static_cast<size_t>(_in_shape.FlatSize()));
+    assert(expected_output.size() == static_cast<size_t>(_out_shape.FlatSize()));
 
     std::vector<T> cacluated_output(_out_shape.FlatSize());
     nnfw::cker::train::MaxPool2D(_op_params, _in_shape, input.data(), _out_shape,
@@ -63,8 +63,8 @@ template <typename T> class MaxPoolOpVerifier
   void verifyBackward(const std::vector<T> incoming_data, const std::vector<T> expected_grad_data,
                       bool expect_eq = true)
   {
-    assert(incoming_data.size() == _out_shape.FlatSize());
-    assert(expected_grad_data.size() == _in_shape.FlatSize());
+    assert(incoming_data.size() == static_cast<size_t>(_out_shape.FlatSize()));
+    assert(expected_grad_data.size() == static_cast<size_t>(_in_shape.FlatSize()));
 
     std::vector<T> calcuated_grad(_in_shape.FlatSize());
     nnfw::cker::train::MaxPool2DGrad(_out_shape, incoming_data.data(), _arg_max_index.data(),

diff --git a/compute/cker/src/train/Pad.test.cc b/compute/cker/src/train/Pad.test.cc
@@ -46,8 +46,8 @@ template <typename T> class PadOpVerifier
   void verifyForward(const std::vector<T> input, const std::vector<T> expected_output,
                      bool expect_eq = true)
   {
-    assert(input.size() == _in_shape.FlatSize());
-    assert(expected_output.size() == _out_shape.FlatSize());
+    assert(input.size() == static_cast<size_t>(_in_shape.FlatSize()));
+    assert(expected_output.size() == static_cast<size_t>(_out_shape.FlatSize()));
 
     std::vector<T> cacluated_output(_out_shape.FlatSize());
     nnfw::cker::Pad(_op_params.data, _op_params.rank, _in_shape, input.data(), _out_shape,
@@ -62,8 +62,8 @@ template <typename T> class PadOpVerifier
   void verifyBackward(const std::vector<T> backward_output,
                       const std::vector<T> expected_backward_input, bool expect_eq = true)
   {
-    assert(backward_output.size() == _out_shape.FlatSize());
-    assert(expected_backward_input.size() == _in_shape.FlatSize());
+    assert(backward_output.size() == static_cast<size_t>(_out_shape.FlatSize()));
+    assert(expected_backward_input.size() == static_cast<size_t>(_in_shape.FlatSize()));
 
     std::vector<T> backward_input(_in_shape.FlatSize());
     nnfw::cker::train::Depad(_op_params.data, _op_params.rank, _out_shape, backward_output.data(),

diff --git a/compute/cker/src/train/ReduceMean.test.cc b/compute/cker/src/train/ReduceMean.test.cc
@@ -39,8 +39,8 @@ template <typename T> class ReduceMeanVerifier
   void verifyForward(const std::vector<T> &input, const std::vector<T> &expected,
                      bool expect_eq = true)
   {
-    assert(input.size() == _in_shape.FlatSize());
-    assert(expected.size() == _out_shape.FlatSize());
+    assert(input.size() == static_cast<size_t>(_in_shape.FlatSize()));
+    assert(expected.size() == static_cast<size_t>(_out_shape.FlatSize()));
 
     std::vector<T> output(_out_shape.FlatSize());
 
@@ -69,7 +69,7 @@ template <typename T> class ReduceMeanVerifier
     if (expect_eq)
     {
       // consider the floating point error
-      for (int i = 0; i < grad.size(); ++i)
+      for (size_t i = 0; i < grad.size(); ++i)
       {
         EXPECT_NEAR(grad[i], expected[i], 1e-3f);
       }

diff --git a/compute/cker/src/train/SGD.test.cc b/compute/cker/src/train/SGD.test.cc
@@ -55,7 +55,7 @@ template <typename T> class SGDOptimizerVerifier
   {
     assert(_expected.size() == _gradient.size());
 
-    for (int i = 0; i < _expected.size(); ++i)
+    for (size_t i = 0; i < _expected.size(); ++i)
     {
       T g = _gradient.at(i);
       T &var = _expected.at(i);

diff --git a/infra/cmake/modules/ExternalProjectTools.cmake b/infra/cmake/modules/ExternalProjectTools.cmake
@@ -1,4 +1,9 @@
 macro(add_extdirectory DIR TAG)
+  # Disable warning messages from external source code
+  if(DISABLE_EXTERNAL_WARNING)
+    add_compile_options(-w)
+  endif(DISABLE_EXTERNAL_WARNING)
+
   cmake_parse_arguments(ARG "EXCLUDE_FROM_ALL" "" "" ${ARGN})
   if(ARG_EXCLUDE_FROM_ALL)
     add_subdirectory(${DIR} "${CMAKE_BINARY_DIR}/externals/${TAG}" EXCLUDE_FROM_ALL)

diff --git a/infra/debian/runtime/rules b/infra/debian/runtime/rules
@@ -18,7 +18,7 @@ override_dh_auto_build:
 	mkdir -p $(NNFW_WORKSPACE)
 	./nnfw configure -DCMAKE_BUILD_TYPE=Release -DEXTERNALS_BUILD_THREADS=$(NPROC) \
 	  -DDOWNLOAD_GTEST=OFF -DENABLE_TEST=OFF \
-		-DBUILD_PYTHON_BINDING=OFF
+		-DBUILD_PYTHON_BINDING=OFF -DENABLE_STRICT_BUILD=ON
 	./nnfw build -j$(NPROC)
 override_dh_auto_install:
 	./nnfw install --prefix $(NNFW_INSTALL_PREFIX) --strip

diff --git a/infra/nnfw/CMakeLists.txt b/infra/nnfw/CMakeLists.txt
@@ -95,16 +95,16 @@ if(${ENABLE_COVERAGE} AND NOT ${ENABLE_TEST})
   message(FATAL_ERROR "Test should be enabled to measure test coverage")
 endif(${ENABLE_COVERAGE} AND NOT ${ENABLE_TEST})
 
-add_library(nnfw_common INTERFACE)
 if(ENABLE_STRICT_BUILD)
-  target_compile_options(nnfw_common INTERFACE -Werror -Wall -Wextra)
+  add_compile_options(-Werror -Wall -Wextra)
 endif(ENABLE_STRICT_BUILD)
 
-macro(nnfw_strict_build TARGET)
-  if(ENABLE_STRICT_BUILD)
-    target_compile_options(${TARGET} PRIVATE -Werror -Wall -Wextra)
-  endif(ENABLE_STRICT_BUILD)
-endmacro(nnfw_strict_build)
+# Ease build by disabling all warning messages for strict build mode for specific targets
+# (ex. 3rd-party libraries)
+add_library(nnfw_ease_warning INTERFACE)
+if(ENABLE_STRICT_BUILD)
+  target_compile_options(nnfw_ease_warning INTERFACE -w)
+endif(ENABLE_STRICT_BUILD)
 
 # TODO Replace using default build option setting in cmake/buildtool/config/config_linux.cmake
 #      to link nnfw_coverage on each module which want to check coverage

diff --git a/infra/nnfw/cmake/CfgOptionFlags.cmake b/infra/nnfw/cmake/CfgOptionFlags.cmake
@@ -10,7 +10,8 @@ include("cmake/options/options_${TARGET_PLATFORM}.cmake")
 #
 # Default build configuration for project
 #
-option(ENABLE_STRICT_BUILD "Treat warning as error" ON)
+option(ENABLE_STRICT_BUILD "Treat warning as error" OFF)
+option(DISABLE_EXTERNAL_WARNING "Ignore warnings from external libraries" ON)
 option(ENABLE_COVERAGE "Build for coverage test" OFF)
 option(BUILD_EXT_MULTITHREAD "Build external build using multi thread" ON)
 option(BUILD_ONERT "Build onert" ON)

diff --git a/infra/nnfw/cmake/packages/OouraFFTConfig.cmake b/infra/nnfw/cmake/packages/OouraFFTConfig.cmake
@@ -12,6 +12,8 @@ function(_OouraFFT_build)
       ${OouraFFTSource_DIR}/fftsg2d.c
       ${OouraFFTSource_DIR}/fftsg.c
     )
+    # Ignore strict build warnings
+    target_link_libraries(fft2d_fftsg2d PRIVATE nnfw_ease_warning)
     add_library(oourafft::fftsg2d ALIAS fft2d_fftsg2d)
   endif(NOT TARGET oourafft::fftsg2d)
 

diff --git a/infra/nnfw/cmake/packages/PthreadpoolConfig.cmake b/infra/nnfw/cmake/packages/PthreadpoolConfig.cmake
@@ -22,8 +22,6 @@ function(_Pthreadpool_Build)
 
   add_extdirectory("${PthreadpoolSource_DIR}" PTHREADPOOL EXCLUDE_FROM_ALL)
   set_target_properties(pthreadpool PROPERTIES POSITION_INDEPENDENT_CODE ON)
-  # Suppress warnings generated by pthreadpool
-  set_target_properties(pthreadpool PROPERTIES COMPILE_FLAGS "-Wno-deprecated-declarations")
   set(PthreadpoolSource_DIR ${PthreadpoolSource_DIR} PARENT_SCOPE)
   set(Pthreadpool_FOUND TRUE PARENT_SCOPE)
 endfunction(_Pthreadpool_Build)