turn on -Werror=unused-variable in our Bazel CPU build

Summary:
We also fix any existing issues. Note that we only do this for the CPU
build because nvcc is considered a C++ toolchain but it does not have
the same flag support. Adding flags to the GPU build will cause nvcc
errors.

Test Plan: Built locally, rely on CI to confirm.

Reviewers: malfet

Subscribers:

Tasks:

Tags:

Pull Request resolved: https://github.com/pytorch/pytorch/pull/79156

Approved by: https://github.com/seemethere, https://github.com/osalpekar, https://github.com/albanD

.bazelrc

@@ -52,7 +52,9 @@ build \
     --per_file_copt='^//.*\.(cpp|cc)$'@-Werror=type-limits                \
     --per_file_copt=^//.*\.cu$@--compiler-options=-Werror=type-limits     \
     --per_file_copt='^//.*\.(cpp|cc)$'@-Werror=unused-function            \
-    --per_file_copt=^//.*\.cu$@--compiler-options=-Werror=unused-function
+    --per_file_copt=^//.*\.cu$@--compiler-options=-Werror=unused-function \
+    --per_file_copt='^//.*\.(cpp|cc)$'@-Werror=unused-variable            \
+    --per_file_copt=^//.*\.cu$@--compiler-options=-Werror=unused-variable
 
 build \
     --per_file_copt=//:aten/src/ATen/RegisterCompositeExplicitAutograd.cpp@-Wno-error=unused-function \

c10/core/DispatchKeySet.h

@@ -504,6 +504,8 @@ class DispatchKeySet final {
     using iterator_category = std::input_iterator_tag;
     using value_type = DispatchKey;
     using difference_type = ptrdiff_t;
+    using reference = value_type&;
+    using pointer = value_type*;
     // final mask value should mask out the entire keyset
     static const uint8_t end_iter_mask_val =
         num_backends + num_functionality_keys;

c10/test/core/DispatchKeySet_test.cpp

@@ -1,5 +1,7 @@
 #include <gtest/gtest.h>
 
+#include <cstddef>
+#include <iterator>
 #include <unordered_set>
 
 #include <c10/core/DispatchKeySet.h>
@@ -14,7 +16,6 @@ TEST(DispatchKeySet, ShowSemantics) {
   // It corresponds to "dense" functionality, "CPU" backend.
   // This means that it gets a dense functionality bit, and a cpu backend bit
   // set.
-  auto undefined_set = DispatchKeySet();
   auto dense_cpu_set = DispatchKeySet(DispatchKey::CPU);
   ASSERT_TRUE(dense_cpu_set.has(DispatchKey::Dense));
   ASSERT_TRUE(dense_cpu_set.has_backend(BackendComponent::CPUBit));
@@ -48,8 +49,6 @@ TEST(DispatchKeySet, ShowSemantics) {
        DispatchKey::Dense,
        DispatchKey::CUDA,
        DispatchKey::CPU});
-  auto fpga = DispatchKeySet(DispatchKey::FPGA);
-  auto fpga_and_cpu = DispatchKeySet({DispatchKey::FPGA, DispatchKey::CPU});
   // this keyset has all of the building block keys:
   ASSERT_TRUE(autograd_dense_cpu_cuda.has(DispatchKey::AutogradFunctionality));
   ASSERT_TRUE(autograd_dense_cpu_cuda.has(DispatchKey::Dense));
@@ -86,8 +85,6 @@ TEST(DispatchKeySet, ShowSemantics) {
   // Iterators allow you to iterate individually through the DispatchKey's in a
   // DispatchKeySet
   auto empty_set = DispatchKeySet();
-  auto t1 = empty_set.begin();
-  auto t2 = empty_set.end();
   ASSERT_EQ(*empty_set.begin(), *empty_set.end());
 
   // However, only keys that correspond to actual runtime indices of kernels in
@@ -370,29 +367,12 @@ TEST(DispatchKeySet, IteratorCrossProduct) {
 
 TEST(DispatchKeySet, IteratorFull) {
   DispatchKeySet full_set(DispatchKeySet::FULL);
-  uint8_t i = 0;
+  std::ptrdiff_t count = std::distance(full_set.begin(), full_set.end());
-
-  for (const auto& it : full_set) {
-    i++;
-  }
-  // Total # of runtime entries includes an entry for DispatchKey::Undefined,
-  // which is not included when iterating through the DispatchKeySet.
-  ASSERT_EQ(i, num_runtime_entries - 1);
-}
-
-TEST(DispatchKeySet, IteratorRangeFull) {
-  DispatchKeySet full_set(DispatchKeySet::FULL);
-  uint8_t i = 0;
-
-  for (DispatchKey dispatch_key : full_set) {
-    i++;
-  }
 
   // Total # of runtime entries includes an entry for DispatchKey::Undefined,
   // which is not included when iterating through the DispatchKeySet.
-  ASSERT_EQ(i, num_runtime_entries - 1);
+  ASSERT_EQ(count, std::ptrdiff_t{num_runtime_entries} - 1);
 }
-
 TEST(DispatchKeySet, FailAtEndIterator) {
   DispatchKeySet full_set(DispatchKeySet::FULL);
   uint64_t raw_repr = full_set.raw_repr();

c10/test/util/intrusive_ptr_test.cpp

@@ -219,8 +219,6 @@ TEST(
     givenInvalidPtr_whenMoveAssigning_thenNewInstanceIsValid) {
   intrusive_ptr<SomeClass> obj1 = make_intrusive<SomeClass>();
   intrusive_ptr<SomeClass> obj2;
-  // NOLINTNEXTLINE(clang-analyzer-deadcode.DeadStores)
-  SomeClass* obj1ptr = obj1.get();
   obj2 = std::move(obj1);
   EXPECT_TRUE(obj2.defined());
 }
@@ -271,8 +269,6 @@ TEST(
     givenInvalidPtr_whenMoveAssigningToBaseClass_thenNewInstanceIsValid) {
   intrusive_ptr<SomeChildClass> obj1 = make_intrusive<SomeChildClass>(5);
   intrusive_ptr<SomeBaseClass> obj2;
-  // NOLINTNEXTLINE(clang-analyzer-deadcode.DeadStores)
-  SomeBaseClass* obj1ptr = obj1.get();
   obj2 = std::move(obj1);
   EXPECT_TRUE(obj2.defined());
 }
@@ -358,8 +354,6 @@ TEST(
     givenInvalidPtr_whenCopyAssigning_thenNewInstanceIsValid) {
   intrusive_ptr<SomeClass> obj1 = make_intrusive<SomeClass>();
   intrusive_ptr<SomeClass> obj2;
-  // NOLINTNEXTLINE(clang-analyzer-deadcode.DeadStores)
-  SomeClass* obj1ptr = obj1.get();
   obj2 = obj1;
   EXPECT_TRUE(obj2.defined());
 }
@@ -387,8 +381,6 @@ TEST(
     givenInvalidPtr_whenCopyAssigningToBaseClass_thenNewInstanceIsValid) {
   intrusive_ptr<SomeChildClass> obj1 = make_intrusive<SomeChildClass>(5);
   intrusive_ptr<SomeBaseClass> obj2;
-  // NOLINTNEXTLINE(clang-analyzer-deadcode.DeadStores)
-  SomeBaseClass* obj1ptr = obj1.get();
   obj2 = obj1;
   EXPECT_TRUE(obj2.defined());
 }
@@ -1779,8 +1771,7 @@ TEST(
     givenInvalidPtr_whenMoveAssigning_thenNewInstanceIsValid) {
   IntrusiveAndWeak<SomeClass> obj1 = make_weak_intrusive<SomeClass>();
   weak_intrusive_ptr<SomeClass> obj2 = make_invalid_weak<SomeClass>();
-  // NOLINTNEXTLINE(clang-analyzer-deadcode.DeadStores)
+  obj1.weak.lock().get();
-  SomeClass* obj1ptr = obj1.weak.lock().get();
   obj2 = std::move(obj1.weak);
   EXPECT_FALSE(obj2.expired());
 }
@@ -1826,8 +1817,7 @@ TEST(
     givenWeakOnlyPtr_whenMoveAssigning_thenNewInstanceIsValid) {
   IntrusiveAndWeak<SomeClass> obj1 = make_weak_intrusive<SomeClass>();
   weak_intrusive_ptr<SomeClass> obj2 = make_weak_only<SomeClass>();
-  // NOLINTNEXTLINE(clang-analyzer-deadcode.DeadStores)
+  obj1.weak.lock().get();
-  SomeClass* obj1ptr = obj1.weak.lock().get();
   obj2 = std::move(obj1.weak);
   EXPECT_FALSE(obj2.expired());
 }
@@ -1846,8 +1836,7 @@ TEST(
     WeakIntrusivePtrTest,
     givenWeakOnlyPtr_whenMoveAssigningToSelf_thenStaysInvalid) {
   weak_intrusive_ptr<SomeClass> obj1 = make_weak_only<SomeClass>();
-  // NOLINTNEXTLINE(clang-analyzer-deadcode.DeadStores)
+  obj1.lock().get();
-  SomeClass* obj1ptr = obj1.lock().get();
   obj1 = std::move(obj1);
   // NOLINTNEXTLINE(bugprone-use-after-move)
   EXPECT_TRUE(obj1.expired());
@@ -1911,8 +1900,7 @@ TEST(
   IntrusiveAndWeak<SomeChildClass> obj1 =
       make_weak_intrusive<SomeChildClass>(5);
   weak_intrusive_ptr<SomeBaseClass> obj2 = make_invalid_weak<SomeBaseClass>();
-  // NOLINTNEXTLINE(clang-analyzer-deadcode.DeadStores)
+  obj1.weak.lock().get();
-  SomeBaseClass* obj1ptr = obj1.weak.lock().get();
   obj2 = std::move(obj1.weak);
   EXPECT_FALSE(obj2.expired());
 }
@@ -1945,8 +1933,7 @@ TEST(
   IntrusiveAndWeak<SomeChildClass> obj1 =
       make_weak_intrusive<SomeChildClass>(5);
   weak_intrusive_ptr<SomeBaseClass> obj2 = make_weak_only<SomeBaseClass>(2);
-  // NOLINTNEXTLINE(clang-analyzer-deadcode.DeadStores)
+  obj1.weak.lock().get();
-  SomeBaseClass* obj1ptr = obj1.weak.lock().get();
   obj2 = std::move(obj1.weak);
   EXPECT_FALSE(obj2.expired());
 }
@@ -2028,8 +2015,7 @@ TEST(
     givenInvalidPtr_whenCopyAssigning_thenNewInstanceIsValid) {
   IntrusiveAndWeak<SomeClass> obj1 = make_weak_intrusive<SomeClass>();
   weak_intrusive_ptr<SomeClass> obj2 = make_invalid_weak<SomeClass>();
-  // NOLINTNEXTLINE(clang-analyzer-deadcode.DeadStores)
+  obj1.weak.lock().get();
-  SomeClass* obj1ptr = obj1.weak.lock().get();
   obj2 = obj1.weak;
   EXPECT_FALSE(obj2.expired());
 }
@@ -2048,8 +2034,7 @@ TEST(
     givenWeakOnlyPtr_whenCopyAssigning_thenNewInstanceIsValid) {
   IntrusiveAndWeak<SomeClass> obj1 = make_weak_intrusive<SomeClass>();
   weak_intrusive_ptr<SomeClass> obj2 = make_weak_only<SomeClass>();
-  // NOLINTNEXTLINE(clang-analyzer-deadcode.DeadStores)
+  obj1.weak.lock().get();
-  SomeClass* obj1ptr = obj1.weak.lock().get();
   obj2 = obj1.weak;
   EXPECT_FALSE(obj2.expired());
 }
@@ -2068,8 +2053,7 @@ TEST(
     WeakIntrusivePtrTest,
     givenWeakOnlyPtr_whenCopyAssigningToSelf_thenStaysInvalid) {
   weak_intrusive_ptr<SomeClass> obj1 = make_weak_only<SomeClass>();
-  // NOLINTNEXTLINE(clang-analyzer-deadcode.DeadStores)
+  obj1.lock().get();
-  SomeClass* obj1ptr = obj1.lock().get();
   // NOLINTNEXTLINE(clang-diagnostic-self-assign-overloaded)
   obj1 = obj1;
   EXPECT_TRUE(obj1.expired());
@@ -2111,8 +2095,7 @@ TEST(
   IntrusiveAndWeak<SomeChildClass> obj1 =
       make_weak_intrusive<SomeChildClass>(5);
   weak_intrusive_ptr<SomeBaseClass> obj2 = make_invalid_weak<SomeBaseClass>();
-  // NOLINTNEXTLINE(clang-analyzer-deadcode.DeadStores)
+  obj1.weak.lock().get();
-  SomeBaseClass* obj1ptr = obj1.weak.lock().get();
   obj2 = obj1.weak;
   EXPECT_FALSE(obj2.expired());
 }
@@ -2145,8 +2128,7 @@ TEST(
   IntrusiveAndWeak<SomeChildClass> obj1 =
       make_weak_intrusive<SomeChildClass>(5);
   weak_intrusive_ptr<SomeBaseClass> obj2 = make_weak_only<SomeBaseClass>(2);
-  // NOLINTNEXTLINE(clang-analyzer-deadcode.DeadStores)
+  obj1.weak.lock().get();
-  SomeBaseClass* obj1ptr = obj1.weak.lock().get();
   obj2 = obj1.weak;
   EXPECT_FALSE(obj2.expired());
 }

test/cpp/api/dataloader.cpp

@@ -1900,8 +1900,6 @@ TEST(DataLoaderTest, ChunkDatasetDoesNotHang) {
           datasets::ChunkDatasetOptions(
               prefetch_count, batch_size, cache_size));
 
-  samplers::SequentialSampler& chunk_sampler = dataset->chunk_sampler();
-
   auto data_loader = torch::data::make_data_loader(
       dataset.map(transforms::BatchLambda<DummyChunkDataReader::BatchType, DummyChunkDataReader::DataType>(
           [](DummyChunkDataReader::BatchType batch) {
@@ -2043,7 +2041,6 @@ TEST(DataLoaderTest, ChunkDatasetLoad) {
   const size_t prefetch_count = 1;
   const size_t batch_size = 10;
   const size_t dataloader_worker_count = 0;
-  const size_t save_interval = 2;
 
   DummyChunkDataReader data_reader;
   samplers::SequentialSampler sampler(0);
@@ -2223,8 +2220,6 @@ TEST(DataLoaderTest, ChunkDatasetCrossChunkShuffle) {
       std::vector<int> expected_result;
       {
         // construct expected result
-        int offset = 0;
-
         for (const auto i : c10::irange((chunk_count + cross_chunk_shuffle_count - 1) /
                  cross_chunk_shuffle_count)) {
           for (const auto j : c10::irange(chunk_size)) {

test/cpp/api/functional.cpp

@@ -1940,7 +1940,6 @@ TEST_F(FunctionalTest, InstanceNorm2d) {
 
 TEST_F(FunctionalTest, InstanceNorm2dDefaultOptions) {
   int num_features = 5;
-  double eps = 1e-05;
 
   auto input = torch::arange(2. * num_features * 2 * 2).view({2, num_features, 2, 2});
   auto output = F::instance_norm(input);
@@ -2031,7 +2030,6 @@ TEST_F(FunctionalTest, InstanceNorm3d) {
 
 TEST_F(FunctionalTest, InstanceNorm3dDefaultOptions) {
   int num_features = 5;
-  double eps = 1e-05;
 
   auto input = torch::arange(2. * num_features * 2 * 2 * 2).view({2, num_features, 2, 2, 2});
   auto output = F::instance_norm(input);

test/cpp/api/nn_utils.cpp

@@ -613,7 +613,6 @@ TEST_F(NNUtilsTest, PackPaddedSequence) {
       }
       batch_sizes[i-1] = total;
     }
-    int64_t offset = 0;
     std::vector<torch::Tensor> tensors_to_be_cat;
     for (int64_t i = 1; i < static_cast<int64_t>(sorted_lengths.size() + 1); i++) {
       int64_t l = sorted_lengths.at(i-1);

test/cpp/api/optim.cpp

@@ -173,7 +173,7 @@ TEST(OptimTest, OptimizerAccessors) {
   const auto& optimizer_ = Adagrad(params, options);
   optimizer_.defaults();
   // test for param_groups() with const reference return
-  const auto& params_2 = optimizer_.param_groups();
+  (void)optimizer_.param_groups();
   // test for state() with const reference return
   optimizer_.state();
 }

test/cpp/jit/test_backend_compiler_lib.cpp

@@ -119,7 +119,6 @@ class BackendWithCompiler : public PyTorchBackendInterface {
       IValue val = token;
       auto instruction = val.toTupleRef().elements()[0].toStringRef();
       auto debug_handle = val.toTupleRef().elements()[1].toInt();
-      double const_val = 1.0;
 #ifndef NO_PROFILING
       auto start_time_us = torch::profiler::impl::getTime() / 1000;
 #endif
@@ -132,8 +131,6 @@ class BackendWithCompiler : public PyTorchBackendInterface {
               instruction);
           // NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
           auto sub = instruction.substr(15);
-          // NOLINTNEXTLINE(clang-analyzer-deadcode.DeadStores)
-          const_val = stod(sub);
         } else if (instruction == "aten::add" || instruction == "aten::sub") {
           TORCH_CHECK(x.sizes() == h.sizes());
           if (x.dim() > 1 || (x.dim() == 1 && x.size(0) > 1)) {

test/cpp/jit/test_fuser.cpp

@@ -122,11 +122,12 @@ TEST_F(FuserTest, TestOne_CUDA) {
     // with different internal strides.  To do this, we generate a tensor
     // with the "wrong" dimensions, and then use transpose to get an
     // appropriately sized view.
-    for (const auto i : c10::irange(graph.inputs().size())) {
+    std::generate_n(
-      std::vector<int64_t> dims = {128, 128, 32};
+        std::back_inserter(inputs), graph.inputs().size(), [ti, tj] {
-      std::swap(dims[ti], dims[tj]);
+          std::array<int64_t, 3> dims = {128, 128, 32};
-      inputs.push_back(at::rand(dims, at::kCUDA).transpose(ti, tj));
+          std::swap(dims[ti], dims[tj]);
-    }
+          return at::rand(dims, at::kCUDA).transpose(ti, tj);
+        });
 
     auto t22 = inputs[4].sigmoid();
     auto t20 = inputs[3].sigmoid();

test/cpp/jit/test_irparser.cpp

@@ -301,7 +301,6 @@ graph(%a : Float(4, 5),
 TEST(IRParserTest, MalformedStrides) {
   auto graph = std::make_shared<Graph>();
   std::unordered_map<std::string, Value*> vmap;
-  bool error_thrown = false;
   // NOLINTNEXTLINE(cppcoreguidelines-avoid-goto,hicpp-avoid-goto)
   EXPECT_ANY_THROW(parseIR(
       R"IR(

test/cpp/jit/test_lite_interpreter.cpp

@@ -1182,7 +1182,6 @@ TEST(RunTimeTest, ParseOperator) {
   std::vector<IValue> constants{
       to_tuple({1}),
   };
-  int64_t model_version = caffe2::serialize::kProducedBytecodeVersion;
   // 2. Parse the function
   std::string function_name("test_function");
   auto function = std::unique_ptr<mobile::Function>(
@@ -1566,7 +1565,6 @@ TEST(RunTimeTest, RuntimeCall) {
   std::vector<IValue> constantsCall{
       1,
   };
-  int64_t model_version = caffe2::serialize::kProducedBytecodeVersion;
 
   auto foo = std::make_unique<mobile::Function>(c10::QualifiedName("foo"));
   c10::ivalue::TupleElements debug_handles_m_tuple;

test/cpp/jit/test_misc.cpp

@@ -1049,8 +1049,7 @@ TEST(RecordFunctionTest, Callbacks) {
   GraphOptimizerEnabledGuard opt_guard(false);
 
   auto h1 = add_remove_test_add_cb<1>();
-  // NOLINTNEXTLINE(clang-analyzer-deadcode.DeadStores)
+  add_remove_test_add_cb<2>();
-  auto h2 = add_remove_test_add_cb<2>();
   auto h3 = add_remove_test_add_cb<3>();
 
   { RECORD_USER_SCOPE("test"); }
@@ -1144,7 +1143,6 @@ TEST(RecordFunctionTest, Callbacks) {
   } // END: global test
   { // START: thread local test
     auto ctx_th = std::thread([]() {
-      const int test_val = 234;
       const std::string test_str = "test thread str";
       addThreadLocalCallback(RecordFunctionCallback(
           [](const RecordFunction&
@@ -1417,22 +1415,6 @@ TEST(TestSymInt, AddSymbolicInt) {
 }
 
 TEST(FallbackGraphsTest, Basic) {
-  static const auto nestGraphIntoFallbackGraph =
-      [](const std::shared_ptr<Graph>& graph) {
-        ProfilingRecord::removeProfileCounter(graph->block());
-        auto fallback =
-            replaceBlockWithFallbackGraph(graph->block(), graph->inputs());
-        for (size_t i = 0; i < graph->outputs().size(); i++) {
-          graph->outputs()[i]->replaceAllUsesWith(fallback->output(i));
-          fallback->output(i)->copyMetadata(graph->outputs()[i]);
-        }
-        for (auto it = graph->block()->nodes().rbegin();
-             it != fallback->iterator();
-             it++) {
-          it.destroyCurrent();
-        }
-      };
-
   auto x = at::randn({1}, at::kCPU);
   auto y = at::randn({1}, at::kCPU);
   auto stack = createStack({x.clone(), y.clone()});
@@ -2656,11 +2638,13 @@ TEST(RecordDebugHandles, Basic) {
     RECORD_EDGE_SCOPE_WITH_DEBUG_HANDLE_AND_INPUTS("my_function", 42, {});
     float x{5.9999}, y{2.1212};
     float z = x / y;
+    (void)z;
   }
   {
     RECORD_USER_SCOPE_WITH_INPUTS("not_my_function", {});
     float x{5.9999}, y{2.1212};
     float z = x / y;
+    (void)z;
   }
   auto profiler_results_ptr = torch::autograd::profiler::disableProfiler();
   const auto& kineto_events = profiler_results_ptr->events();

test/cpp/tensorexpr/test_kernel.cpp

@@ -69,8 +69,6 @@ TEST_F(Kernel, InliningIntermediates) {
       const auto graph_string = format(graph_template, env);
       auto graph = std::make_shared<Graph>();
       parseIR(graph_string, &*graph);
-      // NOLINTNEXTLINE(clang-analyzer-deadcode.DeadStores)
-      auto device = use_cuda ? kCUDA : kCPU;
       TensorExprKernel k(graph);
       auto stmt = k.getCodeGenStmt();
       std::ostringstream oss;
@@ -80,8 +78,6 @@ TEST_F(Kernel, InliningIntermediates) {
 
       // aten_sub should be removed by the CUDA backend by metavar rewriting
       // and by the CPU backend by horizontal fusion.
-      // NOLINTNEXTLINE(clang-analyzer-deadcode.DeadStores,cppcoreguidelines-avoid-magic-numbers)
-      size_t num_out1_uses = use_cuda ? 0 : 5;
       torch::jit::testing::FileCheck().check_not("aten_sub")->run(oss.str());
     }
   }

test/cpp/tensorexpr/test_reductions.cpp

@@ -50,8 +50,6 @@ TEST(Reductions, ReduceSum0D_1) {
 }
 
 TEST(Reductions, ReduceSum0D_2) {
-  const int M = 10;
-
   BufHandle b("b", {}, kFloat);
   std::vector<float> in(1);
   in[0] = 77.7;

test/cpp/tensorexpr/test_type.cpp

@@ -68,15 +68,14 @@ TEST(Type, BitCasting) {
     ASSERT_EQ(y.dtype(), kShort);
   }
 
-  constexpr int16_t ref16 = 1337;
   constexpr int32_t ref32 = 1337;
   constexpr int64_t ref64 = 1337;
-  at::Half reff16 = 1337.0f;
   constexpr float reff32 = 1337.0f;
   constexpr double reff64 = 1337.0f;
   using SimpleIRExprEval = ExprEval<SimpleIREvaluator>;
   // this is broken
   /*{
+    constexpr int16_t ref16 = 1337;
     at::Half k_;
     at::Half* k = &k_;
     *reinterpret_cast<int16_t*>(k) = ref16;

torch/csrc/StorageMethods.cpp

@@ -82,8 +82,8 @@ static PyObject * THPStorage_copy_(PyObject *self, PyObject *args, PyObject *kwa
 static PyObject * THPStorage_isPinned(PyObject *_self, PyObject *noargs)
 {
   HANDLE_TH_ERRORS
-  auto self = (THPStorage*)_self;
 #if defined(USE_CUDA)
+  auto self = (THPStorage*)_self;
   return PyBool_FromLong(at::globalContext().isPinnedPtr(self->cdata->data<uint8_t>()));
 #else
   Py_RETURN_FALSE;
@@ -94,7 +94,6 @@ static PyObject * THPStorage_isPinned(PyObject *_self, PyObject *noargs)
 static PyObject * THPStorage_elementSize(PyObject *_self, PyObject *noargs)
 {
   HANDLE_TH_ERRORS
-  auto self = (THPStorage*)_self;
   return THPUtils_packInt64(sizeof(uint8_t));
   END_HANDLE_TH_ERRORS
 }
@@ -364,7 +363,6 @@ PyObject * THPStorage_newWithFile(PyObject *_unused, PyObject *args)
   HANDLE_TH_ERRORS
   TORCH_CHECK(PyTuple_Size(args) == 2,
       "_new_with_file takes exactly two arguments");
-  PyObject *fd_obj = PyTuple_GetItem(args, 0);
   int fd = PyObject_AsFileDescriptor(PyTuple_GetItem(args, 0));
   THPUtils_assert(fd != -1, "_new_with_file couldn't retrieve a file "
       "descriptor from given object");

torch/csrc/autograd/autograd_not_implemented_fallback.cpp

@@ -221,7 +221,6 @@ void autogradNotImplementedInplaceOrViewFallbackImpl(const c10::OperatorHandle&
   const auto num_arguments = arguments.size();
   const auto num_returns = returns.size();
   const auto stack_start = stack->size() - num_arguments;
-  bool any_is_inplace = false;
 
   at::Tensor aliased_input;
 
@@ -254,8 +253,6 @@ void autogradNotImplementedInplaceOrViewFallbackImpl(const c10::OperatorHandle&
         const c10::IValue& aliased_input_iv = (*stack)[stack_start + i]; // get a reference to an ivalue on the stack
         TORCH_CHECK(aliased_input_iv.isTensor());
         aliased_input = aliased_input_iv.toTensor();  // TODO: Can we avoid saving this tensor and incurring the refcount bump?
-      } else {
-        any_is_inplace = true;
       }
     }
   }

torch/csrc/jit/passes/onnx/function_extraction.cpp

@@ -553,7 +553,6 @@ Node* FunctionExtractor::CreateFunctionNode(
     const std::string& domain_name,
     const std::string& func_name) {
   const auto& func_scope = func_ctx.scope_key_;
-  const auto& func_scope_ctx = func_ctx.scope_ctxs_[func_scope];
   GRAPH_DEBUG(
       "Create and insert local function for scope: ",
       func_scope->namesFromRoot());
@@ -671,8 +670,7 @@ void FunctionExtractor::ConvertScopeToFunction(
   const auto func_name =
       construct_unique_module_name(module_class_name.substr(pos + 1));
 
-  auto func_def_n =
+  CreateFunctionDefNode(func_ctx, graph, domain_name, func_name);
-      CreateFunctionDefNode(func_ctx, graph, domain_name, func_name);
 
   // create and insert local function node to graph.
   for (const auto& it : func_ctx.scope_ctxs_) {

torch/csrc/jit/passes/onnx/helper.cpp

@@ -97,8 +97,7 @@ c10::optional<at::ScalarType> ONNXTypeToATenType(int32_t onnx_type) {
 Node* addNodeToBlock(Block* block, Symbol kind, ArrayRef<Value*> inputs) {
   auto new_node = block->appendNode(block->owningGraph()->create(kind));
   for (auto input : inputs) {
-    // NOLINTNEXTLINE(clang-analyzer-deadcode.DeadStores)
+    new_node->addInput(input);
-    auto new_input = new_node->addInput(input);
   }
   return new_node;
 }

torch/csrc/jit/passes/onnx/shape_type_inference.cpp

@@ -15,6 +15,7 @@
 #include <onnx/shape_inference/implementation.h>
 #include <algorithm>
 #include <cmath>
+#include <iterator>
 #include <limits>
 #include <unordered_set>
 #include <utility>
@@ -684,9 +685,8 @@ std::vector<::c10::ShapeSymbol> Broadcast(
   size_t rank_min = std::min(rank_0, rank_1);
   std::vector<::c10::ShapeSymbol> final_shape;
   final_shape.reserve(rank_max);
-  for (auto idx : c10::irange(rank_max)) {
+  std::generate_n(
-    final_shape.emplace_back(::c10::ShapeSymbol::newSymbol());
+      std::back_inserter(final_shape), rank_max, ::c10::ShapeSymbol::newSymbol);
-  }
   for (auto idx : c10::irange(rank_min)) {
     const c10::ShapeSymbol& ss_shape_0 = input_shape_value_0[rank_0 - 1 - idx];
     const c10::ShapeSymbol& ss_shape_1 = input_shape_value_1[rank_1 - 1 - idx];
@@ -1366,9 +1366,10 @@ void ComputeConstant(Node* n, int opset_version) {
                 expand_shape.value().size());
             if (expand_shape.value()[0] > 0) {
               std::vector<c10::ShapeSymbol> final_shape;
-              for (const auto i : c10::irange(expand_shape.value()[0])) {
+              std::generate_n(
-                final_shape.emplace_back(c10::ShapeSymbol::newSymbol());
+                  std::back_inserter(final_shape),
-              }
+                  expand_shape.value()[0],
+                  ::c10::ShapeSymbol::newSymbol);
               UpdateShape(n->output(), c10::SymbolicShape(final_shape));
             }
           }
@@ -1607,7 +1608,6 @@ void SpecialPostProcess(Node* n) {
       // onnx Sequence type requires element type to be set.
       // If the list to insert is empty, we set the elem type by
       // looking at the tensor being inserted.
-      auto list_node = n->input(0)->node();
       auto seq_node = n->input(0)->node();
       auto t_type = n->input(1)->type()->cast<TensorType>();
 

torch/csrc/jit/python/python_ir.cpp

@@ -608,7 +608,7 @@ void initPythonIRBindings(PyObject* module_) {
           "schema",
           [](Node& n) {
             std::stringstream ss;
-            if (auto sch = n.maybeSchema()) {
+            if (n.maybeSchema()) {
               ss << n.schema();
             } else {
               ss << "(no schema)";

torch/csrc/jit/python/python_sugared_value.cpp

@@ -1191,8 +1191,10 @@ std::shared_ptr<SugaredValue> toSugaredValue(
     obj = py::getattr(obj, "op");
   }
 
+#ifdef USE_RPC
   bool isRpcAvailable = py::cast<bool>(
       py::module::import("torch.distributed.rpc").attr("is_available")());
+#endif
 
   if (auto callee = as_function(obj)) {
     return std::make_shared<FunctionValue>(callee->function_);

torch/csrc/utils.cpp

@@ -1,4 +1,3 @@
-#include <c10/util/irange.h>
 #include <torch/csrc/python_headers.h>
 #include <torch/csrc/THP.h>
 #include <torch/csrc/utils/python_strings.h>
@@ -10,6 +9,7 @@
 
 #include <algorithm>
 #include <cstdarg>
+#include <iterator>
 #include <sstream>
 #include <string>
 #include <unordered_map>
@@ -129,9 +129,9 @@ void THPUtils_invalidArguments(PyObject *given_args, PyObject *given_kwargs,
   std::vector<std::string> option_strings;
   va_list option_list;
   va_start(option_list, num_options);
-  // NOLINTNEXTLINE(clang-analyzer-deadcode.DeadStores)
+  std::generate_n(std::back_inserter(option_strings), num_options, [&option_list] {
-  for(const auto i : c10::irange(num_options))
+    return va_arg(option_list, const char*);
-    option_strings.emplace_back(va_arg(option_list, const char*));
+  });
   va_end(option_list);
 
   PyErr_SetString(PyExc_TypeError, torch::format_invalid_args(

torch/lib/libshm/manager.cpp

@@ -55,12 +55,8 @@ void unregister_fd(int fd) {
 }
 
 void print_init_message(const char* message) {
-  // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
+  write(1, message, strlen(message));
-  size_t unused;
+  write(1, "\n", 1);
-  // NOLINTNEXTLINE(clang-analyzer-deadcode.DeadStores)
-  unused = write(1, message, strlen(message));
-  // NOLINTNEXTLINE(clang-analyzer-deadcode.DeadStores)
-  unused = write(1, "\n", 1);
 }
 
 bool object_exists(const char* name) {