[2/N] Fix some violations of unused-function and unused-variable checks in torch_cpu (#129878)

Follows #128670

Pull Request resolved: https://github.com/pytorch/pytorch/pull/129878
Approved by: https://github.com/ezyang

aten/src/ATen/MapAllocator.cpp

@@ -275,7 +275,9 @@ MapAllocator::MapAllocator(WithFd, c10::string_view filename, int fd, int flags,
 
     struct stat file_stat{};
     if (fstat(fd, &file_stat) == -1) {
+#ifndef STRIP_ERROR_MESSAGES
       int last_err = errno;
+#endif
       if (!(flags_ & ALLOCATOR_MAPPED_FROMFD)) {
         ::close(fd);
       }
@@ -289,7 +291,9 @@ MapAllocator::MapAllocator(WithFd, c10::string_view filename, int fd, int flags,
             TORCH_CHECK(false, "unable to resize file <", filename_, "> to the right size: ", strerror(errno), " (", errno, ")");
           }
           if (fstat(fd, &file_stat) == -1 || file_stat.st_size < static_cast<int64_t>(size)) {
+#ifndef STRIP_ERROR_MESSAGES
             int last_err = errno;
+#endif
             ::close(fd);
             TORCH_CHECK(false, "unable to stretch file <", filename_, "> to the right size: ", strerror(last_err), " (", last_err, ")");
           }
@@ -298,7 +302,9 @@ MapAllocator::MapAllocator(WithFd, c10::string_view filename, int fd, int flags,
  */
 #ifndef __APPLE__
           if ((write(fd, "", 1)) != 1) /* note that the string "" contains the '\0' byte ... */ {
+#ifndef STRIP_ERROR_MESSAGES
             int last_err = errno;
+#endif
             ::close(fd);
             TORCH_CHECK(false, "unable to write to file <", filename_, ">: ", strerror(last_err), " (", last_err, ")");
           }

aten/src/ATen/core/dispatch/OperatorEntry.cpp

@@ -7,6 +7,7 @@ namespace c10 {
 namespace impl {
 
 namespace {
+#ifndef STRIP_ERROR_MESSAGES
   std::string toString(std::optional<DispatchKey> k) {
     if (k.has_value()) {
       return toString(*k);
@@ -14,6 +15,7 @@ namespace {
       return "(catch all)";
     }
   }
+#endif
 }
 
 OperatorEntry::OperatorEntry(OperatorName&& operator_name)
@@ -510,6 +512,7 @@ void OperatorEntry::reportSignatureError(const CppSignature& call_signature, con
   );
 };
 
+#ifndef STRIP_ERROR_MESSAGES
 static std::string post_process_dispatch_key_str(std::string dispatch_key) {
   const std::string substr = "PrivateUse1";
   if (substr.size() <= dispatch_key.size() && std::equal(substr.rbegin(), substr.rend(), dispatch_key.rbegin())) {
@@ -525,6 +528,7 @@ static std::string post_process_dispatch_key_str(std::string dispatch_key) {
   }
   return dispatch_key;
 }
+#endif
 
 void OperatorEntry::reportError(DispatchKey dispatchKey) const {
   // If there is an invariant problem, report it now.

aten/src/ATen/native/DistributionTemplates.h

@@ -98,7 +98,7 @@ at::Tensor& random_impl(at::Tensor& self, std::optional<Generator> generator) {
       "This warning will become an error in version 1.7 release, please fix the code in advance"); \
   }
 
-static void check_from_to_in_range(int64_t from, int64_t to_inc, caffe2::TypeMeta dtype) {
+inline void check_from_to_in_range(int64_t from, int64_t to_inc, caffe2::TypeMeta dtype) {
   const auto scalar_type = typeMetaToScalarType(dtype);
   if (isFloatingType(scalar_type)) {
     AT_DISPATCH_FLOATING_TYPES_AND2(at::ScalarType::Half, at::ScalarType::BFloat16, scalar_type, "check_random_fp_bounds", [&] {
@@ -290,7 +290,7 @@ at::Tensor& uniform_impl_(at::Tensor& self, double from, double to, std::optiona
     uniform_impl_<uniform_kernel, RNG>(float_tensor, from, to, generator);
   } else {
     AT_DISPATCH_FLOATING_TYPES_AND2(at::ScalarType::Half, at::ScalarType::BFloat16, self.scalar_type(), "check_uniform_bounds", [&] {
-      const auto dtype = self.dtype();
+      [[maybe_unused]] const auto dtype = self.dtype();
       const auto min = static_cast<double>(std::numeric_limits<scalar_t>::lowest());
       const auto max = static_cast<double>(std::numeric_limits<scalar_t>::max());
       CHECK_OUT_OF_BOUNDS(from, "from", min, max, dtype);

aten/src/ATen/native/Linear.cpp

@@ -278,10 +278,12 @@ Tensor einsum(c10::string_view equation, TensorList operands, at::OptionalIntArr
     return std::isupper(label) ? label - 'A' : label - 'a' + NUM_OF_LETTERS;
   };
 
+#ifndef STRIP_ERROR_MESSAGES
   // Convert subscript in [0, TOTAL_LABELS) to label in [A-Za-z]
   auto subscript_to_label = [=](uint8_t s) -> unsigned char {
     return s < NUM_OF_LETTERS ? s + 'A' : s + 'a' - NUM_OF_LETTERS;
   };
+#endif
 
   // Find arrow (->) to split equation into lhs and rhs
   const auto arrow_pos = equation.find("->");

aten/src/ATen/native/LinearAlgebra.cpp

@@ -3546,7 +3546,9 @@ Tensor _weight_int8pack_mm_cpu(
 }
 
 Tensor& _int_mm_out_cpu(const Tensor& self, const Tensor& mat2, Tensor& result) {
+#ifndef STRIP_ERROR_MESSAGES
   static constexpr c10::string_view func_name = "int_mm_out_cpu";
+#endif
   TORCH_CHECK(self.dim() == 2, func_name, ": Expected self to be of dimension 2 but got ", self.dim());
   TORCH_CHECK(mat2.dim() == 2, func_name, ": Expected mat2 to be of dimension 2 but got ", mat2.dim());
   TORCH_CHECK(self.size(1) == mat2.size(0), func_name, ": self.size(1) needs to match mat2.size(0) but got ", self.size(1), " and ", mat2.size(0));

aten/src/ATen/native/LossMultiLabelMargin.cpp

@@ -114,7 +114,9 @@ static void multilabel_margin_loss_forward_out_cpu_template(
     Tensor& output,
     Tensor& is_target,
     int64_t reduction) {
+#ifndef STRIP_ERROR_MESSAGES
   auto target_arg = TensorArg(target, "target", 2);
+#endif
   // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
   int64_t nframe, dim;
   const int64_t ndims = input.dim();
@@ -161,7 +163,9 @@ static void multilabel_margin_loss_backward_out_frame(
     const Tensor& is_target_contiguous,
     int64_t nframe,
     int64_t dim) {
+#ifndef STRIP_ERROR_MESSAGES
   auto is_target_arg = TensorArg(is_target_contiguous, "is_target", 5);
+#endif
 
   TORCH_CHECK(
       is_target_contiguous.min().item<scalar_t>() >= 0, is_target_arg, " is out of range");

aten/src/ATen/native/NamedTensor.cpp

@@ -60,8 +60,10 @@ static void report_moving_unnamed_dim_error(
 static void report_not_a_subsequence_error(
     DimnameList names, DimnameList other, bool is_aligning_two_tensors) {
   if (is_aligning_two_tensors) {
+#ifndef STRIP_ERROR_MESSAGES
     auto shorter = names.size() > other.size() ? other : names;
     auto longer = names.size() > other.size() ? names : other;
+#endif
     TORCH_CHECK(false,
         "Could not align Tensor", shorter, " and Tensor", longer,
         " because ", shorter, " is not a subsequence of ", longer, ". ");

aten/src/ATen/native/Pool.h

@@ -126,7 +126,9 @@ pool2d_shape_check(
   int64_t outputHeight, int64_t outputWidth, MemoryFormat memory_format)
 {
   const int64_t ndim = input.ndimension();
+#ifndef STRIP_ERROR_MESSAGES
   const int64_t nOutputPlane = nInputPlane;
+#endif
 
   TORCH_CHECK(kW > 0 && kH > 0,
               "kernel size should be greater than zero, but got ",

aten/src/ATen/native/TensorAdvancedIndexingUtils.h

@@ -5,7 +5,8 @@
 
 namespace at::native {
 namespace {
-static std::string shapes_as_str(TensorList tensors) {
+#ifndef STRIP_ERROR_MESSAGES
+inline std::string shapes_as_str(TensorList tensors) {
   std::ostringstream os;
   bool first = true;
   for (auto& tensor : tensors) {
@@ -19,9 +20,10 @@ static std::string shapes_as_str(TensorList tensors) {
   }
   return os.str();
 }
+#endif
 } // anonymous namespace
 
-static std::tuple<bool, Tensor> canDispatchToMaskedFill(const Tensor& self, const torch::List<std::optional<at::Tensor>>& indices,
+inline std::tuple<bool, Tensor> canDispatchToMaskedFill(const Tensor& self, const torch::List<std::optional<at::Tensor>>& indices,
 const Tensor& value){
   if (!(value.numel() ==1 && value.device().is_cpu())){
     return std::make_tuple(false,Tensor());
@@ -54,7 +56,7 @@ const Tensor& value){
   return std::make_tuple(true, mask);
 }
 
-static AdvancedIndex make_info(Tensor self, IOptTensorListRef orig) {
+inline AdvancedIndex make_info(Tensor self, IOptTensorListRef orig) {
   checkIndexTensorTypes(orig, /*allow_int*/ true);
   // first expand BoolTensor (masks) or ByteTensor (masks) into 1 or more LongTensors
   auto indices = expandTensors(self, orig);

aten/src/ATen/native/TensorConversions.cpp

@@ -673,7 +673,7 @@ Tensor sparse_compressed_to_dense(
   // dims into a single dim, so that the remaining dims are only block
   // dims eventually, and then dense dims.
   auto n_batch = values.size(0);
-  int64_t nrows, ncols;
+  int64_t nrows = 0, ncols = 0;
   auto dense_reshaped_sizes = dense.sizes().vec();
   if (!block_sparse) {
     nrows = self.size(batch_ndim);
@@ -928,7 +928,6 @@ static std::pair<Tensor, Tensor> _not_zero_mask_to_col_row_indices(
 static inline
 void _to_sparse_check_arguments(const std::string& funcname, const Tensor& self, const int64_t sparse_dim) {
   auto layout_from = self.layout();
-  auto layout_to = kSparse;
 
   auto layout_from_valid = layout_from == kStrided || layout_from == kSparse || at::sparse_csr::is_sparse_compressed(layout_from);
   if (!layout_from_valid) {
@@ -944,11 +943,11 @@ void _to_sparse_check_arguments(const std::string& funcname, const Tensor& self,
     }
   } else if (layout_from == kSparse) {
     if (sparse_dim != self.sparse_dim()) {
-      AT_ERROR(funcname, ": conversion from ", layout_from, " to ", layout_to, " with sparse_dim argument !=self.sparse_dim() is not supported");
+      AT_ERROR(funcname, ": conversion from ", layout_from, " to ", kSparse, " with sparse_dim argument !=self.sparse_dim() is not supported");
     }
   } else if (at::sparse_csr::is_sparse_compressed(layout_from)) {
     if (sparse_dim != 2) {
-      AT_ERROR(funcname, ": conversion from ", layout_from, " to ", layout_to, " with sparse_dim argument !=2 is not supported");
+      AT_ERROR(funcname, ": conversion from ", layout_from, " to ", kSparse, " with sparse_dim argument !=2 is not supported");
     }
   }
 }
@@ -1758,7 +1757,7 @@ Tensor _compressed_to_block_compressed_cpu(const Tensor& self, IntArrayRef block
   // First we determine the number of blocks needed. For each given
   // block, if it contains a non-zero element we will allocate values
   // and indices for it.
-  int64_t num_blocks;
+  int64_t num_blocks = 0;
   auto compressed_dim = (target_layout == Layout::SparseBsr) ? self.size(0) : self.size(1);
   auto plain_dim = (target_layout == Layout::SparseBsr) ? self.size(1) : self.size(0);
   auto compressed_blocksize = (target_layout == Layout::SparseBsr) ? blocksize[0] : blocksize[1];
@@ -1859,10 +1858,9 @@ Tensor sparse_compressed_to_sparse_bsc(const Tensor& self, IntArrayRef blocksize
 }
 
 Tensor sparse_coo_to_sparse(const Tensor& self, const int64_t sparse_dim) {
-  auto layout_to = kSparse;
   _to_sparse_check_arguments("sparse_coo_to_sparse", self, sparse_dim);
 
-  AT_ERROR("sparse_coo_to_sparse: ", self.layout(), " to ", layout_to, " conversion not supported");
+  AT_ERROR("sparse_coo_to_sparse: ", self.layout(), " to ", kSparse, " conversion not supported");
   return Tensor{};
 }
 

aten/src/ATen/native/TensorShape.cpp

@@ -3026,6 +3026,7 @@ static inline Tensor sparse_compressed_transpose(
 
   const auto transpose_type = classify_dim(dim0);
   {
+#ifndef STRIP_ERROR_MESSAGES
     auto dim_type_name = [](const TransposeDim dim) {
       switch (dim) {
         case TransposeDim::Batch:
@@ -3041,6 +3042,7 @@ static inline Tensor sparse_compressed_transpose(
               static_cast<std::underlying_type_t<TransposeDim>>(dim));
       }
     };
+#endif
     const auto dim1_type = classify_dim(dim1);
     TORCH_CHECK(
         dim1_type == transpose_type,

aten/src/ATen/native/cpu/MaxUnpoolKernel.cpp

@@ -37,8 +37,8 @@ void cpu_max_unpool(
 
   // treat batch size and channels as one dimension
   // and the feature map as another dimension
-  int64_t channels, output_depth, output_height, output_width;
-  if (is_3d) {
+  [[maybe_unused]] int64_t channels, output_depth, output_height, output_width;
+  if constexpr (is_3d) {
     TORCH_CHECK(ndim == 4 || ndim == 5, "MaxUnpool3d: expect input to be 4d or 5d tensor.");
     channels = ndim == 4 ? input.size(0) : input.size(0) * input.size(1);
     output_depth = output.size(-3);
@@ -79,7 +79,7 @@ void cpu_max_unpool(
   });
 
   if (optional_error_index) {
-    if (is_3d) {
+    if constexpr (is_3d) {
       AT_ERROR("Found an invalid max index: ", optional_error_index.value(),
           " (output volumes are of size ", output_depth,
           "x", output_height, "x", output_width);

aten/src/ATen/native/cpu/SerialStackImpl.h

@@ -91,7 +91,9 @@ bool can_use_native_serial_stack_impl(Tensor& result, TensorListType tensors, in
   }
 
   // check remainder of inputs
+#ifndef STRIP_ERROR_MESSAGES
   auto const &first_tensor_shape = first_tensor.sizes();
+#endif
   for (const auto i : c10::irange(1, tensors.size())) {
     auto const &tensor = tensors[i];
     TORCH_CHECK(tensors[i].sizes() == first_tensor.sizes(),

aten/src/ATen/native/quantized/cpu/qconv.cpp

@@ -1793,7 +1793,9 @@ class QConvAddInt8 final {
       const c10::intrusive_ptr<ConvPackedParamsBase<kSpatialDim>>& packed_weight,
       double output_scale,
       int64_t output_zero_point) {
+#if AT_MKLDNN_ENABLED() || !defined(STRIP_ERROR_MESSAGES)
     auto& ctx = at::globalContext();
+#endif
 #if AT_MKLDNN_ENABLED()
     if (ctx.qEngine() == at::QEngine::ONEDNN) {
       if (kReluFused) {

aten/src/ATen/native/quantized/cpu/qlinear.cpp

@@ -1131,7 +1131,9 @@ class QLinearLeakyReluInt8 final {
       double output_scale,
       int64_t output_zero_point,
       double negative_slope) {
+#if AT_MKLDNN_ENABLED() || !defined(STRIP_ERROR_MESSAGES)
     auto& ctx = at::globalContext();
+#endif
 #if AT_MKLDNN_ENABLED()
     if (ctx.qEngine() == at::QEngine::ONEDNN) {
       return dynamic_cast<PackedLinearWeightsOnednn*>(packed_weight.get())->apply_leaky_relu(
@@ -1153,7 +1155,9 @@ class QLinearTanhInt8 final {
       const c10::intrusive_ptr<LinearPackedParamsBase>& packed_weight,
       double output_scale,
       int64_t output_zero_point) {
+#if AT_MKLDNN_ENABLED() || !defined(STRIP_ERROR_MESSAGES)
     auto& ctx = at::globalContext();
+#endif
 #if AT_MKLDNN_ENABLED()
     if (ctx.qEngine() == at::QEngine::ONEDNN) {
       return dynamic_cast<PackedLinearWeightsOnednn*>(packed_weight.get())->apply_tanh(

aten/src/ATen/native/sparse/SparseCsrTensor.cpp

@@ -938,7 +938,9 @@ Tensor empty_like_sparse_csr(
 
 template <bool require_view, bool require_copy>
 Tensor select_sparse_csr_worker(const Tensor& self, int64_t dim, int64_t index) {
+#ifndef STRIP_ERROR_MESSAGES
   constexpr const char* select_name = (require_view ? "select()" : "select_copy()");
+#endif
   AT_DISPATCH_ALL_SPARSE_COMPRESSED_LAYOUTS(
       self.layout(), "select", []() { return; });
   TORCH_CHECK_INDEX(

torch/csrc/autograd/custom_function.cpp

@@ -264,11 +264,13 @@ static optional_variable_list _process_backward_mode_ad(
     const _view_as_self_fn_t& view_as_self_fn) {
   auto num_outputs = raw_outputs.size();
 
+#ifndef STRIP_ERROR_MESSAGES
   const char* error_msg_input_returned_as_is =
       "A input that has been returned as-is as output is being saved for backward. "
       "This is not supported if you override setup_context. You should return and "
       "save a view of the input instead, e.g. with x.view_as(x) or setup ctx inside "
       "the forward function itself.";
+#endif
 
   // Sets the grad_fn and output_nr of an output Variable.
   auto set_history = [&](Variable& var,

torch/csrc/jit/mobile/interpreter.cpp

@@ -358,8 +358,7 @@ bool InterpreterState::run(Stack& stack) {
           // when STRIP_ERROR_MESSAGES is defined (which happens for production
           // mobile builds). This will cause the stack to be in an inconsistent
           // state. It has previously resulted in a SEV (S22350).
-          const auto& sref = stack.back().toStringRef();
-          TORCH_WARN(sref);
+          TORCH_WARN(stack.back().toStringRef());
           stack.pop_back();
           frame.step();
         } break;

torch/csrc/jit/runtime/interpreter.cpp

@@ -845,9 +845,8 @@ struct InterpreterStateImpl : c10::intrusive_ptr_target {
               }
               stack.pop_back();
             } else {
-              const auto& msg = stack.back().toStringRef();
               if (need_warn) {
-                TORCH_WARN(msg);
+                TORCH_WARN(stack.back().toStringRef());
               }
               stack.pop_back();
             }

torch/csrc/jit/runtime/static/impl.cpp

@@ -55,12 +55,13 @@ C10_DEFINE_bool(
 namespace torch::jit {
 
 namespace {
-
+#ifndef STRIP_ERROR_MESSAGES
 std::string iValueToString(const c10::IValue& val) {
   std::ostringstream oss;
   oss << val;
   return oss.str();
 }
+#endif
 
 bool allArgsAreTensors(const Node* node) {
   const auto& inputs = node->inputs();

torch/csrc/jit/serialization/import.cpp

@@ -44,7 +44,9 @@ static void postSetStateValidate(const IValue& v) {
   const auto& objType = obj->type();
   for (const auto i : c10::irange(objType->numAttributes())) {
     const auto& attrType = objType->getAttribute(i);
+#ifndef STRIP_ERROR_MESSAGES
     const auto& attrName = objType->getAttributeName(i);
+#endif
     const auto& slot = obj->getSlot(i);
     // const auto attrType = objType->getAttribute(i);
     // Verify that all the non-optional attributes have been initialized

torch/csrc/profiler/unwind/unwind.cpp

@@ -232,7 +232,7 @@ struct UnwindCache {
   const LibraryInfo& libraryFor(uint64_t addr) {
     auto* r = findLibraryFor(addr);
     if (!r) {
-      for (const auto& l : libraries_with_no_unwind_) {
+      for ([[maybe_unused]] const auto& l : libraries_with_no_unwind_) {
         TORCH_WARN("Did not find a PT_GNU_EH_FRAME segment for ", l);
       }
       libraries_with_no_unwind_.clear();

torch/custom_class.h

@@ -335,11 +335,13 @@ class class_ : public ::torch::detail::class_base {
 
     // type validation
     auto getstate_schema = classTypePtr->getMethod("__getstate__").getSchema();
+#ifndef STRIP_ERROR_MESSAGES
     auto format_getstate_schema = [&getstate_schema]() {
       std::stringstream ss;
       ss << getstate_schema;
       return ss.str();
     };
+#endif
     TORCH_CHECK(
         getstate_schema.arguments().size() == 1,
         "__getstate__ should take exactly one argument: self. Got: ",