diff --git a/aten/src/ATen/native/AdaptiveMaxPooling3d.cpp b/aten/src/ATen/native/AdaptiveMaxPooling3d.cpp
index 001e3c7d2d56..1c037c31c6f6 100644
--- a/aten/src/ATen/native/AdaptiveMaxPooling3d.cpp
+++ b/aten/src/ATen/native/AdaptiveMaxPooling3d.cpp
@@ -100,7 +100,7 @@ static void adaptive_max_pool3d_single_out_frame(
   at::parallel_for(0, sizeD, 0, [&](int64_t start, int64_t end) {
     for (const auto d : c10::irange(start, end)) {
       /* loop over output */
-      int64_t ot, oh, ow;
+      int64_t ot = 0, oh = 0, ow = 0;
       for(ot = 0; ot < osizeT; ot++)
       {
         int64_t istartT = start_index(ot, osizeT, isizeT);
@@ -209,7 +209,7 @@ static void adaptive_max_pool3d_backward_single_out_frame(
       const int64_t *ind_p_d = ind_p + d*osizeT*osizeH*osizeW;
 
       /* calculate max points */
-      int64_t ot, oh, ow;
+      int64_t ot = 0, oh = 0, ow = 0;
       for(ot = 0; ot < osizeT; ot++)
       {
         for(oh = 0; oh < osizeH; oh++)
@@ -357,13 +357,13 @@ TORCH_IMPL_FUNC(adaptive_max_pool3d_backward_out_cpu)
   int dimH = 2;
   int dimW = 3;
   int64_t sizeB = 1;
-  int64_t sizeD;
-  int64_t isizeT;
-  int64_t isizeH;
-  int64_t isizeW;
-  int64_t osizeT;
-  int64_t osizeH;
-  int64_t osizeW;
+  int64_t sizeD = 0;
+  int64_t isizeT = 0;
+  int64_t isizeH = 0;
+  int64_t isizeW = 0;
+  int64_t osizeT = 0;
+  int64_t osizeH = 0;
+  int64_t osizeW = 0;
 
   /* get contiguous gradOutput */
   auto gradOutput_ = gradOutput.contiguous();
diff --git a/aten/src/ATen/native/BatchLinearAlgebra.cpp b/aten/src/ATen/native/BatchLinearAlgebra.cpp
index 036b7b40c20a..71011ab92d58 100644
--- a/aten/src/ATen/native/BatchLinearAlgebra.cpp
+++ b/aten/src/ATen/native/BatchLinearAlgebra.cpp
@@ -1508,7 +1508,7 @@ void _linalg_check_errors(
     return;
   }
 
-  int32_t info;
+  int32_t info = 0;
   std::string batch_str;
   if (is_matrix) {
     info = infos.item<int>();
@@ -2079,7 +2079,7 @@ TORCH_IMPL_FUNC(lu_unpack_out)(const Tensor& LU,
       .set_check_mem_overlap(false)
       .check_all_same_dtype(false)
       .resize_outputs(false)
-      .declare_static_shape(pivots.sizes(), /*squash_dim=*/pivots.dim() - 1)
+      .declare_static_shape(pivots.sizes(), /*squash_dims=*/pivots.dim() - 1)
       .add_output(perm)
       .add_owned_const_input(pivots.contiguous())
       .build();
diff --git a/aten/src/ATen/native/BatchLinearAlgebraKernel.cpp b/aten/src/ATen/native/BatchLinearAlgebraKernel.cpp
index 79e7b8b04938..f16457f067bb 100644
--- a/aten/src/ATen/native/BatchLinearAlgebraKernel.cpp
+++ b/aten/src/ATen/native/BatchLinearAlgebraKernel.cpp
@@ -500,8 +500,8 @@ void apply_lstsq(const Tensor& A, Tensor& B, Tensor& rank, Tensor& singular_valu
   auto infos_data = infos.data_ptr<int>();
 
   // only 'gels' driver does not compute the rank
-  int rank_32;
-  int64_t* rank_data;
+  int rank_32 = 0;
+  int64_t* rank_data = nullptr;
   int64_t* rank_working_ptr = nullptr;
   if (driver_t::Gels != driver_type) {
     rank_data = rank.data_ptr<int64_t>();
@@ -510,9 +510,9 @@ void apply_lstsq(const Tensor& A, Tensor& B, Tensor& rank, Tensor& singular_valu
 
   // 'gelsd' and 'gelss' are SVD-based algorithms
   // so we can get singular values
-  value_t* s_data;
+  value_t* s_data = nullptr;
   value_t* s_working_ptr = nullptr;
-  int64_t s_stride;
+  int64_t s_stride = 0;
   if (driver_t::Gelsd == driver_type || driver_t::Gelss == driver_type) {
     s_data = singular_values.data_ptr<value_t>();
     s_working_ptr = s_data;
@@ -531,7 +531,7 @@ void apply_lstsq(const Tensor& A, Tensor& B, Tensor& rank, Tensor& singular_valu
   int lwork = -1; // default value to decide the opt size for workspace arrays
   scalar_t work_opt;
   value_t rwork_opt;
-  int iwork_opt;
+  int iwork_opt = 0;
   lapack_func(trans, m, n, nrhs,
     A_data, lda,
     B_data, ldb,
@@ -550,9 +550,9 @@ void apply_lstsq(const Tensor& A, Tensor& B, Tensor& rank, Tensor& singular_valu
 
   // 'rwork' only used for complex inputs and 'gelsy', 'gelsd' and 'gelss' drivers
   Tensor rwork;
-  value_t* rwork_data;
+  value_t* rwork_data = nullptr;
   if (A.is_complex() && driver_t::Gels != driver_type) {
-    int64_t rwork_len;
+    int64_t rwork_len = 0;
     switch (driver_type) {
       case driver_t::Gelsy:
         rwork_len = std::max<int64_t>(1, 2 * n);
@@ -570,7 +570,7 @@ void apply_lstsq(const Tensor& A, Tensor& B, Tensor& rank, Tensor& singular_valu
 
   // 'iwork' workspace array is relevant only for 'gelsd'
   Tensor iwork;
-  int* iwork_data;
+  int* iwork_data = nullptr;
   if (driver_t::Gelsd == driver_type) {
     iwork = at::empty({std::max<int>(1, iwork_opt)}, A.options().dtype(at::kInt));
     iwork_data = iwork.mutable_data_ptr<int>();
diff --git a/aten/src/ATen/native/BinaryOps.cpp b/aten/src/ATen/native/BinaryOps.cpp
index 3fe3ac2b4a25..f2e45442d189 100644
--- a/aten/src/ATen/native/BinaryOps.cpp
+++ b/aten/src/ATen/native/BinaryOps.cpp
@@ -1417,7 +1417,7 @@ Tensor& comparison_op_(Tensor& self, const Scalar& other, OutImpl& out_impl) {
 
 // We need explicit cast to OutFunc because each *_out func is overloaded twice. Without An explicit cast, merely
 // referring to *_out function is ambiguous.
-using OutFunc = std::add_const<Tensor&(&)(Tensor&, const Tensor&, const Tensor&)>::type;
+using OutFunc = std::add_const_t<Tensor&(&)(Tensor&, const Tensor&, const Tensor&)>;
 
 // less, alias for torch.lt
 Tensor& less_out(const Tensor& self, const Tensor& other, Tensor& result) { return at::lt_out(result, self, other); }
diff --git a/aten/src/ATen/native/Normalization.cpp b/aten/src/ATen/native/Normalization.cpp
index d9637f365c9b..1fb64c323705 100644
--- a/aten/src/ATen/native/Normalization.cpp
+++ b/aten/src/ATen/native/Normalization.cpp
@@ -87,12 +87,12 @@ DEFINE_DISPATCH(batch_norm_cpu_backward_stub);
 DEFINE_DISPATCH(renorm_scale_factor_stub);
 
 namespace {
-  void check_dims_match_num_input_features(const char* arg_name, SymInt expected, SymInt actual){
+  void check_dims_match_num_input_features(const char* arg_name, const SymInt& expected, const SymInt& actual){
     TORCH_CHECK(actual == expected,
              arg_name, " should contain ", expected, " elements not ", actual);
   }
 
-  static inline Tensor repeat_if_defined(const Tensor& t, SymInt repeat) {
+  static inline Tensor repeat_if_defined(const Tensor& t, const SymInt& repeat) {
     if (t.defined()) {
       return t.repeat_symint(repeat);
     }
@@ -173,7 +173,7 @@ std::tuple<Tensor,Tensor,Tensor> batch_norm_cpu_transform_input_template(
       return 1 / at::sqrt(running_var + eps);
     }
   }());
-  constexpr bool mixed_type = !std::is_same<scalar_t, param_t>::value;
+  constexpr bool mixed_type = !std::is_same_v<scalar_t, param_t>;
   const auto dtype = mixed_type ? kFloat : input.scalar_type();
   auto w = weight.defined() ? as_nd(weight) :
       at::detail::scalar_tensor_static(1, dtype, kCPU);
@@ -208,7 +208,7 @@ std::tuple<Tensor,Tensor> batch_norm_cpu_update_stats_template(
   int64_t n = input.numel() / n_input;
 
   bool all_contiguous = is_contiguous(input);
-  constexpr bool mixed_type = !std::is_same<scalar_t, param_t>::value;
+  constexpr bool mixed_type = !std::is_same_v<scalar_t, param_t>;
   const auto dtype = mixed_type ? kFloat : input.scalar_type();
 
   auto save_mean_a = save_mean.accessor<param_t, 1>();
@@ -292,7 +292,7 @@ std::tuple<Tensor,Tensor> batch_norm_cpu_update_stats_template(
     reduce_dims[i - 1] = i;
   }
 
-  constexpr bool mixed_type = !std::is_same<scalar_t, param_t>::value;
+  constexpr bool mixed_type = !std::is_same_v<scalar_t, param_t>;
   const auto dtype = mixed_type ? kFloat : input.scalar_type();
   Tensor save_mean = is_contiguous(input) ? at::empty({n_input}, input.options().dtype(dtype)) : at::mean(input, /*dim=*/reduce_dims, /*keepdim=*/false, dtype);
   Tensor save_var_transform = at::empty({n_input}, input.options().dtype(dtype));
@@ -307,7 +307,7 @@ std::tuple<Tensor, Tensor, Tensor> batch_norm_backward_cpu_template(
 
   using accscalar_t = at::acc_type<scalar_t, false>;
 
-  constexpr bool mixed_type = !std::is_same<scalar_t, param_t>::value;
+  constexpr bool mixed_type = !std::is_same_v<scalar_t, param_t>;
   const auto dtype = mixed_type ? kFloat : input.scalar_type();
 
   Tensor grad_input;
@@ -360,7 +360,7 @@ std::tuple<Tensor, Tensor, Tensor> batch_norm_backward_cpu_template(
     reduce_dims[i - 1] = i;
   }
 
-  auto sum = at::sum(grad_out_, /*dims=*/reduce_dims);
+  auto sum = at::sum(grad_out_, /*dim=*/reduce_dims);
   auto sum_a = sum.accessor<scalar_t, 1>();
 
   auto reduce_iter = TensorIteratorConfig()
@@ -406,7 +406,7 @@ std::tuple<Tensor, Tensor, Tensor> batch_norm_backward_cpu_template(
       for (const auto f : c10::irange(b_begin, b_end)) {
         param_t w = weight.defined() ? weight_a[f] : param_t(1);
 
-        param_t mean, invstd;
+        param_t mean{}, invstd{};
         if (train) {
           mean = save_mean_a[f];
           invstd = save_invstd_a[f];
diff --git a/aten/src/ATen/native/RNN.cpp b/aten/src/ATen/native/RNN.cpp
index 345503d5d759..bf6c1740cc52 100644
--- a/aten/src/ATen/native/RNN.cpp
+++ b/aten/src/ATen/native/RNN.cpp
@@ -209,7 +209,7 @@ struct CellParams : public CellParamsBase {
     TORCH_INTERNAL_ASSERT(false, "Not yet implemented");
   }
   static c10::intrusive_ptr<CellParamsBase> __setstate__(
-      CellParamsSerializationType state) {
+      const CellParamsSerializationType& state) {
     TORCH_INTERNAL_ASSERT(false, "Not yet implemented");
   }
 };
@@ -289,9 +289,9 @@ struct QuantizedCellParams : public CellParamsBase {
                                                zero_point_hh.toLong()};
     return CellParamsSerializationType(
         "quantized",
-        std::move(tensors_to_serialize),
-        std::move(doubles_to_serialize),
-        std::move(longs_to_serialize),
+        tensors_to_serialize,
+        doubles_to_serialize,
+        longs_to_serialize,
         {});
   }
   static c10::intrusive_ptr<CellParamsBase> __setstate__(
@@ -355,10 +355,10 @@ c10::intrusive_ptr<CellParamsBase> make_quantized_cell_params(
       /*packed_hh=*/std::move(packed_hh),
       /*col_offsets_ih=*/std::move(col_offsets_ih),
       /*col_offsets_hh=*/std::move(col_offsets_hh),
-      /*scale_ih=*/std::move(scale_ih),
-      /*scale_hh=*/std::move(scale_hh),
-      /*zero_point_ih=*/std::move(zero_point_ih),
-      /*zero_point_hh=*/std::move(zero_point_hh));
+      /*scale_ih=*/scale_ih,
+      /*scale_hh=*/scale_hh,
+      /*zero_point_ih=*/zero_point_ih,
+      /*zero_point_hh=*/zero_point_hh);
 }
 
 // QuantizedCellParams vs. QuantizedCellParamsDynamic
@@ -431,10 +431,10 @@ struct QuantizedCellParamsDynamic : public CellParamsBase {
     // reduce_range parameter is serialized along with the int field values.
     return CellParamsSerializationType(
         "quantized_dynamic",
-        std::move(tensors_to_serialize),
+        tensors_to_serialize,
         {},
         {reduce_range_},
-        std::move(packed_params_to_serialize));
+        packed_params_to_serialize);
   }
   static c10::intrusive_ptr<CellParamsBase> __setstate__(
       CellParamsSerializationType state) {
@@ -507,7 +507,7 @@ struct QuantizedCellParamsFP16 : public CellParamsBase {
         packed_params_to_serialize{packed_ih, packed_hh};
 
     return CellParamsSerializationType(
-        "quantized_fp16", {}, {}, {}, std::move(packed_params_to_serialize));
+        "quantized_fp16", {}, {}, {}, packed_params_to_serialize);
   }
   static c10::intrusive_ptr<CellParamsBase> __setstate__(
       CellParamsSerializationType state) {
@@ -667,13 +667,13 @@ tpair_of<Tensor> hidden_slice(const tpair_of<Tensor>& t, int64_t start, int64_t
 // It's a struct only because functional programming in C++ is a pain, and it's easier
 // to pass around "vtable pointers" than actual function pointers.
 
-void check_rnn_cell_forward_input(const Tensor& input, c10::SymInt input_size) {
+void check_rnn_cell_forward_input(const Tensor& input, const c10::SymInt& input_size) {
   TORCH_CHECK(
     input.sym_size(1) == input_size,
     "input has inconsistent input_size: got ", input.sym_size(1), " expected ", input_size);
 }
 
-void check_rnn_cell_forward_hidden(const Tensor& input, const Tensor& hx, c10::SymInt hidden_size, c10::SymInt hidden_label) {
+void check_rnn_cell_forward_hidden(const Tensor& input, const Tensor& hx, const c10::SymInt& hidden_size, const c10::SymInt& hidden_label) {
   TORCH_CHECK(
     input.sym_size(0) == hx.sym_size(0),
     "Input batch size ", input.sym_size(0), " doesn't match hidden", hidden_label, " batch size ", hx.sym_size(0));
diff --git a/aten/src/ATen/native/ReduceOps.cpp b/aten/src/ATen/native/ReduceOps.cpp
index e6270e43aa8c..b4dbfbb2d1f9 100644
--- a/aten/src/ATen/native/ReduceOps.cpp
+++ b/aten/src/ATen/native/ReduceOps.cpp
@@ -250,7 +250,7 @@ static void meta_func_cum_ops(
   maybe_wrap_dim(dim, self.dim());
 
   const auto& result = meta.maybe_get_output();
-  ScalarType out_dtype;
+  ScalarType out_dtype{};
 
   if (result.defined()) {
     out_dtype = dtype.value_or(result.scalar_type());
@@ -1639,7 +1639,7 @@ Tensor allany_dims_default(const Tensor &self, OptionalIntArrayRef dim, bool kee
     return out;
   }
 
-  if (dim->size() == 0) {
+  if (dim->empty()) {
     if (self.scalar_type() == kByte) {
       // Convert to a 1 or 0 mask
       auto out = at::empty_like(self);
diff --git a/aten/src/ATen/native/ReduceOpsUtils.h b/aten/src/ATen/native/ReduceOpsUtils.h
index 4f0e9f9f8632..928853ed44ca 100644
--- a/aten/src/ATen/native/ReduceOpsUtils.h
+++ b/aten/src/ATen/native/ReduceOpsUtils.h
@@ -370,7 +370,7 @@ inline void resize_reduction(
   auto shape = get_reduction_shape(self, dims_, keepdim, allow_empty_dims);
   if (self.layout() == kStrided) {
     meta.set_output_raw_strided(0, shape, {}, self.options().dtype(out_dtype));
-  } else if (shape.size() == 0) {
+  } else if (shape.empty()) {
     meta.set_output_raw_strided(0, shape, {}, self.options().dtype(out_dtype).layout(kStrided));
   } else {
     TORCH_CHECK(false, "resize_reduction: support for output with ", self.layout(), " layout is not implemented yet");
diff --git a/aten/src/ATen/native/Repeat.cpp b/aten/src/ATen/native/Repeat.cpp
index 8bd253134b7a..2a62a74103c4 100644
--- a/aten/src/ATen/native/Repeat.cpp
+++ b/aten/src/ATen/native/Repeat.cpp
@@ -87,7 +87,7 @@ Tensor repeat_interleave_symint(
   }
 
   auto ret = input.index_select(
-      dim.value(), at::repeat_interleave_symint(repeats_, output_size));
+      dim.value(), at::repeat_interleave_symint(repeats_, std::move(output_size)));
   // Restore conj and neg bits
   if (conj) {
     ret = ret.conj();
diff --git a/aten/src/ATen/native/Repeat.h b/aten/src/ATen/native/Repeat.h
index 879a09bddd99..b8d6f92553a4 100644
--- a/aten/src/ATen/native/Repeat.h
+++ b/aten/src/ATen/native/Repeat.h
@@ -28,7 +28,7 @@ static inline Tensor repeat_interleave_common(
   }
   Tensor repeats_ = repeats.contiguous();
   Tensor cumsum = repeats.cumsum(0);
-  int64_t total;
+  int64_t total = 0;
   if (output_size.has_value()) {
     total = output_size.value();
   } else {
diff --git a/aten/src/ATen/native/Resize.cpp b/aten/src/ATen/native/Resize.cpp
index 01d11e652928..f4d895c2c585 100644
--- a/aten/src/ATen/native/Resize.cpp
+++ b/aten/src/ATen/native/Resize.cpp
@@ -274,7 +274,7 @@ const Tensor& resize__symint(
   return _resize_(self, size, optional_memory_format);
 }
 
-void resize_bytes_nocuda(const Storage& storage, c10::SymInt newsize) {
+void resize_bytes_nocuda(const Storage& storage, const c10::SymInt& newsize) {
   // handles all devices except cuda (which needs to be in a different .so)
   c10::DeviceType device_type = storage.device_type();
   if (device_type == at::kCPU) {
diff --git a/aten/src/ATen/native/Resize.h b/aten/src/ATen/native/Resize.h
index fb6a7ecb6634..951a08e84c77 100644
--- a/aten/src/ATen/native/Resize.h
+++ b/aten/src/ATen/native/Resize.h
@@ -38,7 +38,7 @@ TORCH_API bool resize_output_check_symint(const Tensor& output, SymIntArrayRef s
 
 TORCH_API void resize_bytes_cpu(StorageImpl* storage, size_t size_bytes);
 TORCH_API void resize_bytes_meta(StorageImpl* storage, c10::SymInt size_bytes);
-TORCH_API void resize_bytes_nocuda(const Storage& storage, c10::SymInt size_bytes);
+TORCH_API void resize_bytes_nocuda(const Storage& storage, const c10::SymInt& size_bytes);
 
 inline void maybe_resize_storage_cpu(TensorImpl* self, size_t new_size_bytes) {
   // It does not make sense to try to resize a storage
diff --git a/aten/src/ATen/native/cpu/AmpGradScalerKernels.cpp b/aten/src/ATen/native/cpu/AmpGradScalerKernels.cpp
index 005b9c15060c..f4d2cb35c986 100644
--- a/aten/src/ATen/native/cpu/AmpGradScalerKernels.cpp
+++ b/aten/src/ATen/native/cpu/AmpGradScalerKernels.cpp
@@ -1,7 +1,7 @@
 #define TORCH_ASSERT_ONLY_METHOD_OPERATORS
 
 #include <ATen/native/AmpKernels.h>
-#include <math.h>
+#include <cmath>
 #include <ATen/DeviceGuard.h>
 #include <ATen/Dispatch.h>
 #include <ATen/OpMathType.h>
@@ -32,7 +32,7 @@ void _amp_foreach_non_finite_check_and_unscale_cpu_kernel(
     TensorList scaled_grads,
     at::Tensor& found_inf,
     const at::Tensor& inv_scale) {
-  if (scaled_grads.size() == 0) {
+  if (scaled_grads.empty()) {
     return;
   }
 
@@ -55,7 +55,7 @@ void _amp_foreach_non_finite_check_and_unscale_cpu_kernel(
         t.layout() == at::kStrided,
         "one of scaled_grads was not a strided tensor.");
     auto iter = at::TensorIterator::unary_op(
-        const_cast<at::Tensor&>(t), const_cast<at::Tensor&>(t));
+        const_cast<at::Tensor&>(t), t);
     if (at::isReducedFloatingType(iter.dtype())) {
       AT_DISPATCH_REDUCED_FLOATING_TYPES(
       iter.dtype(),
diff --git a/aten/src/ATen/native/cpu/AvgPoolKernel.cpp b/aten/src/ATen/native/cpu/AvgPoolKernel.cpp
index ab7586d21d40..5a8597d413a2 100644
--- a/aten/src/ATen/native/cpu/AvgPoolKernel.cpp
+++ b/aten/src/ATen/native/cpu/AvgPoolKernel.cpp
@@ -71,7 +71,7 @@ void cpu_avg_pool2d(
 
       acc_t sum = 0;
 
-      int64_t divide_factor;
+      int64_t divide_factor = 0;
       if (divisor_override.has_value()) {
         divide_factor = divisor_override.value();
       } else {
@@ -100,7 +100,7 @@ void cpu_avg_pool2d(
 }
 
 template <typename scalar_t,
-          typename std::enable_if<!is_reduced_floating_point<scalar_t>::value, int>::type = 0>
+          std::enable_if_t<!is_reduced_floating_point<scalar_t>::value, int> = 0>
 void cpu_avg_pool2d_channels_last(
     const Tensor& output_,
     const Tensor& input_,
@@ -147,7 +147,7 @@ void cpu_avg_pool2d_channels_last(
       ih1 = std::min(ih1, input_height);
       iw1 = std::min(iw1, input_width);
 
-      int64_t divide_factor;
+      int64_t divide_factor = 0;
       if (divisor_override.has_value()) {
         divide_factor = divisor_override.value();
       } else {
@@ -214,7 +214,7 @@ void cpu_avg_pool2d_channels_last(
 }
 
 template <typename scalar_t,
-          typename std::enable_if<is_reduced_floating_point<scalar_t>::value, int>::type = 0>
+          std::enable_if_t<is_reduced_floating_point<scalar_t>::value, int> = 0>
 void cpu_avg_pool2d_channels_last(
     const Tensor& output_,
     const Tensor& input_,
@@ -266,7 +266,7 @@ void cpu_avg_pool2d_channels_last(
       ih1 = std::min(ih1, input_height);
       iw1 = std::min(iw1, input_width);
 
-      int64_t divide_factor;
+      int64_t divide_factor = 0;
       if (divisor_override.has_value()) {
         divide_factor = divisor_override.value();
       } else {
@@ -386,7 +386,7 @@ void cpu_avg_pool2d_backward(
           ih1 = std::min(ih1, input_height);
           iw1 = std::min(iw1, input_width);
 
-          int64_t divide_factor;
+          int64_t divide_factor = 0;
           if (divisor_override.has_value()) {
             divide_factor = divisor_override.value();
           } else {
@@ -455,7 +455,7 @@ void cpu_avg_pool2d_backward_channels_last(
           ih1 = std::min(ih1, input_height);
           iw1 = std::min(iw1, input_width);
 
-          int64_t divide_factor;
+          int64_t divide_factor = 0;
           if (divisor_override.has_value()) {
             divide_factor = divisor_override.value();
           } else {
@@ -611,7 +611,7 @@ void cpu_avg_pool3d(
 
       acc_t sum = 0;
 
-      int64_t divide_factor;
+      int64_t divide_factor = 0;
       if (divisor_override.has_value()) {
         divide_factor = divisor_override.value();
       } else {
@@ -642,7 +642,7 @@ void cpu_avg_pool3d(
 }
 
 template <typename scalar_t,
-          typename std::enable_if<!is_reduced_floating_point<scalar_t>::value, int>::type = 0>
+          std::enable_if_t<!is_reduced_floating_point<scalar_t>::value, int> = 0>
 void cpu_avg_pool3d_channels_last(
     const Tensor& output_,
     const Tensor& input_,
@@ -696,7 +696,7 @@ void cpu_avg_pool3d_channels_last(
       ih1 = std::min(ih1, input_height);
       iw1 = std::min(iw1, input_width);
 
-      int64_t divide_factor;
+      int64_t divide_factor = 0;
       if (divisor_override.has_value()) {
         divide_factor = divisor_override.value();
       } else {
@@ -765,7 +765,7 @@ void cpu_avg_pool3d_channels_last(
 }
 
 template <typename scalar_t,
-          typename std::enable_if<is_reduced_floating_point<scalar_t>::value, int>::type = 0>
+          std::enable_if_t<is_reduced_floating_point<scalar_t>::value, int> = 0>
 void cpu_avg_pool3d_channels_last(
     const Tensor& output_,
     const Tensor& input_,
@@ -824,7 +824,7 @@ void cpu_avg_pool3d_channels_last(
       ih1 = std::min(ih1, input_height);
       iw1 = std::min(iw1, input_width);
 
-      int64_t divide_factor;
+      int64_t divide_factor = 0;
       if (divisor_override.has_value()) {
         divide_factor = divisor_override.value();
       } else {
@@ -952,7 +952,7 @@ void cpu_avg_pool3d_backward(
             ih1 = std::min(ih1, input_height);
             iw1 = std::min(iw1, input_width);
 
-            int64_t divide_factor;
+            int64_t divide_factor = 0;
             if (divisor_override.has_value()) {
               divide_factor = divisor_override.value();
             } else {
@@ -1031,7 +1031,7 @@ void cpu_avg_pool3d_backward_channels_last(
             ih1 = std::min(ih1, input_height);
             iw1 = std::min(iw1, input_width);
 
-            int64_t divide_factor;
+            int64_t divide_factor = 0;
             if (divisor_override.has_value()) {
               divide_factor = divisor_override.value();
             } else {