[clang-tidy] NO.15 enable cppcoreguidelines-pro-type-const-cast Part.1 (

PaddlePaddle#58285) * fix test * fix test * revert context_pool * fix * CI
SecretXV · Nov 28, 2023 · d25848a · d25848a
1 parent 4f9ce8d
commit d25848a
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Showing 14 changed files with 90 additions and 75 deletions.
diff --git a/.clang-tidy b/.clang-tidy
@@ -158,7 +158,7 @@ cppcoreguidelines-explicit-virtual-functions,
 cppcoreguidelines-init-variables,
 cppcoreguidelines-narrowing-conversions,
 cppcoreguidelines-no-malloc,
--cppcoreguidelines-pro-type-const-cast,
+cppcoreguidelines-pro-type-const-cast,
 -cppcoreguidelines-pro-type-member-init,
 -cppcoreguidelines-slicing,
 -hicpp-avoid-goto,

diff --git a/paddle/phi/api/lib/context_pool.cc b/paddle/phi/api/lib/context_pool.cc
@@ -61,7 +61,7 @@ const phi::DeviceContext* DeviceContextPool::Get(const Place& place) {
 }
 
 phi::DeviceContext* DeviceContextPool::GetMutable(const Place& place) {
-  return const_cast<phi::DeviceContext*>(Get(place));
+  return const_cast<phi::DeviceContext*>(Get(place));  // NOLINT
 }
 
 }  // namespace experimental
@@ -72,7 +72,7 @@ namespace paddle {
 PADDLE_API phi::Allocator* GetAllocator(const phi::Place& place) {
   const phi::DeviceContext* dev_ctx =
       paddle::experimental::DeviceContextPool::Instance().Get(place);
-  return const_cast<phi::Allocator*>(&dev_ctx->GetAllocator());
+  return const_cast<phi::Allocator*>(&dev_ctx->GetAllocator());  // NOLINT
 }
 
 #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)

diff --git a/paddle/phi/backends/device_manager.cc b/paddle/phi/backends/device_manager.cc
@@ -183,7 +183,7 @@ void Device::BlasAXPBY(const stream::Stream& stream,
                    phi::CppTypeToDataType<T>::Type(),
                    numel,
                    alpha,
-                   reinterpret_cast<void*>(const_cast<T*>(x)),
+                   reinterpret_cast<void*>(const_cast<T*>(x)),  // NOLINT
                    beta,
                    reinterpret_cast<void*>(y));
 }

diff --git a/paddle/phi/backends/gpu/gpu_context.cc b/paddle/phi/backends/gpu/gpu_context.cc
@@ -919,17 +919,17 @@ ncclComm_t GPUContext::nccl_comm() const { return impl_->GetNcclComm(); }
 void GPUContext::set_nccl_comm(ncclComm_t comm) { impl_->SetNcclComm(comm); }
 
 void GPUContext::Init() {
-  impl_->allocator_ = const_cast<Allocator*>(&this->GetAllocator());
+  impl_->allocator_ = const_cast<Allocator*>(&this->GetAllocator());  // NOLINT
   impl_->Init();
 }
 
 void GPUContext::SetStream(gpuStream_t stream) {
-  impl_->allocator_ = const_cast<Allocator*>(&this->GetAllocator());
+  impl_->allocator_ = const_cast<Allocator*>(&this->GetAllocator());  // NOLINT
   impl_->SetStream(stream);
 }
 
 void GPUContext::SetCUDAStream(CUDAStream* stream, bool clear) {
-  impl_->allocator_ = const_cast<Allocator*>(&this->GetAllocator());
+  impl_->allocator_ = const_cast<Allocator*>(&this->GetAllocator());  // NOLINT
   impl_->SetCUDAStream(stream, clear);
 }
 
@@ -1006,7 +1006,7 @@ void GPUContext::PartialInitWithoutAllocator(int stream_priority) {
 }
 
 void GPUContext::PartialInitWithAllocator() {
-  impl_->allocator_ = const_cast<Allocator*>(&this->GetAllocator());
+  impl_->allocator_ = const_cast<Allocator*>(&this->GetAllocator());  // NOLINT
   impl_->PartialInitWithAllocator();
 }
 

diff --git a/paddle/phi/core/device_context.cc b/paddle/phi/core/device_context.cc
@@ -176,8 +176,10 @@ struct DeviceContext::Impl {
       allocator = cuda_graph_allocator_;
     }
 #endif
-    return tensor->AllocateFrom(
-        const_cast<Allocator*>(allocator), dtype, requested_size, fake_alloc);
+    return tensor->AllocateFrom(const_cast<Allocator*>(allocator),
+                                dtype,
+                                requested_size,
+                                fake_alloc);  // NOLINT
   }
 
   template <typename T>
@@ -218,8 +220,10 @@ struct DeviceContext::Impl {
         (fake_alloc || tensor->numel() == 0) && requested_size == 0
             ? host_zero_allocator_
             : host_allocator_;
-    return tensor->AllocateFrom(
-        const_cast<Allocator*>(allocator), dtype, requested_size, fake_alloc);
+    return tensor->AllocateFrom(const_cast<Allocator*>(allocator),
+                                dtype,
+                                requested_size,
+                                fake_alloc);  // NOLINT
   }
 
   template <typename T>

diff --git a/paddle/phi/kernels/cpu/cumprod_grad_kernel.cc b/paddle/phi/kernels/cpu/cumprod_grad_kernel.cc
@@ -56,10 +56,10 @@ void CumprodGradKernel(const Context& dev_ctx,
   Allocator::AllocationPtr x_conj;
   Allocator::AllocationPtr out_conj;
   if (phi::IsComplexType(x.dtype())) {
-    x_conj = const_cast<Allocator&>(dev_ctx.GetAllocator())
+    x_conj = const_cast<Allocator&>(dev_ctx.GetAllocator())  // NOLINT
                  .Allocate(numel * sizeof(T));
     auto* x_data_conj = reinterpret_cast<T*>(x_conj->ptr());
-    out_conj = const_cast<Allocator&>(dev_ctx.GetAllocator())
+    out_conj = const_cast<Allocator&>(dev_ctx.GetAllocator())  // NOLINT
                    .Allocate(numel * sizeof(T));
     auto* out_data_conj = reinterpret_cast<T*>(out_conj->ptr());
 

diff --git a/paddle/phi/kernels/cpu/instance_norm_grad_kernel.cc b/paddle/phi/kernels/cpu/instance_norm_grad_kernel.cc
@@ -93,9 +93,9 @@ void InstanceNormGradKernel(const Context& dev_ctx,
   }
 
   auto scale_e =
-      scale_ptr
-          ? EigenVector<T>::Flatten(*scale_ptr)
-          : EigenVector<T>::Flatten(const_cast<const DenseTensor&>(scale_data));
+      scale_ptr ? EigenVector<T>::Flatten(*scale_ptr)
+                : EigenVector<T>::Flatten(
+                      const_cast<const DenseTensor&>(scale_data));  // NOLINT
   auto mean_e = EigenVector<T>::Flatten(saved_mean);
   auto inv_var_e = EigenVector<T>::Flatten(saved_variance);
   auto dy_e = EigenVector<T>::Flatten(d_y);

diff --git a/paddle/phi/kernels/cpu/instance_norm_kernel.cc b/paddle/phi/kernels/cpu/instance_norm_kernel.cc
@@ -111,14 +111,14 @@ void InstanceNormKernel(const Context& dev_ctx,
     set_constant(dev_ctx, &bias_data, static_cast<T>(0));
   }
   auto scale_e =
-      scale_ptr
-          ? EigenVector<T>::Flatten(*scale_ptr)
-          : EigenVector<T>::Flatten(const_cast<const DenseTensor&>(scale_data));
+      scale_ptr ? EigenVector<T>::Flatten(*scale_ptr)
+                : EigenVector<T>::Flatten(
+                      const_cast<const DenseTensor&>(scale_data));  // NOLINT
   auto scale_arr = scale_e.reshape(C_shape);
-  auto bias_e =
-      bias_ptr
-          ? EigenVector<T>::Flatten(*bias_ptr)
-          : EigenVector<T>::Flatten(const_cast<const DenseTensor&>(bias_data));
+  auto bias_e = bias_ptr
+                    ? EigenVector<T>::Flatten(*bias_ptr)
+                    : EigenVector<T>::Flatten(
+                          const_cast<const DenseTensor&>(bias_data));  // NOLINT
   auto bias_arr = bias_e.reshape(C_shape);
 
   dev_ctx.template Alloc<T>(y);

diff --git a/paddle/phi/kernels/cpu/matrix_rank_tol_kernel.cc b/paddle/phi/kernels/cpu/matrix_rank_tol_kernel.cc
@@ -34,7 +34,7 @@ void LapackSVD(const T* x_data, T* eigenvalues_data, int rows, int cols) {
   char jobz = 'N';
   int mx = std::max(rows, cols);
   int mn = std::min(rows, cols);
-  T* a = const_cast<T*>(x_data);
+  T* a = const_cast<T*>(x_data);  // NOLINT
   int lda = rows;
   int lwork = 3 * mn + std::max(mx, 7 * mn);
   std::vector<T> work(lwork);

diff --git a/paddle/phi/kernels/cpu/svd_kernel.cc b/paddle/phi/kernels/cpu/svd_kernel.cc
@@ -28,7 +28,7 @@ void LapackSvd(
   char jobz = full ? 'A' : 'S';
   int mx = std::max(rows, cols);
   int mn = std::min(rows, cols);
-  T* a = const_cast<T*>(X);
+  T* a = const_cast<T*>(X);  // NOLINT
   int lda = rows;
   int ldu = rows;
   int ldvt = full ? cols : mn;